Browse Topic: Steering systems

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Trajectory Optimization for Non-Cooperative Target Spacecraft Based on Sequential Convex Programming2026-99-2330To be published on 10/30/2026
Autonomous operations near space targets that do not cooperate present great challenges because position and attitude planning must be linked. A target of this type lacks navigation aids or communication systems, which makes relative motion planning much more difficult. Although methods based on convex optimization work well for problems with three degrees of freedom, like planetary landing, their use in integrated operations with six degrees of freedom is limited by constraints that are not convex. To solve this problem, this paper proposes a new framework for servicing spacecraft using sequential convex programming. The primary contribution is the extension of convex methods to high dimensional trajectory planning while adding a robust trust region constraint based on trajectory deviation. This mechanism reduces the chance of finding no solution during convex approximations. It improves reliability and convergence when solving complex problems. The approach first creates a continuous optimal control problem with an index that balances energy use and the angle of the line of sight. Nonlinear dynamics are simplified using first order Taylor expansions. Constraints for the field of view and collision avoidance are modeled using spherical and ellipsoidal regions. The non convex problem is changed into a sequence of discrete convex subproblems and solved through iterations. Numerical simulations show that the servicing spacecraft can safely approach the target with stable attitude while meeting all physical limits. The method using sequential convex programming with a trust region demonstrates great potential in optimizing computational efficiency alongside strong numerical stability, laying a solid foundation for future real-time autonomous trajectory generation in complex orbital environments.
Wang, Yuzhi, Yao, Zihao, Wang, Qian, Han, Ziwen, Zhang, Chao, Zhang, Yumei, Li, Shunli
Research on Trajectory Planning Based on High-Precision Improved C-W Equation2026-99-2329To be published on 10/30/2026
In today’s space environment, the number of tumbling objects such as abandoned satellites, uncontrolled spacecraft, and space debris has increased dramatically. Therefore, in-orbit services such as maintenance, recovery, and capture of rolling targets is an urgent topic that needs to be explored. In this paper, for the mission of spacecraft approaching the tumbling target at close range, the trajectory planning method is studied by using the high-precision relative equation of motion as the orbital dynamic model. Firstly, the error of the relative equation of motion of the center of mass is analyzed, and a method to improve the accuracy of the C-W (Clohessy-Wiltshire) equation is considered to construct a higher-precision orbital dynamics model. The constraints such as attitude dynamics, line-of-sight range, control ability and keep-out zone are mathematically described, and the optimal control problem of pose synchronization is established. On this basis, this paper takes the traditional C-W equation and the improved C-W equation as the relative motion models of the centroids, and, based on the adaptive pseudospectral method and sequential convex optimization, solves the position and attitude synchronization planning problem. This article focuses on analyzing the errors of the C-W equation before and after improvement, as well as the impact of linearization on these errors, studies the accuracy improvement brought by the improved equation and the safe obstacle avoidance of position and attitude trajectories, and compares the advantages and disadvantages of the two methods. Simulations verify the accuracy and efficiency of this trajectory optimization algorithm and confirm the accuracy improvement of the improved C-W equation. The improved C-W equation has significant application potential in trajectory planning tasks in complex orbital environments with high-precision requirements.
Zhang, Chao, Yao, Zihao, Gou, Jingwen, Zhang, Yumei, Wang, Yuzhi, Li, Shunli
Research on Dynamic Force Formation in Aerospace Search and Rescue Missions Based on an Improved Contract Net Algorithm2026-99-2310To be published on 10/30/2026
hierarchical biddingjoint evaluation
Lv, Nan, Liu, Ruirui, Li, Donghao, Liu, Xin
NGO-Based Orbits Optimization for Impulsive Transfers between DRO and Halo Orbits2026-99-293910/19/2026
This research investigates the design of fuel-saving orbital transfer trajectories for spacecraft departing from distant retrograde orbits (DRO) toward Halo orbits around the Earth-Moon L2 libration point. Relevant trajectory designs hold practical value for future lunar space station projects and deep space exploration undertakings. Previous research commonly adopted indirect modeling methods or classic evolutionary algorithms to acquire transfer solutions, yet these techniques show limited performance in multi-body dynamic systems, particularly unsatisfactory convergence efficiency. To address this defect, this paper proposes an improved direct optimization strategy based on the Northern Goshawk Optimization (NGO) algorithm. All numerical analyses are conducted within the circular restricted three-body problem (CR3BP) framework, which accurately reflects gravitational features in cislunar space while avoiding overly complicated calculation processes. The departing and target orbits are discretized mathematically, and the whole transfer mission is defined as a constrained nonlinear optimization problem aiming to minimize total velocity variation. The adopted algorithm balances global exploration and local exploitation rather than prioritizing a single search mode. This balanced mechanism effectively lowers the risk of falling into local optimal solutions, a prevalent flaw of conventional optimization methods. Precise dynamic modeling is also implemented: canonical transformation is applied to reorganize dynamic governing equations for simpler computation, and differential correction technology is utilized to optimize preliminary trajectory results generated by NGO iteration. Multiple tests with diverse initial values and mission constraints are carried out to verify the method’s stability. Simulation results prove that the proposed strategy maintains steady performance under external interference and stably yields qualified feasible trajectories. Overall, the Northern Goshawk Optimization algorithm proves applicable for high-precision orbital transfer optimization. This study also provides valuable references for low-energy trajectory planning in cislunar space.
Huang, Jiaxin, Zhang, Zexu, Liang, Jintang, Wang, Yiyu
Trajectory Planning for Heterogeneous Multi-Satellite Cooperative Proximity Operations2026-99-291410/19/2026
This study addresses the challenges in heterogeneous multi-satellite cooperative proximity operations, including functional heterogeneity, strong temporal coupling, and difficulties in unified planning. A functionally layered framework composed of observation, support, and servicing satellites is constructed. Considering the distinct mission characteristics of different satellite types, differentiated decision variables, objective functions, and constraints are designed, and a particle swarm optimization algorithm is employed for solution. Simulation results demonstrate that the proposed method can generate feasible cooperative trajectories satisfying constraints such as safety distance, temporal coordination, communication links, and mission windows, while maintaining system stability and operational safety. Parametric analysis indicates that mission duration, minimum access distance, and support effectiveness significantly affect system performance. Through functional decomposition and cooperative optimization, the system achieves an effective trade-off among different preferences, providing theoretical support for the mission design and control of heterogeneous multi-satellite proximity operations.
Xiao, Jinzhao, Zhang, Jin
A CTCM-ALA Adaptive Embedded Fusion Optimization Method for UAV Path Planning in Complex Medical Rescue Environments2026-99-290510/19/2026
To improve the accuracy and safety margin deficiencies of unmanned aerial vehicles in autonomous medical emergency search-and-rescue missions, this paper develops an adaptive embedded fusion optimizer named CTCM-ALA for rescue UAV path planning. The proposed algorithm hybridizes the tribe-competition member-cooperation strategy and artificial lemming algorithm to construct hierarchically adaptive initial populations, speed up convergence and prevent local convergence traps. Tests on 12 CEC2017 functions prove its better optimization performance. Real-world applications in dense urban and complicated topographic medical rescue cases verify its reliability and precision.
Chen, Yuhan, Xiong, Junhui, Li, Yuesheng, Zhou, Yifan, Yao, Junjie, Li, Rujun
Double-Envelope Coordinated Control with Subjective-Objective Consistent Stability Evaluation for Corner-Module Vehicles10-11-01-000610/9/2026
Physical constraints are characterized by vehicle stability boundaries and actuator hardware limits. However, they cannot capture driver perception of vehicle responses or quantify the effects of actuator inputs on vehicle stability. This article proposes a double-envelope coordinated control strategy with subjective–objective consistent stability evaluation for corner-module vehicles. Actuator stability margins are derived from vehicle stability boundaries under four-wheel steering and distributed drive. They are used to prevent vehicle instability in advance by constraining destabilizing actuator inputs. Real-vehicle tests identify driver-perceived boundaries under varying vehicle speeds and road adhesion conditions. Mapping these boundaries to the objective stability index establishes a subjective–objective consistent evaluation. Actuator stability margins and consistent evaluation form a soft envelope, while vehicle stability boundaries and actuator hardware limits define a hard envelope. The resulting double-envelope coordinated control adjusts actuator priorities according to the actuator stability margins and adapts control targets to reflect driver perception. CarSim-Simulink co-simulation and hardware-in-the-loop experiments demonstrate improvements in extreme obstacle avoidance capability and driver handling confidence.
Shi, Wenbo, Ding, Haitao, Xu, Nan, Zhang, Xinjie
Stuck-Assist: A Large Multimodal Model-Based Stuck Recovery Assist for Driving on Off-Road Deformable Terrains2026-01-75139/22/2026
Recent advancements in off-road autonomy have shown significant progress in perception, planning, and control frameworks, including end-to-end learning approaches. Comprehensive results have been demonstrated in both simulation and real-world experiments; however, there are significant challenges in critical cases that need further evaluation. One such challenge is the immobilization of autonomous ground vehicles (AGVs) in unstructured off-road environments, which can significantly impact agriculture, space exploration, military operations, and search and rescue missions. Addressing this problem requires recovery strategies that are context-sensitive, adaptable to terrain and vehicle conditions, and effective in integrating multimodal inputs. To this end, this paper investigates the use of a large multimodal model (LMM) providing higher-level planning assistance with human-in-the-loop evaluations for vehicle recovery after immobilization in unstructured off-road terrain. The experimental simulation platform developed was based on the Algoryx (AGX) Dynamics engine for high-fidelity terramechanics interaction and vehicle physics combined with Unreal Engine 5. This platform was further integrated with a driving simulator equipped with steering wheel and pedal interfaces for human-in-the-loop experiments. We evaluated ten representative unstuck scenarios across two deformable terrains (loose sand and compact sand) under two modes: an unskilled baseline, where participants attempted recovery unaided, and a co-intelligence mode, where participants used LMM advisory instructions. The results show that LMM assistance improved stuck recovery rates by 70% compared to unaided and unskilled human driving.
Bhosale, Mayuresh, Whitson, Jordan A., Vahidi, Ardalan, Jia, Yunyi
Terrain-Aware Probabilistic Prediction of Suspension Component Forces in Unmanned Ground Vehicles2026-01-75099/22/2026
Unmanned ground vehicles (UGVs) operating in unstructured environments must account not only for terrain traversability but also for terrain-induced loads that affect component durability. Existing path planning approaches primarily consider obstacle avoidance and mobility, while neglecting cumulative structural degradation due to repeated loading. High-fidelity physics-based simulations can capture these effects but are computationally prohibitive for real-time applications. This study investigates machine learning-based surrogate models for predicting vehicle component reaction forces from terrain height sequences generated using a controlled parametric terrain formulation. Both feed-forward and recurrent neural network architectures are evaluated, and ensemble-based probabilistic techniques are incorporated to quantify predictive uncertainty. Results show that the ensemble long short-term memory (Ens-LSTM) model achieves the lowest prediction error (mean absolute error of 0.621 kN) while maintaining narrow 95% prediction intervals (3.22–4.28 kN). A simpler ensemble feed-forward network (Ens-NN) achieves comparable accuracy (0.626 kN) with reduced model complexity. These results demonstrate that data-driven surrogate models can provide accurate and uncertainty-aware force predictions, enabling the integration of structural reliability considerations into fatigue-aware path planning for UGVs.
Chua, Yang Kang, Mundiwala, Mohammad, Wang, Xudong, Castanier, Matthew, Hu, Zhen, Hu, Chao
Simulation-Based Development and Evaluation of Heat-Quantity-Based Thermal Operating Strategies for Automotive Applications2026-24-00129/21/2026
Thermal management of hybrid electric vehicle (HEV) powertrains requires the simultaneous conditioning of multiple components operating at fundamentally different temperature levels. For thermal management systems, which directly couple the thermal circuits of the internal combustion engine (ICE), electric motor and inverter (EMINV), and traction battery (BAT) for example via controllable three-way valves and a ring-circuit, the decision of when and which components to couple has a direct impact on overall powertrain efficiency. Existing thermal operating strategies rely on empirically defined temperature thresholds and fixed component priority rankings, without quantifying the actual efficiency benefit associated with each coupling decision. This paper presents the development and simulation-based evaluation of a heat-quantity-based thermal operating strategy for a prototype HEV at TU Darmstadt. The strategy introduces three new computational modules — a Q-Indicator quantifying the thermal surplus or deficit of each component, an η-Indicator evaluating real-time component efficiencies as a function of temperature and operating point, and a Δη module computing the combined efficiency gain of each potential coupling pair prior to actuation. Coupling is executed only when the combined efficiency delta is positive, replacing empirical prioritization with a quantitative, efficiency-driven decision mechanism. The strategy is evaluated against an uncoupled baseline (REF-0) and a temperature-threshold-based predecessor strategy (REF-1) across a representative commuter cycle at ambient temperatures of −10 °C, 0 °C, and +30 °C using a co-simulation environment comprising a 1D ring-circuit fluid model in AVL Cruise M and a backward-facing 0D drivetrain model in MATLAB/Simulink. The results demonstrate measurable improvements in battery preconditioning and system efficiency at cold and moderate ambient temperatures. The heat-quantity-based strategy achieves comparable or superior thermal outcomes to the threshold-based approach while activating ring-circuit coupling more selectively. At warm ambient conditions, the strategy correctly withholds intervention based on a negative efficiency delta evaluation, confirming robust scenario-adaptive behavior. The findings highlight the potential of efficiency-driven coupling logic as a generalized and physically grounded basis for thermal operating strategy development in electrified powertrains.
Stenger, Erik, Fiore, Luis, Weimer, Niko, Beidl, Christian
Research on the Stability of Three-Axis Vehicles with Coordinated Steering under High-Speed Low-Adhesion Conditions2026-99-11369/11/2026
Three-axle vehicles are widely used in engineering, transportation, and other heavy-duty applications, but they are prone to lateral instability at high speeds or on low-adhesion road conditions, which severely degrades handling stability. To enhance their dynamic performance under extreme operating conditions, this paper proposes a direct yaw-moment control (DYC) strategy based on an incremental linear quadratic regulator (ILQR) for a distributed-drive three-axle vehicle equipped with active front-wheel steering (AFS) and differential drive assist steering (DDAS), thereby improving the accuracy and responsiveness of lateral stability control. Furthermore, to mitigate the mutual coupling and interference among multiple control subsystems, a coordinated steering strategy based on phase-plane analysis is proposed to achieve effective integration and dynamic coordination of AFS, DDAS, and DYC. Co-simulation studies conducted in Matlab/Simulink and TruckSim reveal that the proposed coordinated steering strategy substantially diminishes the peak yaw rate and vehicle sideslip angle across diverse driving conditions, thereby considerably enhancing the lateral stability of the three-axle vehicle during extreme maneuvers.
Hu, Jiadong, Wang, Tie
Trajectory Tracking Control for UUVs Based on Deep Reinforcement Learning2026-99-11329/11/2026
Unmanned Underwater Vehicles (UUVs) operate in complex and uncertain environments, which require a suitable controller. While traditional PID controllers are widely used, they often have slow response speed and inadequate disturbance rejection, particularly under complex and uncertain conditions. To overcome these shortcomings, this paper introduces the DDPG-DLPID, an adaptive motion controller, including a Deep Deterministic Policy Gradient (DDPG) reinforcement learning that can acquire the parameters of PID controllers. In this paper, we design two loops: the inner loop handles velocity regulation, and the outer loop handles position and attitude. By using DDPG, the system can efficiently adjust the PID parameters of both loops in real time, allowing it to effectively adapt to environmental changes and achieve optimized requirements. To evaluate the controller, we design the following scenarios, including straight-line and complex path-following tasks. Compared with single-loop PID and dual-loop PID controllers, the proposed DDPGDLPID approach achieves faster response and higher tracking accuracy, while substantially reducing tracking errors under interference conditions. Physical experiments under three conditions-straight-line voyage, attitude maintaining, and depth control-were further carried out to validate the strategy’s real-world applicability. Experimental data confirm that DDPG-DLPID has better performance when compared with both traditional PID and dual-loop PID controllers across all test scenarios.
Wang, Ling, Shi, Yan
An Improved Multi-Object Vehicle Detection Algorithm Based on YOLOv11s2026-99-11299/11/2026
For object detection in complex road situations, such as inadequate detection performance and difficulties caused by vehicle occlusion and cluttered environments, this paper pursues a YOLOv11s-based object detection framework. The algorithm successfully designed a novel PEConv module. This module integrates a partial convolutional network with an efficient multi-head attention mechanism. Through a Split operation, the input image is divided into locally enhanced channels and original channels. The locally enhanced channels undergo partial convolution and feature weight allocation via the efficient multi- head attention mechanism for feature extraction. Finally, these channels are fused with the original channels before undergoing convolution. This approach preserves the original features while minimising feature loss caused by the series of operations. Therefore, the PEConv module is based on a partially convolutional network and efficient multi-head attention. It improves the detection ability by precisely giving more weight to small objects and occluded parts with augmented partial channel attention and original channel fusion. This study further enhances the model’s detection precision and improves its performance in addressing small target vehicles and severe occlusion issues by refining and upgrading the original C3K2 architecture. The LSBlock is integrated into the original model’s bottleneck structure, replacing the traditional 3x3 convolution to create the C3K2 - LSBlock module. Experimental results show that on the UA - DETRAC dataset, compared with the original YOLOv11s, the optimized YOLOv11s has improved the original mAP @ 50 by 3.4%, reaching 61.3%, and improved the original mAP @ 50: 95 by 2%, which verifies the correctness of it.
Chen, Yulin, Wang, Yini, Wang, Jianwei, Zhang, Xin
Study on Longitudinal Vehicle Control Strategy Considering the Speed Reference Trajectory2026-99-11359/11/2026
To ensure the dynamic characteristics in the vehicle’s longitudinal control process, a longitudinal control strategy considering the speed reference trajectory is designed. Based on a hierarchical control method, the speed input in the upper-level control algorithm is designed using a reference trajectory, and the model predictive control (MPC) algorithm is applied to solve for the vehicle’s desired acceleration. In the lower-level control, a feedforward and feedback control structure is used to track the target acceleration, while an inverse longitudinal model is established to calculate the vehicle actuator outputs. Finally, simulation verification is carried out for host vehicle speed change and cut-in, cut-out situations ahead of the vehicle. The results indicate that the method achieves a smoother acceleration response, ensuring driving comfort.
Song, Jia, Li, Wenjie, Ma, Wenyu
Safety-Aware Path Planning for Unmanned Surface Vehicle: An Adaptive Hybrid Collision Avoidance Operation2026-99-15249/11/2026
An adaptive performance-enhanced path planning algorithm is proposed for unmanned surface vehicle (USV) to improve their responsiveness in dynamic maritime environments. The improved ant colony (ACO) algorithm incorporates a pheromone penalty mechanism and path smoothing to enhance search efficiency and path smoothness by removing redundant nodes and reducing excessive turning. Additionally, the dynamic window approach (DWA) is enhanced through three key modifications: optimizing overshoot, enhancing selection efficiency in candidate path, and adaptively adjusting evaluation function weights. These improvements improve the accuracy of planning and avoidance ability. Comparative analysis based on simulation data indicates that the proposed method yields a measurable improvement in path quality—characterized by reduced travel length and enhanced collision avoidance—leading to more robust navigation performance in complex marine transportation scenarios.
Sun, Jiamian, Li, Weifeng
Study on Route Planning Method of Variable-Configuration Vehicles Considering Mode Decision2026-99-15019/11/2026
The form changes of vehicles directly affect their driving performance, terrain adaptability, and motion efficiency. Conventional path planning techniques are unable to address the unique needs of irregularly shaped vehicles. Consequently, a hierarchical path planning algorithm that takes configuration changes into account is introduced. By introducing a pattern decision-making mechanism, the path planning process is divided into multiple levels. According to the task requirements and environmental conditions, the vehicle configuration is dynamically selected, and the driving path is optimized for the driving characteristics under different configurations, thereby fully utilizing the adaptability and through capability of the vehicle.
Chen, Zixuan, Pi, Dawei, Li, Guangda, Zhou, Yulin
Evaluation Method for Driving Safety on Highway Successive Curves2026-99-15689/11/2026
To evaluate the driving safety performance of continuous curves, this study developed a safety assessment model using a human-computer interaction simulation platform. First, three indicators are selected, including the driver’s heart rate variability, the rate of change in steering wheel angle, and trajectory lateral deviation, which together form a driving safety evaluation indicator system. Secondly, through significance testing and range analysis. Through analysis, four key curve-related elements are identified as having a notable influence on the overall evaluation indicators. A global optimization algorithm using multivariate nonlinear regression is then applied to establish the driving safety model. Finally, taking a dual four-lane highway in Sichuan province as an example, the safety of the successive curve in the project is evaluated. Empirical results show that when the intermediate straight line section H ≤ 4.23, driving is hazardous; 4.23 < H ≤ 4.46, driving is relatively hazardous; 4.46 < H ≤ 4.78, driving is relatively safe; H > 4.78, driving is safe. For oval-shaped curve segments, when H ≤ 4.53, driving is hazardous; 4.53 < H ≤ 5.32, driving is relatively hazardous; 5.32 < H ≤ 5.86, driving is relatively safe; H > 5.86, driving is safe. Through this method, the driving safety of successive curves can be effectively evaluated, particularly with a focus on driver comfort and safety. This provides valuable references for assessing driving risks associated with different combinations of curve elements.
Huang, Yongheng, Zhang, Ruizheng, Sun, Chao, Zeng, Xinjie, Zheng, Liwen
Experimental Study on Handling Stability of an 8×4 Heavy-Duty Truck2026-99-11229/11/2026
To analyze the handling stability of an 8×4 heavy-duty truck, a multi- body dynamics model of the heavy truck was established in ADAMS. Simulation tests for minimum turning radius, double lane change, steering wheel step input, and steady-state returnability were conducted on this model. Analysis of the simulation and experimental results revealed that, except for the significant discrepancy between the rigid-flex coupling model simulation results and the actual values in the returnability experiment, other experimental results were relatively close to the simulation data, indicating that the established vehicle model has high accuracy. It can provide a basis for the subsequent optimization design of this vehicle type.
He, Wenjian, Dong, Fulong
2025 International Conference on Intelligent Equipment, Vehicle Engineering and Automation Control2026-99-11009/11/2026
TOC
Tobolski, Sue
Ant Colony-Artificial Potential Field Fusion Path Planning Algorithm Based on a Maneuver Action Library2026-99-12579/4/2026
The traditional Ant Colony Algorithm has defects such as easy entrapment in local optima due to a simplistic heuristic function and slow convergence due to excessive search directions. A fusion path planning algorithm integrating ant colony optimization and artificial potential field based on a maneuver action library is proposed. Firstly, a mathematical model for UCAV path planning is established. Considering the maneuverability constraints of UCAVs, and drawing on the concept of basic maneuver action libraries for fighter aircraft, an ant colony-potential field fusion path planning algorithm based on a maneuver action library is introduced. Simulation results demonstrate that compared to two other algorithms, the proposed method significantly improves the number of waypoints and planning completion time.
Li, Ruishen, Chen, Xiaogang
Aumovio says system can detect BAC with non-invasive finger touch26AUTP09_109/1/2026
Aumovio, working with sensor-maker trinamiX, has developed a solution to detect a driver's blood alcohol content (BAC) with a non-invasive finger touch. The race to develop systems to prevent alcohol-impaired driving has seen many different solutions proposed, from passively sensing the breath of a driver to analyzing micro-movement of the steering wheel. Aumovio says its system detects BAC with the accuracy of the drawn-blood tests, which have been proven to be more accurate - and less prone to false positives -- than breath or urine tests.
Clonts, Chris
Design of a Digital Twin for Robot-Assembled Workpiece Transfer Station and Virtual Commissioning with It2026-99-03478/14/2026
In this research, the design of a digital twin system for a Robot-Assembled Workpiece Transfer Station (RAWTS) and virtual commissioning with it were detailed, aiming for debugging high-repeatability, high-precision robotic motions. The system employs a structured three-layer digital twin framework, Physical, Digital, and Information Fusion layers, interconnected via an OPC UA communication architecture to enable real-time virtual-physical data synchronization. The 6-axis industrial robot’s kinematic model is established using the D-H parameter method, and the translational end-effector’s kinematic relationships are configured with defined OPEN/CLOSE poses. A behavior-driven digital twin model is constructed within NX MCD, incorporating lightweight-processed 3D geometry from SolidWorks. Virtual commissioning involves PLC and robot program integration, OPC UA-based signal mapping, and kinematic path planning with reachability validation to avoid singularities and collisions. Key joint angles at critical path points are optimized, and virtual-physical integration debugging is performed, resulting in first-attempt success in physical operation. The study demonstrates that the NX MCD-based digital twin approach effectively validates control logic, optimizes robot trajectories, reduces on-site debugging time, and enhances operational precision and safety, offering a practical reference for digital twin applications in robotic systems.
Zang, Yuping, Wang, Ye, Fu, Hudai, Li, Weiwei, Jiang, Zhiyu, Wang, Dayu
Design of a Four-Wheel Turning Tobacco Harvester Suitable for Hilly Areas2026-99-03338/14/2026
To address the core requirement of “layered ripeness and non-destructive harvesting” in tobacco-growing hilly regions of China, a specialized tobacco leaf harvester was developed. Considering the challenges posed by scattered plots and complex terrain, a four-wheel steering chassis system was proposed. The platform adopts a four-wheel independent drive and steering (4WID-4WIS) configuration, powered by DC servo motors and integrated with a microcontroller-based ROS system. The resulting drive chain—comprising motors, gear reducers, and off-road tires—achieves a maximum operating speed of 0.5 m/s. A novel rotary cross-blade harvesting module was designed in conjunction with a conveyor-based transmission mechanism, enabling stratified harvesting and leaf transport. Full-condition field tests were conducted. In terms of mobility, the harvester achieved stable operation at 0.5 m/s on cement roads, 0.1–0.2 m/s in fields, and demonstrated slip-free climbing on 20° slopes. In terms of harvesting performance, the system’s adjustable modules accommodated varying plant heights; however, issues with blade grip were observed when handling irregularly slanted stalks, affecting collection efficiency. During continuous field entry and exit operations, no mechanical failures occurred, verifying the prototype’s operational stability. This study introduces an innovative combination of omnidirectional mobile chassis and stratified blade modules, offering technical support for the modernization of tobacco agriculture. Further refinement of the harvesting strategy will be pursued to enhance practicality.
Guo, Ting, Gu, Jin, Li, Wen, Tang, Xiaoming, Long, Chao, Yang, Dongchao
Modeling and Simulation of the Quick Discharge Hydraulic System of an Internal Mixer2026-99-03058/14/2026
The hydraulic system of the mechanical lever locking device of the internal mixer and the hydraulic system of the gear rack swing hydraulic cylinder are developed. The AMESim simulation models of the two systems are established, and the simulation parameters of each hydraulic element are reasonably set. Firstly, the relationship between the opening time of the mechanical lever locking device of the internal mixer and the charging volume and pressure of the accumulator is analyzed. It is found that the locking time of the mechanical lever locking device of the internal mixer needs about 10 s, while the unlocking time is less than 0.05 s. The unlocking and rubber discharging speed is very fast, and the opening time of the mechanical lever locking device of the internal mixer will become shorter when the pressure or volume of the accumulator air bag becomes larger; Through the analysis of the hydraulic system of the rack and pinion swing hydraulic cylinder, it is obtained that when the air bag volume of the accumulator is 7.557 L, the displacement of the piston rod of the rack and pinion swing hydraulic cylinder rises fastest, which is shortened from 20 s to about 15 s; The greater the air bag pressure of the accumulator is, the faster the displacement of the piston rod of the gear rack swing hydraulic cylinder rises, which is shortened from 20 s to 18 s.
Zhang, Haoqiang, Cai, Liu
Attention and Intrinsic Curiosity-Enhanced Deep Reinforcement Learning for Path Planning in Dynamic Environments2026-01-50567/31/2026
Efficient and reliable path planning remains a core challenge for autonomous vehicles operating in dynamic and crowded environments. Although Deep Reinforcement Learning (DRL) has shown considerable potential in autonomous decision-making, it still faces challenges such as insufficient feature extraction, sparse rewards, and low obstacle avoidance efficiency in complex scenarios. To address these issues, this paper proposes an end-to-end path planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization (PPO) algorithm, the framework incorporates a dual-channel attention convolutional neural network module (Attention-CNN) to enhance spatial and semantic understanding of dynamic obstacles, and introduces an Intrinsic Curiosity Module (ICM) to promote active exploration in sparse-reward settings. Furthermore, a reactive avoidance reward function based on velocity-obstacle theory is designed and embedded to achieve real-time proactive collision avoidance in highly dynamic environments. Experiments are conducted in a semi-structured dynamic crowd scenario constructed on the GAZEBO simulation platform. The results demonstrate that PPO-ICM-Attn achieves significant improvements in key metrics such as path success rate, travel time, and path efficiency compared to baseline methods like A*+DWA and standard DRL. Although the gap remains in path efficiency compared to A*+DWA, the proposed method exhibits superior robustness and navigation performance overall, validating its effectiveness in complex dynamic environments.
Shen, Shiquan, Liu, Jiahao, Chen, Zheng, Li, Zongdian, Zhao, Yuting, Wu, Minggong, Zhao, Jie, Qin, Zongquan, Wang, Yanfeng
Research on Fault-Tolerant Control Strategy for Drive System Failure of Corner Module-by-Wire Chassis Vehicles2026-01-50547/31/2026
As an emerging research focus, corner module-by-wire chassis vehicles overcome the limitations of traditional chassis in flexibility, cost, and development efficiency, serving as a key infrastructure in the autonomous driving era. However, their numerous actuators raise significant actuator failure risks. This paper analyzes the characteristics of such vehicles and studies fault-tolerant control for drive system failures. Firstly, a vehicle model for the corner module-by-wire chassis was established based on CarSim and Simulink. Then, a hierarchical lateral stability control strategy was designed for the non-faulty actuators: the decision control layer employed sliding mode control (SMC) and fuzzy PID control, selecting the optimal method to output additional yaw moments; the control allocation layer distributed the upper-level target yaw moments based on the vertical load of the tires, converting them into individual wheel torques to meet the constraints. For the drive system, potential fault scenarios were analyzed and their fault modes were classified. By using the non-faulty actuators for torque reconstruction, fault-tolerant strategies were designed for single-motor, diagonal dual-motor, and coaxial dual-motor faults. A co-simulation platform was built using MATLAB/Simulink and CarSim, testing the stability control strategies under three fault modes in constant-speed straight-line and double-lane change conditions. Simulation results show that the designed drive system fault-tolerant control strategy effectively maintains the vehicle’s expected dynamic performance and stability.
Zheng, Hongyu, Zhang, Tianhao, Zhang, Yuzhou
Design and Research of Robotic Arm for Belt Conveyor Roller Replacement2026-99-01877/31/2026
To address the high failure rate of rollers in coal mine belt conveyors, the inefficiency of manual replacement, and the operational disruptions caused by maintenance shutdowns, this study proposes a robotic arm system capable of replacing rollers without halting conveyor operations. The research focuses on the kinematic performance and path planning strategy of the robotic arm. A kinematic model is established using the Denavit–Hartenberg (DH) parameters, and the workspace distribution is analyzed via the Monte Carlo method. The results show that the horizontal reach exceeds 2020 mm and the vertical reach extends up to 2000 mm, which fully satisfies the spatial requirements for roller replacement across the entire conveyor system. In the path planning phase, an obstacle expansion model is constructed, and an improved Informed RRT* algorithm is implemented to generate collision-free trajectories, ensuring effective obstacle avoidance. To improve trajectory smoothness, path pruning and cubic B-spline interpolation are applied to refine the initial paths. For trajectory planning in joint space, quintic polynomial interpolation is employed, with boundary conditions set to ensure zero velocity and zero acceleration at both the start and end points, thereby guaranteeing smooth and stable motion of the robotic arm. Simulation results indicate that joint angles, angular velocities, and angular accelerations vary smoothly throughout the roller grasping process, without abrupt changes, and converge to zero at the beginning and end of the trajectory. End-effector trajectory tracking error analysis reveals positioning errors within 0.8 mm for side rollers and 3 mm for central rollers, well within acceptable engineering accuracy thresholds. This work provides a theoretical foundation and a practical implementation framework for advancing automation and intelligent operation in roller replacement tasks within coal mine belt conveyor systems.
Pu, Congyuan, Qian, Ke
Robotic Grinding Trajectory Planning and Correction Method for Large-Scale Complex-Shaped Products2026-99-02277/31/2026
Due to the inherent characteristics of large dimensions, complex curved surfaces, and densely distributed protrusions present in aerospace products, conventional offline programming and trajectory planning techniques for robots primarily prioritize the facilitation of uninterrupted grinding processes and the generation of points along trajectories on surfaces characterized by smoothness. However, these methods encounter challenges in identifying and proactively avoiding surface protrusions during the planning phase. The present paper puts forth a proposal for an automated robot trajectory planning method for grinding operations. This method is predicated on the integration of region partitioning and deformation correction. Specifically, this method first identifies protrusions based on curvature features and rule matching, followed by an analysis of the feasible workspace of a six-axis industrial robot equipped with an external axis. The product surface is discretized into multiple regional units according to the distribution characteristics of protrusions and the constraints of the feasible workspace. Subsequently, a parameter-optimized parallel sectioning method is employed to independently plan trajectories for each unit. The utilization of on-site measured point cloud data facilitates the analysis of contour deviations. In addition, grinding trajectories are dynamically corrected to meet high-precision process requirements. This approach effectively overcomes the challenge that trajectory planning for large-scale, complex-shaped products is easily affected by protrusions. According to the established methodology, the development of an offline programming software system for robotic automatic grinding was initiated. To this end, experiments were conducted on aircraft wall panels to plan and modify grinding trajectories using the proposed method. This process was undertaken to validate the effectiveness and engineering practicability of the proposed method.
Fan, Changhao, Wang, Mingyang, Lv, Ruiqiang, Zhou, Peng
Analysis and Optimization of Braking Deviation for Commercial Vehicles Based on ADAMS2026-99-08027/30/2026
Given the braking deviation of commercial vehicles, this paper discusses the influencing factors and uses Adams simulation software to accurately model the vehicle model due to the unreasonable match between the suspension system and the steering system. Through K&C analysis and dynamics analysis of the model, the root cause of braking deviation is identified, and the simulation method is used to quickly realize optimization and verification.
Yan, Tang, Wang, Jingxian, Sun, Hongyang, Wu, Zhen
Research on Model State Machine-Based Simulation and Validation of Civil Aircraft Ground Deceleration Functional Architecture2026-99-08347/30/2026
The implementation of the ground deceleration function in civil aircraft represents a critically complex process that deeply relies on the seamless collaboration of multiple onboard systems, including but not limited to braking, thrust reversal, spoiler, and steering systems. The operational logic governing these systems is highly intricate, characterized by tightly coupled interactions, stringent safety requirements, and a vast array of diverse physical and logical interfaces. This inherent complexity makes it exceptionally difficult to gain a thorough, system-level understanding of the implementation mechanisms and collaborative principles solely through traditional means of examining extensive, yet often fragmented, design documentation. The limitations of document-based analysis frequently lead to unforeseen integration conflicts, which are typically discovered late in the development cycle, resulting in substantial rework costs and project delays. To address this pervasive industry challenge, this paper selects the aircraft ground deceleration function as a representative case study and proposes an innovative, simulation-based validation methodology. This approach systematically utilizes model state machines to create a dynamic digital representation of the system-of-systems, enabling rigorous validation of aircraft deceleration requirements under various operational scenarios. By adopting this model-based systems engineering (MBSE) paradigm for mechanism representation, our approach effectively captures the nuanced coordination, timing dependencies, and dynamic interactions within the multi-system operational logic. It thereby facilitates the intuitive identification, analysis, and resolution of potential design flaws, including logical conflicts, deadlocks, race conditions, and uncovered or ambiguous requirements. Consequently, the method not only provides a robust framework for validating the aircraft’s function-related design requirements with greater confidence but also offers crucial, data-driven support for the iterative optimization and evolution of the overall functional architecture. The fundamental value proposition of this research lies in its transformative capability to convert implicit design knowledge and assumptions—originally scattered across voluminous documents, specifications, and expert minds—into an integrated set of executable, observable, and analyzable formal models. This digital thread enables systems engineers and designers to identify deep-seated integration and coordination issues proactively during the early conceptual and detailed design stages, rather than relying on discovery during the late, costly integration and testing phases. By shifting validation left in the development V-cycle, this approach significantly reduces the risk of major design changes and associated cost overruns later in the project lifecycle. Ultimately, it effectively enhances the overall maturity, safety, certifiability, and operational reliability of complex aircraft function development, paving the way for more efficient and predictable engineering processes.
Wang, Mingqian, Yu, Qiao, Yu, Miao, Tang, Chao
Remote Steering Control SystemsJ2588_202607 (Current)7/23/2026
This SAE Information Report establishes a uniform procedure for assuring the manufactured quality, installed utility, and performance of automotive remote steering controls other than those provided by the vehicle manufacturer (OEM). These products are intended to provide driving capability to persons with physical disabilities. The adaptive modifications seek to compensate for lost or reduced function in the extremities of the driver with a disability. Remote steering controls are designed to provide a steering input device alternative to the OEM steering wheel that either reduces the required input force, changes the required range of motion or changes the location of the steering control, or any combination of the above. These controls supplement by power, other than by the driver’s own muscular efforts, the force output of the driver with a disability. Because this is an Information Report, the numerical values for performance measurements presented in this report and in the accompanying Test Procedure, while based upon the best knowledge available at the time, have not been validated by a testing of the Test Procedure.
Adaptive Devices Standards Committee
Research on Quadrotor Trajectory Tracking Based on Model Predictive Control2026-99-18507/17/2026
Quadrotors (UAVs) are widely used in intelligent inspection, environmental monitoring, and logistics due to their simple structure, strong maneuverability, and vertical take-off and landing capabilities. However, their highly nonlinear, strongly coupled, and highly constrained dynamic characteristics make trajectory tracking control a challenging task. To improve trajectory tracking accuracy and control robustness, this paper proposes a quadrotor trajectory tracking method based on model predictive control (MPC). First, a six-degree-of-freedom dynamic model of the quadrotor is established and linearized with small disturbances to transform it into a state-space model suitable for MPC design. An MPC optimization controller is then constructed, with an objective function that minimizes state error and imposes an input energy penalty, while explicitly considering the system's input and state constraints. Simulation results demonstrate that this method exhibits good tracking accuracy and control smoothness for typical trajectory tracking tasks (such as circular and spiral trajectory tracking). Compared with traditional PID and LQR controllers, the proposed method significantly improves maximum error, mean square error, and interference rejection. This study provides an engineering-feasible optimization control framework for UAV trajectory control.
Peng, Fei, Tao, Zhong, Gao, Qiang, Jia, Bobo
UAV Path Planning Method for Urban-Mountain Mixed Environments Based on Improved Divine Religions Algorithm2026-99-18407/17/2026
This paper, for the first time, applies the Divine Religions Algorithm (DRA) to three-dimensional UAV path planning. Targeting the complex terrain of urban-mountain mixed environments, we propose a novel method that incorporates multiple enhancements, including A* initialization, single-point disturbance mutation, and adaptive weighting. First, the A* algorithm is employed to generate high-quality initial paths, serving as the skeleton of the population. Innovative mechanisms such as terrain-adaptive disturbances and dynamic weight adjustment are integrated to achieve both efficiency and robustness in path optimization. Comparative experiments with Genetic Algorithm (GA) and Crowned Porcupine Optimization (CPO) show that the improved DRA algorithm exhibits significant advantages in terms of path length, safety margin, average altitude variation, average turning angle, and overall cost function. It consistently obtains superior paths and achieves faster convergence. The results demonstrate that the proposed approach provides an efficient, adaptive, and practical intelligent optimization tool for UAV path planning in urban-mountain mixed or similarly complex environments, offering promising prospects for engineering applications.
Fang, Lianyu, Yi, Wenjun
On Disturbance-Aware Minimum-Time Trajectory Planning: Evidence from Tests on a Dynamic Driving Simulator10-10-03-00267/13/2026
This work aims to investigate how disturbance-aware, robustness-embedding reference trajectories translate into actual driving performance when executed by professional drivers in a dynamic driving simulator. The study compares three planned reference trajectories against a free-driving baseline (NO-REF) to assess the trade-offs between lap time (LT) performance and steering effort: NOM, the nominal time-optimal trajectory; TLC, a track-limit-robust, time-optimal trajectory obtained by tightening margins to the track edges; and FLC, a friction-limit-robust, time-optimal trajectory obtained by tightening against axle/tire saturation. All reference trajectories share the same minimum LT objective with a small steering-smoothness regularizer, and are evaluated with two professional drivers driving a high-performance car on a virtual track. The reference trajectories stem from a disturbance-aware minimum-LT framework recently proposed by some of the authors, where worst-case disturbance growth is propagated over a finite horizon and used to tighten tire-friction and track-limit constraints, preserving performance while delivering probabilistic safety margins. LT and steering energy (SE) are evaluated as indicators of driving performance and steering effort, respectively, while RMS values of lateral deviation, speed error, and drift angle are used to characterize driving style. The results reveal a Pareto-like trade-off between LT and SE: NOM achieves the shortest LT, but with the highest SE, TLC minimizes SE at the expense of longer LT, while FLC lies near the efficient frontier, markedly reducing SE relative to NOM with only a minor LT increase. Removing reference trajectories (NO-REF) leads to both higher SE and longer LT, confirming that trajectory guidance improves pace and control efficiency. Overall, the findings highlight reference-based and disturbance-aware planning, particularly the FLC variant, as effective tools for training and for achieving fast yet stable trajectories.
Masoni, Matteo, Palermo, Vincenzo, Gabiccini, Marco, Gulisano, Martino, Previati, Giorgio, Gobbi, Massimiliano, Comolli, Francesco, Mastinu, Gianpiero, Guiggiani, Massimo
A Review of UAV Support Technologies for Low-Altitude Airspace Operation and Management2026-99-05977/10/2026
In recent years, with the low-altitude economy developing rapidly, the operation and management of low-altitude airspace has gradually become a hot topic. Unmanned aerial vehicles (UAVs) constitute a fundamental component of the low-altitude airspace ecosystem, significantly influencing its structure and functionality. The technological advancement of UAVs has fundamentally transformed the operational paradigm for low-altitude airspace management. This paper presents a comprehensive review of UAV-supported technologies in the context of low-altitude airspace operations and management. It systematically analyzes key technologies and applications of UAVs in areas such as airspace capacity and safety assessment, trajectory planning, and standardized flight management. Drawing from kinematic analysis and traffic flow theory, UAV density control and collision risk prediction offer quantitative insights into airspace capacity evaluation. Additionally, probabilistic analysis and simulation techniques enhance the accuracy and efficiency of safety assessments. In trajectory planning, multi-objective optimization algorithms tailored to operational scenarios—such as logistics delivery and agricultural operations—have significantly improved the utilization of airspace resources. Concurrently, collision avoidance techniques leveraging graph search, numerical optimization, and machine learning ensure flight safety in complex environments. Standardized flight management relies on pilot qualification review, airworthiness certification, and planning standardization, while discussing airspace segmentation strategies based on geofencing and intelligent control systems. Future developments in UAV-supported technologies are expected to trend toward higher precision, intelligence, and regulatory integration. By incorporating cutting-edge fields such as deep reinforcement learning and digital integration, these technologies are poised to further enhance the efficiency and safety of low-altitude airspace management, thereby providing robust technical support for the sustainable growth of the low-altitude economy.
Gong, Lei, Ma, Zhenxiao, Luo, Qin
Path Planning Algorithm for Self-Driving Cars Based on High-Precision Maps and Improved A*2026-99-05887/10/2026
To address the limitations of the traditional A* algorithm in lane-level navigation, we propose an autonomous vehicle path planning algorithm based on high-precision maps and an improved A* algorithm to ensure effective application in complex traffic environments. We construct a hierarchical high-precision map based on the Lanelet2 framework to achieve structured modeling of complex road environments. To address the adaptability issues of the A* algorithm in lane-level navigation, we propose optimization schemes, including heuristic function improvements, path segment division, and target point validity verification, to ensure that vehicles can autonomously change lanes on multi-lane roads. By combining dynamic programming (DP) and quadratic programming (QP), we ensure the safety and smoothness of the path. Simulation results demonstrate that the optimized algorithm enables smooth stopping and starting at traffic lights in structured road environments and autonomous lane changes on multi-lane roads. Compared to using DP alone, QP provides smoother and safer driving paths and exhibits superior obstacle avoidance performance in speed planning. This method effectively ensures the rationality of path planning in complex road environments while strictly adhering to traffic rules, thereby enhancing the safety and reliability of path planning.
Wang, Siyu, Zhou, Rong, Shi, Tian, Xu, Zhen, Zhao, Zhiguo
Research on Low-Altitude Economic Air Traffic Control Model Based on Spatiotemporal Constraint Reinforcement Learning2026-99-05807/10/2026
With the rapid development of the low-altitude economy—represented by drone logistics, aerial inspections, and air taxis—air traffic has exhibited new characteristics including diverse forms, high density, and significant speed differences. To address these changes, the traditional air traffic control system requires upgrades, particularly in dynamic aircraft scheduling. This study proposes an air traffic control model (DS-ATM) tailored to this domain, built on the Deepseek large model. By integrating spatiotemporal graph neural networks with multi-objective reinforcement learning algorithms, the model achieves real-time path planning and conflict resolution in complex airspace environments. Validated using public datasets such as OpenSky Network, NASA UTM Dataset, and METAR meteorological data, experimental results demonstrate its significant advantages in reducing conflict rates and scheduling delays.
Li, Rui, Zhao, Fangyu, She, Yue, Li, Wujie
Steering System Fault Diagnosis and Fault-Tolerant Control of Active Wheel-Corner Electric Vehicles10-10-04-00367/9/2026
The Active Wheel-Corner (AWC) integrates driving, braking, steering, and suspension systems into the wheel end, forming a fully drive-by-wire, four-wheel independent steering and four-wheel independent driving (4WIS&4WID) vehicle platform. While improving vehicle control performance, the full by-wire architecture also places higher demands on system reliability and fault tolerance. The steer-by-wire system has electrical, communication, and software failure risks, which may cause the vehicle to lose steering capability and trigger severe traffic accidents. This article proposes a hierarchical active fault-tolerant control strategy based on fault information reconstruction (FAST-FTC), enabling fault diagnosis and active fault-tolerant control when the steer-by-wire system fails, effectively ensuring the steering maneuverability and lateral stability of the vehicle under fault conditions. First, the strategy designs an adaptive observer combined with the Dugoff tire model to estimate nonlinear tire forces, while introducing a fault factor to achieve quantitative grading of steering system faults. Second, a hierarchical controller is designed for steering system faults. The upper-level controller, based on Adaptive Super-Twisting Sliding Mode Control (AST-SMC), determines the generalized forces required to track the desired trajectory under different fault conditions. The lower-level controller, based on the Fault-Aware Model Predictive Control (FA-MPC) strategy, dynamically adjusts weight matrices according to the fault factor and tire reconstructed stiffness, coordinating the allocation of four-wheel driving and the steering of healthy wheels to ensure lateral stability. Finally, the effectiveness of the proposed active fault-tolerant control strategy is validated through Hardware-in-the-Loop (HIL) simulation and real-vehicle tests.
Xiao, Feng, Jiang, Yueyong, Cheng, Rui, Xu, Changhe, Tang, Xiangjiao, Gao, Fengling, Li, Jianhua
A Framework for Precautionary Safety for Automated Driving Systems—Toward Tactical Decisions Fulfilling Quantitative Risk Acceptance Criteria12-09-04-00287/8/2026
Safety of Automated Driving Systems (ADSs) is arguably one of the main remaining barriers before widespread market deployment. While there exists a plethora of methods for planning a trajectory that fulfils certain constraints, what those constraints should look like, to enable effective planning of safe trajectories, is still being discussed. In this article, we generalize the concept of Precautionary Safety (PCS) and present a framework providing constraints on the tactical and operational decisions of the ADS. Such constraints consider the ADS’ capabilities, the external conditions, knowledge of statistically relevant events and behaviors of other traffic actors, as well as the controllability of these events. The proposed framework enables assessment of the statistical fulfilment of quantitative risk acceptance criteria (QRACs), including requirements on accident, injury, and fatality rates. The framework further provides a means to dynamically adapt the constraints used for trajectory planning, i.e., to adapt the driving to the situation at hand. A case study, considering a possible collision scenario with a jaywalking pedestrian and a rear-end collision with a trailing vehicle, is provided to showcase the applicability and usefulness of the presented framework. The simulation-based case study displays the safety benefits from considering QRACs with multiple injury risk levels and further shows how the proposed PCS framework can be applied in practice.
Gyllenhammar, Magnus, de Campos, Gabriel Rodrigues, Sandblom, Fredrik, Törngren, Martin, Fredriksson, Jonas
Context-Dependent Assessment of Driving Dynamics and User Perception in Automated Driving2026-01-07577/1/2026
As automation advances and occupants transition from active drivers to passive passengers, understanding how automated driving behavior is evaluated becomes increasingly important. While longitudinal and lateral vehicle dynamics are known to influence perceived comfort and safety, it remains unclear to what extent motion–perception relationships remain stable across urban traffic contexts. This study compares two real-world investigations of automated driving: a left-turn maneuver at a signalized intersection on a test track and a roundabout maneuver with a shuttle in public traffic. Both datasets include high-resolution vehicle dynamics and structured subjective ratings. A consistent objectification approach was applied to examine the transferability of motion–perception relationships across contexts. However, differences in vehicle platform, automation level, trajectory characteristics, and study design limit direct comparability and require cautious interpretation. Despite partially overlapping ranges in selected peak-based dynamic parameters, such as longitudinal acceleration, subjective comfort and safety ratings were consistently higher in the roundabout scenario. Furthermore, strong associations were observed between motion parameters and subjective evaluations in the intersection context (adj. R2 up to 0.891), whereas objective parameters showed only limited explanatory power in the roundabout scenario (adj. R2 ≤ 0.06). The results indicate that motion–perception relationships derived within a specific context may not be directly transferable across different traffic scenarios. The findings highlight limitations of globally derived motion-based evaluation models and underline the importance of validating objectification approaches across diverse operational environments.
Panzer, Anna, Strenge, Emma, Iatropoulos, Jannes, Henze, Roman
Cooperative Edge Extension for Automated Driving Level 4 and Beyond2026-01-07767/1/2026
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
Lüntzel, Vitus, Lukezic, Nikola, Kraus, David, Seidel, Luca, Beck, Maximilian, Schindewolf, Marc, Sax, Eric
Impact of Network-Induced Delays on Autonomous Vehicle Steering Stability10-10-03-00246/25/2026
This article presents a cross-layer framework that integrates realistic vehicle-to-network-to-vehicle (V2N2V) delay characterization with a rigorous stability analysis of automated vehicle steering control. Both constant and network-induced time-varying delays modeled via deterministic bounds are addressed. For constant delays, delay-independent stability regions within the controller gain space are analytically derived. For time-varying delays with stochastic network origins, modeled using deterministic bounds, a refined Lyapunov–Krasovskii functional (LKF) incorporating augmented single- and double-integral terms is constructed. To establish delay-dependent linear matrix inequality (LMI) conditions, a reciprocally convex combination approach is employed to handle the delay interval partitioning, and the second-order Bessel–Legendre inequality is applied to tighten the integral quadratic bounds. The resulting LMI conditions explicitly capture the coupled effects of delay magnitude, delay variation rate, and control gains on closed-loop stability. Simulations of a lane-keeping scenario confirm that the predicted stability boundaries accurately match the closed-loop system behavior. Notably, incorporating a realistic time-varying V2N2V delay profile into the controller design reduces the lateral-state root-mean-square error (RMSE) by over 54% and decreases the settling time by a factor of 10 compared to designs relying on an average-delay assumption. However, high packet loss rates are shown to still induce residual oscillations due to information scarcity. Ultimately, these results elucidate delay-induced instability mechanisms and provide practical guidelines for designing delay-robust steering controllers for connected and automated vehicles.
Li, Jialin, Lu, Jianwei, Wei, Heng, Ao, Di
Automating Component NVH Characterization: A Systematic Approach for Component Test Bench Characterization2026-01-07046/20/2026
Vehicle electrification and accelerated development cycles create a need for virtual Noise, Vibration and Harshness (NVH) development tools which are fast, precise and, seamlessly interchangeable between development sites, suppliers and OEMs. Component-based Transfer Path Analysis (C-TPA), standardized in ISO 20270:2019, enables independent component characterization and integration with virtual models to predict sound and vibration in new assemblies, referred to as Virtual Prototype Assemblies (VPA). However, conventional measurements are labor-intensive, typically restricted to a small number of samples, and overlook production variability. This paper introduces a fully automated, ISO 20270-compliant C-TPA system for non-rigid test benches, featuring a pre-instrumented test fixture with multiple vibration shakers and sensors automatically linked to a data acquisition system for immediate processing. Components can be characterized within minutes, with blocked forces directly integrated into a VPA workflow, replacing time-intensive in-vehicle testing with a repeatable, operator-independent bench procedure. A case study on an automotive steering system demonstrates the method’s accuracy, repeatability, and efficiency, along with its ability to predict realistic interior sound pressure levels and capture production variability, enabling robust virtual NVH evaluation early in the development cycle.
Sturm, Michael, Wienen, Kevin, Brandstetter, Markus, Sorber, Eric, Corbeels, Patrick, Verrecas, Bart, Gonçalves, Vinícius
Unsupervised Feature Frequency Map Extraction for Simple C-EPS Defect Detection2026-01-07176/20/2026
In recent years, the automotive industry has actively explored the application of various AI-based models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformers to improve defect detection rates at the End-of-Line (EOL) stage. However, implementing these approaches in the Noise, Vibration, and Harshness (NVH) area face several practical challenges: ① extended evaluation times compared to other data types, which limit the quantity of training data and lead to overfitting; ② label imbalance caused by the relatively small amount of defect data; ③ reduced labeling accuracy due to human error; ④ decreased robustness under domain shifts such as changes in jig fixtures, test environments, and signal-to-noise ratio (SNR); ⑤ diminished model reliability when new defect arise during development; and ⑥ constraints imposed by compatibility requirements with existing test equipment. This study proposes a Convolutional Autoencoder (CAE) based framework trained on NVH datasets collected from normal and defective Column-type Electric Power Steering (C-EPS) systems. Latent variables at the bottleneck layer are used for dimension reduction, enabling visualization and unsupervised classification using a clustering algorithm. A classification model derived from the encoder is fine-tuned with clustered data, and Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable AI (XAI) technique, is applied to extract Feature Frequency Maps (FFM) highlighting defect-related noise and vibration characteristics. The proposed approach does not rely on the deep learning model to directly classify defect. Instead, it utilizes extracted FFM as weights(mask) to detect defect. This method enables quantitative data representation and ensures high applicability with existing EOL equipment. Post-processing within the FFM enables root cause analysis, reducing issue resolution time and supporting integration with conventional signal analysis techniques.
Park, Jun-Seo, Jo, Hyeon-Choel, Cho, In-Je, Seo, Jae-Yong, Yoo, Seong-Sik
Challenges in Predicting Fatigue Damage during Agricultural Operations: Insights from Machine Learning Models02-19-03-00176/10/2026
Agricultural vehicles operating in rough environments experience increased fatigue damage accumulation, which may decrease machine safety and reliability. Autonomous agricultural machines offer an opportunity to incorporate fatigue damage considerations into path planning. This work investigates whether machine learning can predict fatigue damage to a tractor chassis using light detection and ranging (LiDAR)-based terrain features, vehicle speed, and rotational vehicle state data (e.g., triaxial angle, angular velocity, and angular acceleration). Fatigue damage was estimated using the Rupp filter and the Durability Transfer Concept. Following poor predictive performance of the machine learning models, an exploratory analysis of damage histograms, dominant frequency, and acceleration magnitude was performed. Results indicated that most estimated fatigue damage occurred in the 0–2 Hz band, which coincides with the frequency range of terrain-induced acceleration. On-road driving led to the greatest fatigue damage, potentially due to the harder driving surface and increased vehicle speed. Differences between root mean square (RMS) acceleration magnitude and fatigue damage indicate that isolated high-magnitude events may have contributed to increased estimated fatigue damage. Several suggestions for future development were identified. Identification of the endurance limit of the tractor chassis will permit the removal of nondamaging events, improving label accuracy. Furthermore, the presence of a front-loader implement may have impacted chassis acceleration. Thus, a comprehensive dataset with multiple implement configurations is needed to determine the influence of implement configuration on dynamics and resultant damage.
Govers, Megan Emily, Hamilton-Wright, Andrew, Hassan, Marwan, Oliver, Michele L.
Development and Validation of a One-Dimensional Model for an Opposed-Piston Free-Piston Engine Generator with Parametric Analysis2026-37-00216/9/2026
Opposed-piston free-piston engine generators (OFPEGs) are emerging as a promising technology for next-generation hybrid and electrified transportation systems due to their high efficiency, reduced mechanical complexity, and improved noise, vibration, and harshness (NVH) characteristics. However, due to eliminating the conventional crankshaft mechanism and directly coupling a free-piston engine with linear generators, performance of OFPEG systems is governed by a strong coupling between piston dynamics, in-cylinder combustion processes, and electrical loading conditions. This coupling presents substantial challenges for system design, control, and optimization, limiting the further development and application of OFPEGs. Existing researches lack a comprehensive numerical model that integrates detailed in-cylinder thermodynamic process with control system of linear generator, and quantitative analysis of the effect of piston motion trajectory on system performance remains insufficiently explored. In this study, a novel one-dimensional OFPEG model is developed in Gasdyn and coupled with a linear motor model and a control strategy in MATLAB/Simulink, thus forming a complete numerical model for OFPEG. The model is validated against experimental measurements, demonstrating effective prediction of thermodynamic and dynamic performance with acceptable errors. Based on the validated model, the effects of varying piston motion trajectory on system performance are analyzed. Lower Rt and higher Ωcom and Ωexp are recommended for higher performance. When Rt is reduced to 2.5:1, thermal efficiency and indicated power improve to 36.3% and 3.4 kW, respectively. When Ωcom is increased to 0.6, thermal efficiency and indicated power improve to 35.5% and 3.22 kW, respectively. When Ωexp is increased to 0.6, thermal efficiency and indicated power improve to 36.0% and 3.41 kW, respectively. These improvements are primarily attributed to reduced heat transfer losses and enhanced scavenging efficiency under the modified trajectories. The results provide valuable insights into the optimization of piston motion trajectory to achieve higher performance. Furthermore, the proposed numerical model provides an effective tool for OFPEG design, optimization, and control strategy development, supporting the advancement of high-efficiency, low-carbon OFPEG systems for future transportation applications.
Wang, Jiayu, Morandi, Nicola, Lucchini, Tommaso, FENG, HUIHUA, Jia, Boru, Ren, Peirong
Quantum-Inspired Optimization for Trajectory Planning of Reusable Launch Vehicles across Diverse Missions2026-26-07256/1/2026
Trajectory optimization for reusable launch vehicles is a critical challenge in space mission design, aiming to determine fuel-efficient paths for spacecraft during ascent, hover, and descent phases. Minimizing fuel consumption not only enhances cost-effectiveness but also improves mission sustainability. The optimization process is governed by nonlinear orbital mechanics, gravitational perturbations, atmospheric drag, and operational constraints such as thrust limits and collision avoidance. These factors make the problem highly non-convex and discontinuous, posing significant difficulties for classical gradient-based approaches, which often fail to identify global optima. In this work, we formulate the trajectory optimization problem for a reusable rocket executing an ascent–hover–descent cycle. The vehicle must ascend to a specified target altitude, maintain a stable hover for a given duration, and then return to the launch site. The primary decision variable is the throttle control profile, which is represented as a vector of throttle settings over a discretized time horizon and governs thrust levels throughout all flight phases. The objective is to minimize total fuel consumption while satisfying all physical and operational constraints. To address the problem’s complexity, we employ the BQPhy platform, which implements Quantum-Inspired Evolutionary Optimization (QIEO). This metaheuristic approach efficiently explores the search space, overcoming the limitations of traditional methods. Comparative analysis with a classical Genetic Algorithm (GA) shows that the QIEO-based method delivers solutions 5–10 times faster while achieving superior fuel-optimal trajectories. The proposed approach highlights the potential of quantum-inspired optimization for high-dimensional, nonlinear aerospace trajectory design problems, offering a promising solution for enhancing the efficiency of reusable spaceflight operations.
Eswara Sai Kumar, Kandula, Singh, Utkarsh, Pohankar, Pritam, A, Anoop, Maharana, Priyabrata, Lineswala, Rut
Autonomous Aircraft Docking: A Vision-Based Approach to Aircraft Parking Systems2026-26-07866/1/2026
Automated aircraft parking systems enhance airport ground operations by enabling precise and autonomous docking of aircraft at gates. These systems reduce turnaround time, minimize human error, and optimize apron space through real-time object detection, obstacle avoidance, and dynamic path planning. Unlike fixed guided-path methods, the proposed system adapts to congestion and environmental conditions such as low visibility, ensuring safety and efficient maneuvering. Validation through simulation demonstrates the system’s potential to improve operational resilience and support scalable automation in future airport infrastructure.
Penugonda, Navya Sunaina, Ediga, Venkatadiwakar Goud
Coordinated Stability Control for Active Rear Steering Vehicles Based on Dynamic Stability Region2026-99-07135/15/2026
To improve the handling stability of four-wheel steering/drive vehicles under complex high-speed maneuvers, this study proposes a coordinated control strategy that incorporates Active Rear Steering (ARS) and Direct Yaw Moment Control (DYC) based on a dynamic stability region. Firstly, a four-wheel steering vehicle dynamics model including lateral motion and yaw motion is established, and the ideal values of the control variables are determined. Secondly, combined with the fuzzy control theory and double-line method, the boundary of the dynamic stability region is obtained in the sideslip angle-sideslip angle rate β−β̇ phase plane, and the vehicle state is categorized into stable, unstable, and critical stable region. Then, A hierarchical control architecture is designed based on the stability boundary. The upper controller comprehensively solves the target rear wheel angle and additional yaw moment through feedforward feedback control; the coordinated control layer allocates control weights according to the stable state of the vehicle; the lower controller optimizes torque distribution through quadratic programming. Finally, the control strategy is validated by MATLAB/Simulink and CarSim co-simulation platform. The results show that the proposed control strategy reduces the RMS values of yaw rate and sideslip angle by 23.1% and 28.5% respectively, significantly improving the handling stability of the vehicle.
Nie, Kehe, Chen, Jin, Wang, Falong, Li, Ren, Bai, Xianxu
Linear Trajectory Planning of Hydrogen Storage Tank with Unequal Polar Holes2026-99-07185/15/2026
In this paper, the design and process research of uniform filling linear trajectory for filament wound hydrogen storage tank with unequal polar holes are carried out. Firstly, by optimizing the slip coefficient, the winding angles of the left and right heads are smoothly and continuously transitioned to the cylindrical section. We study the necessary conditions for achieving the central angle of uniform filling, and calculate the tangent points of the trajectory line based on the continuous fraction principle. Meanwhile, the slip coefficients at the left and right ends that satisfy stable winding and uniform covering are determined. Based on the equal contour constraint conditions, we analyze the motion trajectory equation of the four-axis winding machine and convert it into the corresponding machine code for actual winding operations. Experimental results show that stable winding of fibers on the surface of the unequal-polar-hole mandrel is achieved, and uniform filling and winding effects are obtained after a certain number of winding cycles. Simulation results show that the proposed design parameters and optimization algorithm are feasible and effective.
Chen, Baosen, Fu, Jianhui, Cao, Xuewen, Yu, Libin
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