Browse Topic: Trajectory control
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is
In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes’ intersections, and it shows improved route performance in vehicle testing. The proposed model is validated in a Comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test results
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents a novel approach to curvature prediction using deep neural networks trained on GPS-derived ground truth data, rather than model predictions, providing a more accurate training signal that reflects actual vehicle motion. We develop a multi-modal neural network architecture with temporal GRU encoders that processes vision features, driver intent signals, historical curvature, and vehicle state parameters to predict curvature. A key innovation is the use of GPS-based actual curvature measurements computed from vehicle motion data (κ = ωz/v) as training supervision, enabling the model to learn from real-world driving patterns. The model is trained on 5,322 samples from real-world driving data collected on The University of Oklahoma’s Norman Campus using a Comma 3X device and a 2025 Nissan Leaf electric vehicle. Experimental results
This paper presents the design and implementation of a Semi-Autonomous Light Commercial Vehicle (LCV) capable of following a person while performing obstacle avoidance in urban and controlled environments. The LCV leverages its onboard 360-degree view camera, RTK-GNSS, Ultrasonic sensors, and algorithms to independently navigate the environment, avoiding obstacles and maintaining a safe distance from the person it is following. The path planning algorithm described here generates a secondary lateral path originating from the primary driving path to navigate around static obstacles. A Behavior Planner is utilized to decide when to generate the path and avoid obstacles. The primary objective is to ensure safe navigation in environments where static obstacles are prevalent. The LCV's path tracking is achieved using a combination of Pure Pursuit and Proportional-Integral (PI) controllers. The Pure Pursuit controller is utilized as lateral control to follow the generated path, ensuring
Semi-trailer trains are the main force of highway freight. In a complex environment with multiple vehicles, accidents are easily caused by complex structures and driver operation problems. Intelligent technology is urgently needed to improve safety. In view of the shortcomings of existing research on its dedicated models and algorithms, this paper studies the intelligent decision-making and trajectory planning of semi-trailer trains under multiple vehicles. A local trajectory planning method based on global path planning and Frenet coordinate decoupling based on the improved A* algorithm is proposed. The smooth weight transition function and B-spline curve are introduced to optimize the global path. The polynomial function is combined with the acceleration rate to optimize the local trajectory. TruckSim, Prescan and Simulink are used to build a joint simulation platform for multi-condition verification. The simulation results show that the search efficiency of the improved A* algorithm
Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and
Complex vertical takeoff and landing configurations that transition between vertical and forward flight modes necessitate advanced flight control systems to substantially reduce pilot workload. Prior work demonstrated the Trajectory Control System, a flight control architecture that enables such Simplified Vehicle Operations. However, there may also be scenarios or applications that require more aggressive maneuvering with rates and attitudes that exceed the nominal envelope. This paper demonstrates a flight control architecture with a middle-loop that harmonizes the Trajectory Control System with a Tactical Maneuvering System that enables more aggressive maneuvering, with seamless in-flight transitions between the two. In both cases, the middle-loop is linked with an explicit model-following inner-loop control system. Flight test results for the Trajectory Control System and maneuver simulation results for the Tactical Maneuvering System are shown for a subscale tilt-wing
Helicopters' Vertical Take-Off and Landing (VTOL) capabilities are essential for maritime operations, especially for small-deck naval vessels. Unmanned Aerial Vehicles (UAVs) offer a cheaper, expendable, and efficient alternative for certain tasks, such as reducing pilot risk and lowering fuel consumption. While the procedures to approach and land on (moving) ships are standardized and bound to established operational limits in the case of crewed helicopters, UAVs lack such guidelines. This study investigates optimal rotary-wing UAV approach trajectories to a moving ship, for varying wind conditions and relative initial positions, and for different objectives. The goal is to provide preliminary guidelines for maritime UAV recovery operations, and a preliminary estimation of performance-based operational limits. The optimal trajectories are obtained using a global path-performance optimization framework based on Optimal Control Theory. The trajectories are compared to each other and to
This paper presents a distributed algorithm to track a desired target while fostering the emergence of a swarm formation and providing obstacle avoidance capability to deal with unknown scenarios. The proposed approach is based on the merge between a Flight Management System for global path planning and the definition of virtual forces through a custom Artificial Potential Field to prevent drones collisions between each other, with external objects and to provide cohesion of the swarm configuration. Each drone independently computes its global route and adjusts its path based on an optimal control action to minimize a potential energy function induced by its neighbors and obstacles. This approach results in a high cost-effective strategy to enhance UAVs autonomy level by managing a large group of drones, guaranteeing a low cost per unit thanks to the low computational effort and low-budget sensor suit while providing all the capabilities to accomplish the desired mission.
Items per page:
50
1 – 50 of 301