Browse Topic: Steering systems

Items (2,053)
This research, path planning optimization of the deep Q-network (DQN) algorithm is enhanced through integration with the enhanced deep Q-network (EDQN) for mobile robot (MR) navigation in specific scenarios. This approach involves multiple objectives, such as minimizing path distance, energy consumption, and obstacle avoidance. The proposed algorithm has been adapted to operate MRs in both 10 × 10 and 15 × 15 grid-mapped environments, accommodating both static and dynamic settings. The main objective of the algorithm is to determine the most efficient, optimized path to the target destination. A learning-based MR was utilized to experimentally validate the EDQN methodology, confirming its effectiveness. For robot trajectory tasks, this research demonstrates that the EDQN approach enables collision avoidance, optimizes path efficiency, and achieves practical applicability. Training episodes were implemented over 3000 iterations. In comparison to traditional algorithms such as A*, GA
Arumugam, VengatesanAlagumalai, VasudevanRajendran, Sundarakannan
Autonomous driving technology has indeed become a focal point of research globally, with significant efforts directed towards enhancing its key components: environment perception, vehicle localization, path planning, and motion control. These components work together to enable autonomous vehicles to navigate complex environments safely and efficiently. Among these components, environment perception stands out as critical, as it involves the robust, real-time detection of targets on the road. This process relies heavily on the integration of various sensors, making data fusion an indispensable tool in the early stages of automation. Sensor fusion between the camera and RADAR (Radio Detection and Ranging) has advantages because they are complementary sensors, where fusion combines the high lateral resolution from the vision system with the robustness in the face of adverse weather conditions and light invulnerability of RADAR, as well as having a lower production cost compared to the
Cury, Hachid HabibTeixeira, Evandro Leonardo SilvaSilva, Rafael Rodrigues
The exponential growth of the agribusiness market in Brazil combined with the high receptivity among farmers of new technological solutions has driven the study and implementation of high technology in the field. This work aimed to apply servo-assisted driving technology to enable autonomous mobility in an off-road sugarcane truck responsible for harvesting sugarcane. The technology consists of a conventional hydraulic steering with a motor, ECU and torque and angle sensors responsible for reading input data converted from GPS signals and previously recorded tracking lines. The motor responsible for replacing 100% of the physical force generated by the driver acts in accordance with the required torque demand, and the sensors combined with the ECU correct the course to meet the follow-up line through external communication ports. The accuracy of the system depends exclusively on the accuracy of the GPS signal, in this case reaching 2,5 cm, which is considered extremely high accuracy
Oliveira Santos Neto, AntídioLara, VanderleiSilva, EvertonDestro, DanielMoura, MárcioBorges, FelipeHaegele, Timo
The SAE Formula, a national stage of the international competition, consists of a student project at universities in Brazil that seeks to encourage engineering students to apply the theoretical knowledge obtained in the classroom to practice, dealing with real problems and difficulties in order to prepare them for the job market. The SAE Formula prototype is developed with the intention of competing in the SAE national competition, where teams from various universities in Brazil meet to compete and demonstrate the projects developed during the year. Focusing on the vehicle dynamics subsystem, which can be divided into the braking, suspension, and steering systems of a prototype, the steering system includes main mechanical components such as the front axle sleeves, wheel hub, steering arm, steering column, rack, wheel, and tire. All these components work together with the suspension systems, including suspension arms, “bell crank,” and spring/shock absorber assembly. These components
Rigo, Cristiano Shuji ShimadaNeto, Antonio Dos Reis De FariaGrandinetti, Francisco JoseCastro, Thais SantosDias, Erica XimenesMartins, Marcelo Sampaio
Single lane changing is one of the typical scenarios in vehicle driving. Planning an appropriate lane change trajectory is crucial in autonomous and semi-autonomous vehicle research. Existing polynomial trajectory planning mostly uses cubic or quintic polynomials, neglecting the lateral jerk constraints during lane changes. This study uses seventh-degree polynomials for lane change trajectory planning by considering the vehicle lateral jerk constraints. Simulation results show that the utilization of the seventh-degree method results in a 41% reduction in jerk compared to the fifth-degree polynomial. Furthermore, this study also proposes lane change trajectory schemes that can cater to different driving styles (e.g., safety, efficiency, comfort, and balanced performance). Depending on the driving style, the planned lane change trajectory ensures that the vehicle achieves optimal performance in one or more aspects during the lane change process. For example, with the trajectory that
Lai, FeiHuang, Chaoqun
SBW(Steer-by-wire) is a steering system that transmits the driver’s request and gives feedback to the driver through electrical signals. This system eliminates the mechanical connection of the traditional steering system, and can realize the decoupling of the steering wheel and the road wheel. In addition, this system has a perfect torque feedback system, which can accurately and delicately feedback the road surface information to the driver. However, vehicle driving deviation is one of the most common failure modes affecting vehicle performance in the automotive aftermarket, this failure mode can exacerbates tire wear, reducing their life cycle, at the same time, the driver must apply a counter torque to the steering wheel for a long time to maintain straight-line travel during driving. This increases the driver’s operational burden and poses safety hazards to the vehicle’s operation. Based on the steer-by-wire system and vehicle driving deviation characteristics, this paper proposes
Xiangfei, XuQu, Yuan
Path planning in parking scenarios for vehicles with Ackermann steering characteristics is a well studied problem in the literature. However, the recent emergence of four-wheel steering (4WS) chassis has brought new opportunities to the field of motion planning. Compared with front-wheel steering (2WS), 4WS vehicles offer higher flexibility and new maneuver modes such as CrabWalk. To utilize such new potential to further improve parking efficiency, this paper proposes a four-wheel steering oriented planning algorithm for parking scenarios. First, Hybrid A*-4WS is proposed to search for a coarse trajectory from the starting pose to the parking slot, with improved node expansion mechanism to incorporate four-wheel steering characteristics. Then a nonlinear programming (NLP) problem is formulated with four-wheel steering kinematic model to fully utilize the maneuver capability of 4WS vehicles, with OBCA used for collision avoidance constraints. Finally, the two algorithms are sequentially
Song, YufeiLiu, YuanzhiXiong, LuTang, Chen
Learning-based motion planning methods such as reinforcement learning (RL) have shown great potential of improving the performance of autonomous driving. However, comprehensively ensuring safety and efficiency remain a challenge for motion planning technology. Most current RL methods output discrete behavioral action or continuous control action, which lack an intuitive representation of the future motion and then face the problems with unstable or reckless driving behavior. To address these issues, this work proposes an interaction-aware reinforcement learning approach based on hybrid parameterized action space for autonomous driving in lane change scenario. The proposed method can output high-level feasible trajectory and low-level actuator control command to control the vehicle’s motion together. Meanwhile, the reward functions for the local traffic environment are designed to evaluate the effect of the interaction between ego vehicle and surrounding vehicles. The contributions of
Li, ZhuorenJin, GuizheYu, RanLeng, BoXiong, Lu
The application trend of automated driving is gaining significant concern, making it increasingly crucial to validate automated driving within the stochastic simulated traffic flow environment from both time and cost perspectives. The stochastic traffic flow model attempts to encapsulate the variability inherent in traffic conditions through a stochastic process. This approach is particularly important as it accounts for the unpredictable nature of traffic, which is often not fully captured by traditional deterministic testing scenarios. However, while stochastic traffic flow models have made strides in simulating the behavior of traffic participants, there remains a significant oversight in the simulation of vehicles’ driving trajectories, leading to unrealistic portrayals of their behaviors. The trajectories of vehicles are a critical component in the overall behavior of traffic flow, and their accurate representation is essential for the simulation to reflect real-world driving
Gao, YiboCao, PengYang, Aixi
This paper proposes a path-tracking and direct yaw moment integrated control strategy based on linear matrix inequality (LMI) and terminal sliding mode for autonomous distributed drive electric vehicles (A-DDEVs) equipped with a steer-by-wire (SBW) system. This strategy effectively attenuates the effects of external disturbances and parameter uncertainties on path tracking, thereby enhancing vehicle safety. The control-oriented vehicle model accounts for roll effects, with the system state matrix incorporating mismatched norm bounded uncertainties. Firstly, for overall vehicle motion control, an LMI-based integral sliding mode controller (ISMC) is designed to generate desired front wheel steering angle and additional yaw moment. This aims to converge path-tracking errors and ensure vehicle stability. A sufficient condition for the existence of a sliding surface ensuring asymptotic stability of the sliding mode dynamics is provided, along with a demonstration of the attainability of the
Li, DanyangZhao, YouqunLin, FenZhang, ChenxiYu, Song
There is evidence to suggest that males and females respond differently in motor vehicle collisions, making it important to study how both sexes respond to vehicle safety systems. The THOR 5th-percentile female (THOR-05F) anthropomorphic test device (ATD) was developed to represent a small female occupant better than the Hybrid III 5th-percentile female (HIII-05F) ATD. However, there are few studies in which they have been directly compared. Therefore, the objective of this study was to compare the responses of the two ATDs in matched frontal sled tests simulating a realistic driver seat environment. A 7th-generation Toyota Camry driver seat test buck was used with Camry parts (i.e., 3-point belt, modified seat, steering wheel, airbag, and column). The belt was equipped with a 4-kN load limiter and pretensioner. Rigid foam (65 psi) was used to represent the knee bolster. Thirteen tests were conducted using speeds of 30 and 56 kph. Chest bands were used to measure external chest
Boyle, David M.Albert, Devon L.Hardy, Warren N.Kemper, Andrew R.
Turning circle diameter (TCD) of vehicle is critical parameter which is used to determine the turning capability of vehicle. TCD is the smallest circular turn that a vehicle can make of given drive track. The TCD depends on vehicle wheel lock angles, wheelbase, and geometric architecture of vehicle. The Regulation certification requirement of steering system, states that the maximum TCD should be less than 24m & TCCD (Turning clearance circle diameter) 25m (M&N category Vehicle). IS 12222:2011 & UN R79 are regulation related to Steering system. This invention relates to measuring the TCD of vehicle. The conclusion of this technical paper proposes new innovative method to overcomes and address the below limitations. It provides accurate and precise results by adjusting room for error. It eliminates the approximation ambiguity. It reduces the manual intervention of human effort to carry out the entire measurement process. It improves the safety of measurement technician, since it
Yadav, SatyendraOjha, VijayChatterjee, AnupamSaikrishna, VNLKarthik, V
The structural integrity of the steering wheel is important for vehicle operations. It is subjected to various load conditions during the vehicle motion. It thus becomes important to understand various aspects of the same which include stiffness, natural frequency, and regulatory requirements i.e. body block test, head form impact test, etc. Simulation plays an important role in understanding the structural integrity and validation requirements of products at the design stage itself. This paper discusses the modeling and simulation of the steering wheel at both the armature level and the complete steering wheel level. As armature is critical from a structural strength and stiffness point of view, certain simulations like modal analysis are performed first at the armature level, and design iterations were done to achieve the natural frequency target. The list of simulations performed includes modal analysis, bending rigidity, static compression, bending stiffness, body block test and
Rathore, Gopal SinghKumar, AnkitChauhan, Adesh KumarDas, A.P.Sahu, Hemanta Kumar
Investigation of clunking noise in a steering system fitted into a test vehicle indicated radial lash in sliding bushings, which are press fitted into housings, as one of the possible causes for clunk. To study the behavior of sliding bush under the influence of assembly clamp loads, manufacturing tolerances and road loads, sliding bushings are modelled in more detail in the steering system finite element models. Further for correlating the bushing measurements from test vehicle to the finite element model, a radial lash output is derived which is not directly available in finite element software. Finally, the correlated model is used to assess the updated design and check for radial lash improvement
Badduri, JaideepPandey, Ashish
Hypersonic platforms provide a challenge for flight test campaigns due to the application's flight profiles and environments. The hypersonic environment is generally classified as any speed above Mach 5, although there are finer distinctions, such as “high hypersonic” (between Mach 10 to 25) and “reentry” (above Mach 25). Hypersonic speeds are accompanied, in general, by a small shock standoff distance. As the Mach number increases, the entropy layer of the air around the platform changes rapidly, and there are accompanying vortical flows. Also, a significant amount of aerodynamic heating causes the air around the platform to disassociate and ionize. From a flight test perspective, this matters because the plasma and the ionization interfere with the radio frequency (RF) channels. This interference reduces the telemetry links' reliability and backup techniques must be employed to guarantee the reception of acquired data. Additionally, the flight test instrumentation (FTI) package needs
Geometric methods based on Reeds–Shepp (RS) curves offer a practical approach for the parking path planning of unmanned mining truck, but discontinuous curvature can cause tire wear and road damage. To address this issue in mine scenario, a continuous curvature parking path planning method based on transition curve and model predictive control (MPC) is proposed for mine scenarios. Initially, according to the shovel position information issued by the cloud dispatching platform, a reference line is planned using RS curves. In order to mitigate the wear and tear of the tires and the damage to unstructured roads due to the in situ steering caused by the sudden change of the curvature, a transition curve consisting of clothoid–arc–clothoid that satisfies the kinematics of continuous vehicle steering is designed on the basis of RS curves to achieve the continuity of road curvature, which will contribute to the economy of tire and handling performance. The calculation of Fresnel integral
Zhang, HaosenChen, QiushiWu, Guangqiang
ABSTRACT This paper presents two techniques for autonomous convoy operations, one based on the Ranger localization system and the other a path planning technique within the Robotic Technology Kernel called Vaquerito. The first solution, Ranger, is a high-precision localization system developed by Southwest Research Institute® (SwRI®) that uses an inexpensive downward-facing camera and a simple lighting and electronics package. It is easily integrated onto vehicle platforms of almost any size, making it ideal for heterogeneous convoys. The second solution, Vaquerito, is a human-centered path planning technique that takes a hand-drawn map of a route and matches it to the perceived environment in real time to follow a route known to the operator, but not to the vehicle. Citation: N. Alton, M. Bries, J. Hernandez, “Autonomous Convoy Operations in the Robotic Technology Kernel (RTK)”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI
Alton, NicholasBries, MatthewHernandez, Joseph
ABSTRACT In the field of ground robotics, the problems of global path planning and local obstacle avoidance are often treated separately but both are assessed in terms of a cost related to navigating through a given environment. Traversal cost is typically defined in terms of the required fuel [1], required travel time [2], and imparted mechanical wear [3] to guide route selection. Prior work [4] has shown that obstacle field complexity and navigation cost can be abstracted into quantitative dimensionless parameters. But determining the cost parameters and their relationship to field complexity requires running repeated path planning simulations [4]. This work presents a method for estimating navigation cost solely from geometric obstacle field complexity measures, namely the statistical properties of an obstacle’s shape and the density of obstacles within an environment, eliminating the requirement to run a path planner in a simulation environment. Citation: S. J. Harnett, S. Brennan
Harnett, Stephen J.Brennan, SeanReichard, KarlPentzer, JesseTau, SethGorsich, David
ABSTRACT Autonomous ground vehicles have the potential to reduce the risk to Soldiers in unfamiliar, unstructured environments. Unmanned operations in unstructured environments require the ability to guide the vehicles from their starting position to a target position. This paper proposes a framework to plan paths across such unstructured environments using a priori information about the environment as cost criteria into a multi-criteria, multi-agent path planner. The proposed multi-criteria, multi-agent path planner uses a penalty-based A* algorithm to plan multiple paths across the unstructured environment and uses entropy weighting for generating weights to calculate a multi-criteria cost with distance, risk, and soil trafficability. The paths generated by the proposed framework provide a better overall performance across the cost criteria and can be used as waypoints to navigate UGVs in off-road environments. Citation: S. Khatiwada, P. Murray-Tuite, M.J. Schmid, “Multi-Criteria
Khatiwada, SachetMurray-Tuite, PamelaSchmid, Matthias J
ABSTRACT Path planning is critical for mission implementation in various robot platforms and autonomous combat vehicles. With the efforts of electrification, battery energy storage as power sources is an ideal solution for robots and autonomous combat vehicles to improve capability and survivability. However, the battery’s limited energy and limited instantaneous power capability could become limiting factors for a mission. The energy and power constraints are also affected by the environment, battery state of health (SOH), and state of charge (SOC) significantly; in the worst case, a well-tested mission profile could fail in the real world if all aspects of the battery are not considered. This paper presents a framework to model the battery’s capability to support a whole mission and specific tasks under various environments. This real-time battery model can be built into an intelligent battery management system to support system-level mission planning, real-time task selection
Nan, XiDong-O’Brien, JingYan, LiangLi, PengHundich, AlexSkalny, David
ABSTRACT Self-driving or autonomous vehicles consist of software and hardware subsystems that perform tasks like sensing, perception, path-planning, vehicle control, and actuation. An error in one of these subsystems may manifest itself in any subsystem to which it is connected. Errors in sensor data propagate through the entire software pipeline from perception to path planning to vehicle control. However, while a small number of previous studies have focused on the propagation of errors in pose estimation or image processing, there has been little prior work on systematic evaluation of the propagation of errors through the entire autonomous architecture. In this work, we present a simulation study of error propagation through an autonomous system and work toward developing appropriate metrics for quantifying the error at both the subsystem and system levels. Finally, we demonstrate how the framework for analyzing error propagation can be applied to analysis of an autonomous systems
Carruth, Daniel W.Goodin, ChristopherDabbiru, LalithaScherer, NicklausJayakumar, Paramsothy
ABSTRACT Multi-robot bounding overwatch requires timely coordination of robot team members. Symbolic motion planning (SMP) can provide provably correct solutions for robot motion planning with high-level temporal logic task requirements. This paper aims to develop a framework for safe and reliable SMP of multi-robot systems (MRS) to satisfy complex bounding overwatch tasks constrained by temporal logics. A decentralized SMP framework is first presented, which guarantees both correctness and parallel execution of the complex bounding overwatch tasks by the MRS. A computational trust model is then constructed by referring to the traversability and line of sight of robots in the terrain. The trust model predicts the trustworthiness of each robot team’s potential behavior in executing a task plan. The most trustworthy task and motion plan is explored with a Dijkstra searching strategy to guarantee the reliability of MRS bounding overwatch. A robot simulation is implemented in ROS Gazebo to
Zheng, HuanfeiSmereka, Jonathon M.Mikulski, DariuszRoth, StephanieWang, Yue
ABSTRACT This work investigates the effects of obstacle uncertainty on the speed, distance, and feasibility of a planned traversal path. Simulation results for artificial and real-world environments are used to numerically quantify how geometric uncertainty within a map affects path traversal cost. A significant outcome of this research is the discovery of a relationship between increasing uncertainty and path cost. As obstacle uncertainty increases, previously planned routes can become infeasible as they effectively become blocked off due to uncertainty in the obstacle geometry. This paper illustrates a method that can serve to increase the speed, simplicity, and reliability of path planning, while allowing uncertainty to be included in the mobility analysis. Citation: S. Tau, S. Brennan, K. Reichard, J. Pentzer, D. Gorsich, “The Effects of Obstacle Dimensional Uncertainty on Path Planning in Cluttered Environments”, In Proceedings of the Ground Vehicle Systems Engineering and
Tau, SethBrennan, SeanReichard, KarlPentzer, JesseGorsich, David
ABSTRACT Motion planning algorithms for vehicles in an offroad environment have to contend with the significant vertical motion induced by the uneven terrain. Besides the obvious problems related to driver comfort, for autonomous vehicles, such “bumpy” vertical motion can induce significant mechanical noise in the real time data acquired from onboard sensors such as cameras to the point that perception becomes especially challenging. This paper advances a framework to address the problem of vertical motion in offroad autonomous motion control for vehicular systems. This framework is first developed to demonstrate the stabilization of the sprung mass in a modified quarter-car tracking a desired velocity while traversing a terrain with changing height. Even for an idealized model such as the quarter-car the dynamics turn out to be nonlinear and a model-based controller is not obvious. We therefore formulate this control problem as a Markov decision process and solve it using deep
Salvi, AmeyaBuzhardt, JakeTallapragada, PhanindraKrovi, VenkatBrudnak, MarkSmereka, Jonathon M.
ABSTRACT Accurate terrain mapping is of paramount importance for motion planning and safe navigation in unstructured terrain. LIDAR sensors provide a modality, in the form of a 3D point cloud, that can be used to estimate the elevation map of the surrounding environment. But, working with the 3D point cloud data turns out to be challenging. This is primarily due to the unstructured nature of the point clouds, relative sparsity of the data points, occlusions due to negative slopes and obstacles, and the high computational burden of traditional point cloud algorithms. We tackle these problems with the help of a learning-based, efficient data processing approach for vehicle-centric terrain reconstruction using a 3D LIDAR. The 3D LIDAR point cloud is projected on the ground plane, which is processed by a generative adversarial network (GAN) architecture in the form of an image to fill in the missing parts of the terrain heightmap. We train the GAN model on artificially generated datasets
Sutavani, SarangZheng, AndrewJoglekar, AjinkyaSmereka, JonathonGorsich, DavidKrovi, VenkatVaidya, Umesh
ABSTRACT Autonomous vehicles rely on path planning to guide them towards their destination. These paths are susceptible to interruption by impassable hazards detected at the local scale via on-board sensors, and malicious disruption. We define robustness as an additional parameter which can be incorporated into multi-objective optimization functions for path planning. The robustness at any point is the output of a function of the isochrone map at that point for a set travel time. The function calculates the sum of the difference in area between the isochrone map and the isochrone map with an impassable semi-circle hazard inserted in each of the four cardinal directions. We calculate and compare two different Pareto paths which use robustness as an input parameter with different weights. Citation: T. Jonsson Damgaard, M. Rittri, P. Franz, A. Halota “Robust Path Planning in the Battlefield,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA
Damgaard, Thomas JonssonRittri, MikaelFranz, PatrickHalota, Anika
ABSTRACT In order to introduce longer heavy vehicles with multiple articulation joints between vehicle units into the UK and other European countries, rear steering of the trailing vehicle units is required to allow for sufficient manoeuvrability. An extensive program of research has been undertaken into trailer steering technologies in recent years. Such systems can enable significantly longer heavy vehicles to negotiate narrow roads. It is thought that this same technology could be used in military supply operations. Possible benefits of using multiple-trailer ‘long combination’ vehicles in military supply include: (i) Fewer vehicles are needed to perform the same supply tasks. This means fewer drivers and consequently less exposure to threats, as well as improved productivity of each driver and vehicle. (ii) Longer vehicles can have 20% to 30% lower fuel consumption for a given freight task than conventional vehicles, depending on the configuration. Application of controlled
Cebon, DavidRoebuck, RichardOdhams, Andrew
ABSTRACT All-Terrain off-road environments are the next frontier for autonomous vehicles to overcome. However, there are many obstacles in the way of this goal. Artificial intelligence has proven to be an invaluable asset in developing perception and path planning systems capable of overcoming these obstacles, but these AI systems fundamentally rely on the availability of data related to the operational environment in order to succeed. Currently, there is no unifying ontology for this data. This has inhibited progress on training AI by reducing the availability of cross-integrable datasets. We present ATLAS: A labeling ontology composed of over 200 labels specifically designed to encompass all-terrain off-road environments. This ontology will lay the ground work for creating scalable standardized all terrain off-road data and will enable future AI by providing an expansive and well labeled ontology that can push the field of autonomous vehicles to new heights. Citation: W. Smith, D
Smith, WestonGrabowsky, DavidMikulski, Dariusz
ABSTRACT Future autonomous combat vehicles will need to travel off-road through poorly mapped environments. Three-dimensional topography may be known only to a limited extent (e.g. coarse height), but this will likely be noisy and of limited resolution. For ground vehicles, 3D topography will impact how far ahead the vehicle can “see”. Higher vantage points and clear views provide much more useful path planning data than lower vantage points and occluded views from trees and structures. The challenge is incorporating this knowledge into a path planning solution. When should the robot climb higher to get a better view or else continue moving along the shortest path predicted by current information? We investigated the use of Deep Q-Networks (DQN) to reason over this decision space, comparing performance to conventional methods. In the presence of significant sensor noise, the DQN was more successful in finding a path to the target than A* for all but one type of terrain. Citation: E
Martinson, EricPurman, BenDallas, Andy
ABSTRACT Robot path-planning is a central task for navigation and most path-planners perform well in mapped environments with explicit obstacle boundaries. However, many obstacle fields are better defined by the probability of obstacles and obstacle geometries rather than by explicit locations. Few tools and data structures exist, other than repeated simulations, to predict robot mobility in these situations. Previously, it was shown that geometric obstacle properties could be used to estimate properties of paths routing around these obstacles, looking only at maps and avoiding the task of path planning [1]. This required knowing obstacle geometries relative to travel direction. This work presents a method for representing obstacle geometry, at arbitrary orientations and positions, and therefore a probabilistic model for determining if space near an obstacle is occupied. This paper explains the theory behind this method, uses this method to calculate the portion of a straight path
Harnett, Stephen J.Brennan, SeanReichard, KarlPentzer, Jesse
ABSTRACT Route planning plays an integral role in mission planning for ground vehicle operations in urban areas. Determining the optimum path through an urban area is a well understood problem for traditional ground vehicles; however, in the case of autonomous unmanned ground vehicles (UGVs), additional factors must be considered. For a UGV, perception, rather than mobility, will be the limiting factor in determining operational areas. Current ground vehicle route planning techniques do not take perception concerns into account, and these techniques are not suited for route planning for UGVs. For this study, perception was incorporated into the route planning process by including expected sensor accuracy for GPS, LIDAR, and inertial sensors into the path planning algorithm. The path planner also accounts for additional factors related to UGV performance capabilities that affect autonomous navigation
Durst, Phillip J.Goodin, ChristopherSong, PeilinDu, Thien K.
ABSTRACT This paper presents a novel adaptive sampling method using intelligent UAVs in battlefields to help soldiers with awareness of environments. A UAV can perform as a robotic wingman in soldier formations, compensating for that cannot be scouted by soldiers, even being exposed to enemy fire. With portable size, the UAV is easily carried and flown for scouting tasks anytime. The flexibility of UAVs makes it possible to collect measurements sequentially. Each measurement is adaptively designed and determined from the Bayesian perspective to increase the fidelity of battlefields. Wavelet structure is considered to optimize measurement projections to substantially reduce the number of measurements based on compressive sensing framework. More specifically, each measurement is optimized by maximizing the posterior variance inferred from existing informative data. A motion planning algorithm for UAVs is designed based on the distribution of optimal measurements, striking a balance
Huang, ShuoLu, JianXie, LinTan, Jindong
ABSTRACT This research proposes a human-multirobot system with semi-autonomous ground robots and UAV view for contaminant localization tasks. A novel Augmented Reality based operator interface has been developed. The interface uses an over-watch camera view of the robotic environment and allows the operator to direct each robot individually or in groups. It uses an A* path planning algorithm to ensure obstacles are avoided and frees the operator for higher-level tasks. It also displays sensor information from each individual robot directly on the robot in the video view. In addition, a combined sensor view can also be displayed which helps the user pin point source information. The sensors on each robot monitor the contaminant levels and a virtual display of the levels is given to the user and allows him to direct the multiple ground robots towards the hidden target. This paper reviews the user interface and describes several initial usability tests that were performed. This research
Lee, SamLucas, Nathan P.Cao, AlexPandya, AbhilashEllis, R. Darin
Navigating Unmanned Aerial Vehicles (UAVs) in urban airspace poses significant challenges for fast and efficient path planning due to the environment's complexity and dynamism. However, the existing research on UAV path planning has ignored the speed of algorithmic convergence and the smoothness of the generated path, which are critical for adapting to the dynamic changing of the urban airspace as well as for the safety of ground personnel, and the UAV itself. In this study, we propose an enhanced Ant Colony Optimization (ACO) algorithm that incorporates two heuristic functions: the compass heuristic and the inertia heuristic. These functions guide the ant agents in their movement towards the destination, aiming for faster convergence and smoother trajectories. The algorithm is evaluated using a gray-scale lattice map generated from ground personnel risk data in Suzhou City. The results indicate that the improved ACO path planning algorithm demonstrates both efficiency and quality
Wang, BofanZhao, ZhouyeHu, BoyaLiu, YufanRu, XiaoyuTong, ZiyueJia, Qing
Internet of vehicles (IoV) system as a typical application scenario of smart city, trajectory planning is one of the key technologies of the system. However, there are some unstructured spaces such as road shoulders and slopes pose challenges for trajectory planning of connected-automated vehicle (CAV). Therefore, this paper addresses the problem of CAV trajectory planning affected by unstructured space. Firstly, based on cyber-physical system (CPS), the cyber-physical trajectory planning system (CPTPS) framework was built. A high-precision digital twin CAV is established based on the physical properties and geometric constraints of CAV, and the digital model is mapped to cyber space of the CPTPS. In order to further reduce the energy consumption of the CAV during driving and the time spent from the start to the end, a model was established. Further, based on the sand cat swarm hybrid particle swarm optimization algorithm (SCSHPSO), global path planning for connected-automated vehicles
Ma, ShiziMa, ZhitaoShi, YingYang, ZhongkaiLai, DaoyinQi, Zhiguo
Advances in vehicle sensing and communication technologies are enabling new opportunities for intelligent driver assistance systems that enhance road safety and performance. This paper provides a comprehensive review of recent research on two complementary areas: haptic/tactile interfaces for conveying road terrain and hazard information to drivers, and shared control frameworks that employ assistive automation to supplement driver inputs. Various haptic feedback techniques for generating realistic road feel through steering wheel torque overlays, pedal interventions, and alternative interface modalities are examined. Control assistance approaches integrating environmental perception to provide steering, braking, and collision avoidance support through blended human–machine control are also analyzed. The paper scrutinizes methods for road sensing using cameras, LiDAR, and radar to classify terrain for adapting system response. Evaluation practices across this domain are critically
Shata, Abdelrahman Ali AdelNaghdy, FazelDu, Haiping
A power steering system helps the heavy-duty operator move the vehicle easily with the hydraulic pump that provides the fluid pressure and facilitating adequate operation. Some failures in the power steering system are due to external and internal factors that can reduce its service life. The external factors could be identified by ocular inspection but normally, due to internal failures, it is necessary to use a hydraulic pressure flow meter. However, this device makes it impossible to detect failures caused by the selected lubricant. This work aims to investigate the causes of power steering system seizure by using the tribological wear examination process and the lubricant characterization under some actual operation conditions. The lubricant characterization was carried out in a four balls tester using fresh and used samples of a re-refined oil based ATF, SAE 15 W40 and synthetic SAE 5 W30 oils at two temperatures. In general, the results showed an unsteady friction profile with
García-Maldonado, MiguelGallardo, EzequielMozqueda-Flores, LuisVite-torres, Manuel
Autonomous vehicle technologies have become increasingly popular over the last few years. One of their most important application is autonomous shuttle buses that could radically change public transport systems. In order to enhance the availability of shuttle service, this article outlines a series of interconnected challenges and innovative solutions to optimize the operation of autonomous shuttles based on the experience within the Shuttle Modellregion Oberfranken (SMO) project. The shuttle shall be able to work in every weather condition, including the robustness of the perception algorithm. Besides, the shuttle shall react to environmental changes, interact with other traffic participants, and ensure comfortable travel for passengers and awareness of VRUs. These challenging situations shall be solved alone or with a teleoperator’s help. Our analysis considers the basic sense–plan–act architecture for autonomous driving. Critical components like object detection, pedestrian tracking
Dehghani, AliSalaar, HamzaSrinivasan, Shanmuga PriyaZhou, LixianArbeiter, GeorgLindner, AlisaPatino-Studencki, Lucila
In recent years, autonomous vehicles (AVs) have been receiving increasing attention from investors, automakers, and academia due to the envisioned potentials of AVs in enhancing safety, reducing emissions, and improving comfort. The crucial task in AV development boils down to perception and navigation. The research is underway, in both academia and industry, to improve AV’s perception and navigation and reduce the underlying computation and costs. This article proposes a model predictive control (MPC)-based local path-planning method in the Cartesian framework to overcome the long computation time and lack of smoothness of the Frenet method. A new equation is proposed in the MPC cost function to improve the safety in path planning. In this regard, an AV is built based on a 2015 Nissan Leaf S by modifying the drive-by-wire function and installing environment perception sensors and computation units. The custom-made AV then collected data in Norman, Oklahoma, and assisted in the
Arjmandzadeh, ZibaAbbasi, Mohammad HosseinWang, HanchenZhang, JiangfengXu , Bin
Resupply missions are critical logistical parts of modern warfare. Supply vehicles carrying fuel and ammunition are high-value targets meaning that the route chosen to approach such a mission is sensitive to risk and a critical time of delivery. We address the problem of a supply vehicle that needs to find a secure path to link up with a mobile frontline unit that has a fixed known itinerary. This paper presents a resupply path planning algorithm, the Adaptive Intercepting Path Planning (AIPP) algorithm, that balances risk and travel time to find the most suitable rendezvous point among several. The algorithm generates the least risky route that meets the rendezvous deadline
Damgaard, Thomas JonssonRittri, MikaelFranz, Patrick
A critical first step for a robot navigating an obstacle field is to plan a collision-free path through the environment. Historically, solutions for path planning largely use grid-based search methods particularly when guarantees are required that do not permit randomization-based methods. In large operational domains, gridding the search environment necessitates significant memory overhead and corresponding performance loss. To avoid gridded maps, grid-free path planners can achieve significant benefits to performance and memory overhead. These methods utilize visibility graphs with edge costs rather than grids with cell weights to represent possible path choices. This work presents methods to extend known 2D grid-free static environment path planners into higher dimensions to use these same planners for dynamic obstacle path planning via timespace representations. Such extensions to include time trajectories into the visibility graph readily admit path planning through highly dynamic
Harnett, Stephen J.Brennan, SeanPangborn, Herschel C.Pentzer, JesseReichard, Karl
In order to meet the driving characteristics and needs of different types of drivers and to improve driving comfort and safety, this article designs personalized variable transmission ratio schemes based on the classification results of drivers’ steering characteristics and proposes a switching strategy for selecting variable transmission ratio schemes in response to changes in driver types. First, data collected from driving simulator experiments are used to classify drivers into three categories using the fuzzy C-means clustering algorithm, and the steering characteristics of each category are analyzed. Subsequently, based on the steering characteristics of each type of driver, suitable speed ranges, steering wheel travel, and yaw rate gain values are selected to design the variable transmission ratio, forming personalized variable transmission ratio schemes. Then, a switching strategy for variable transmission ratio schemes is designed, using a support vector machine to build a
Chen, ChenZheng, HongyuZong, Changfu
Model predictive control (MPC) plays a crucial role in advancing intelligent vehicle technologies. Controllers designed based on various vehicle reference models, including kinematic and dynamic models (both linear and nonlinear), often demonstrate significant differences in control performance. This study contributes by comparing three different MPC control methods and proposing a comprehensive evaluation criterion that considers tracking accuracy, stability, and computational efficiency across various MPC designs. Joint simulations using CarSim and MATLAB/Simulink reveal distinct performance characteristics among the MPC variants. Specifically, kinematic MPC (KMPC) exhibits superior performance at low speeds, linear model predictive control (LMPC) performs best at moderate speeds, and nonlinear MPC (NMPC) achieves optimal performance at high speeds. These findings highlight the adaptive nature of MPC strategies to varying vehicle dynamics and operational conditions, emphasizing the
Lai, FeiXiao, HaoLiu, JunboHuang, Chaoqun
ZF rethinks safety with new airbags, belt tensioner. ZF knows that the steering wheel remains one of the most relevant components in an automotive interior, because this is where drivers have direct contact to the vehicle. As steering wheels become adorned with more functions than some drivers know what to do with, ZF put Marc Schledorn in charge of the teams rethinking how the driver airbag could operate in a world with ever-busier steering wheels. The solution is a new type of steering wheel airbag that ZF Lifetec (ZF's renamed Passive Safety Systems division) announced in June. Instead of moving through a thermoplastic airbag cover mechanically fixed in the center of the wheel, Schledorn told SAE Media, the new design positions the airbag on the top side of the steering wheel and then expands through the upper rim of the wheel when needed
Blanco, Sebastian
This research aims at understanding how the driver interacts with the steering wheel, in order to detect driving strategies. Such driving strategies will allow in the future to derive accurate holistic driver models for enhancing both safety and comfort of vehicles. The use of an original instrumented steering wheel (ISW) allows to measure at each hand, three forces, three moments, and the grip force. Experiments have been performed with 10 nonprofessional drivers in a high-end dynamic driving simulator. Three aspects of driving strategy were analyzed, namely the amplitudes of the forces and moments applied to the steering wheel, the correlations among the different signals of forces and moments, and the order of activation of the forces and moments. The results obtained on a road test have been compared with the ones coming from a driving simulator, with satisfactory results. Two different strategies for actuating the steering wheel have been identified. In the first strategy, the
Previati, GiorgioMastinu, GianpieroGobbi, Massimiliano
Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time
Wei, ShiboGu, YuLiu, Han
In the course of the U-Shift project, an automated, driverless and electrically driven vehicle concept is developed. By separating the vehicle into a drive module and a transport capsule, a novel form of mobility is created. The autonomous driving module, the so-called Driveboard, is able to change the transport capsules independently and thus serves both passenger and goods transport. In order to be able to use the vehicle effectively, especially in urban areas, the space required for manoeuvring and loading or unloading the capsules must be kept as small as possible. This poses special challenges for the steering system. In this paper, a novel steering system is presented that enables both same-direction and opposite-direction wheel steering. First, the fundamental concept of the steering system is presented. After that, the design is explained and the assembled steering system is shown. During normal cornering, there is a mechanical coupling between the wheels. Which means that the
Weitz, FabianGauterin, FrankFrey, MichaelOstendorff, Niklas
The automotive industry is continuously evolving, demanding innovative approaches to enhance testing methodologies and preventively identify potential issues. This paper proposes an advanced test approach in the area of the overall vehicle system including the steering system and powertrain on a Road to Rig test bench. The research aims to revolutionize the conventional testing process by identifying faults at an early stage and eliminating the need to rely solely on field tests. The motivation behind this research is to optimize the test bench setup and bring it even closer to real field tests. Key highlights of the publication include the introduction of an expanded load spectrum, incorporating both steering angle and speed parameters along the test track. The load includes different route and driving profiles like on a freeway, overland and city drive in combination with the steering angles. Furthermore, for the first instance, specific driving manoeuvres, including slalom driving
Kopp, LennartHarfmann, PatrickNiederberger, LucasSchwämmle, TimmKley, Markus
Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving styles based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including the ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel
Ji, DejieFlormann, MaximilianWarnecke, Joana M.Henze, RomanDeserno, Thomas M.
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