Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 55-60· 0 citations· 14 references
Abstract
Addressing the challenges of obstacle avoidance for autonomous vehicles in complex dynamic environments, traditional Artificial Potential Field (APF) methods often suffer from delayed responses to dynamic obstacles and generate paths that violate vehicle kinematic constraints. To overcome these limitations, this paper proposes an improved path planning algorithm, the Relative Velocity Potential Field (RVPF), which fuses relative velocity information with kinematic constraints. First, a relative velocity sensitivity factor is introduced to construct a dynamic potential field. By dynamically reshaping the repulsive field distribution based on the relative velocity vector, this approach endows the algorithm with a predictive capability regarding collision risks, facilitating a transition from passive reaction to active defense. Second, a vehicle kinematic model is established incorporating Ackermann steering geometry. A virtual tangential force strategy is employed to map the resultant potential forces into control variables that adhere to wheelbase and steering angle limits, thereby ensuring the generation of smooth and feasible trajectories. Simulation results demonstrate the superior performance of the proposed algorithm in scenarios involving high-speed oncoming traffic, overtaking, and lateral crossing. Notably, in the lateral crossing scenario—where traditional APF failed due to collisions—the RVPF algorithm achieved collision-free passage by actively decelerating and yielding, increasing the minimum safety distance to 5.02 m. These results confirm that the proposed algorithm significantly enhances the safety and stability of autonomous vehicles across diverse traffic situations.
Unmanned Ground Vehicle navigation remains a critical challenge in dynamic and unstructured environments. This paper proposes an Adaptive Beta-Weighted Force Field method for real-time obstacle avoidance, in which the repulsive force coefficient adapts continuously based on obstacle distance, velocity, and type classification. Unlike conventional fixedweight force field approaches, the proposed method modulates the repulsive gain proportionally to proximity and mobility of each detected obstacle, enabling the vehicle to respond conservatively at range while reacting at close encounters. Three configurations are evaluated through simulation: a fixed variedweight baseline, a fixed uniform-weight control, and the proposed adaptive-weight method, all tested under identical obstacle environments combining static and dynamic obstacles with an A-star global planner. Results demonstrate that the adaptive method achieves the fastest navigation completion while maintaining stable and smooth force behavior throughout the trajectory. The avoidance force variability is substantially reduced compared to both baselines, indicating smoother motion generation. Path efficiency remains comparable across all methods, confirming that adaptive weight modulation improves navigation speed and force stability without sacrificing safety or path quality. The adaptive method achieves a navigation time of 17.20 seconds, a mean avoidance force of 2.848 N, and a force standard deviation of 4.361 N, representing a 22% reduction in travel time and 74% reduction in force variability compared to the fixed-weight baseline, while maintaining a path following efficiency of 96.6%.
Muhammad Aqil Rayhan Majid, Mochammad Sahal, Ari Santoso· International Seminar on Int...· 0 citations
To overcome the limitations of the traditional artificial potential field method, including local minima, unreachable targets, path oscillations, and insufficient consideration of road structure information, this paper proposes an improved potential field algorithm for path planning on structured roads. Based on the conventional attractive and obstacle repulsive forces, a novel road boundary repulsive potential field is introduced to constrain the lateral driving range of the vehicle. A forward auxiliary force is introduced to break the force balance and escape from local minima. A combined force-limiting and dynamic position update mechanism is designed to suppress trajectory mutations. A scenario with a length of 100 m and a width of 4 m containing 5 dynamic obstacles is constructed for verification. The results show that the improved algorithm enables the vehicle to reach the target point without stopping or reversing in a continuous obstacle scenario, with a target reach-ability rate of over 99% and a terminal error of less than 0.3 meters. The maximum lateral deviation of the planned trajectory is less than 0.5 m, the average curvature is less than 0.08 m⁻¹, and no boundary crossing or collision occurs throughout the entire process. The generated trajectory is smooth and fully compatible with the vehicle's kinematic characteristics.
Wenlai Cai, Li-Cheng Li, Ren-Qiang Li et al.· International Conference on...· 1 citation
Collision avoidance for industrial AGVs operating in dynamic, shared environments remains challenging because reactive local planners treat moving obstacles as static, leading to conservative or unsafe behavior. We address this by enabling the local planner to reason over predicted obstacle motion rather than instantaneous positions. Specifically, an Ensemble Kalman Filter (EnKF)-based multi-object tracker provides filtered position and velocity estimates that a modified Dynamic Window Approach (DWA) uses to evaluate candidate trajectories against projected future obstacle states. A radius-informed ensemble generation scheme adapts filter uncertainty to the observed object size from 2D LiDAR, and a geometric-center representation provides stable bounding estimates under partial occlusion. The system is implemented as three modular, pipelined ROS 2 nodes. Experiments in Gazebo, Stage (up to five concurrent robots), and on a real industrial forklift demonstrate collision-free navigation in same-direction, crossing, and head-on scenarios with sub-10 ms controller response times.
Bruk Gebregziabher, Hadush Hailu· 2026 IEEE International Conf...· 0 citations
The Safe-Koopman Framework is introduced, an operator-theoretic motion planning method that generalizes obstacle-free demonstrations to planar planning tasks with obstacles and avoids collisions observed in the unconstrained Koopman baseline.
Xu-Chen Liu, Ruiqi Ke, Yandong Wang et al.· IEEE Transactions on Neural...· 0 citations
A node detection strategy grounded in the safe workspace effectively prevents collisions between the generated path and surrounding obstacles, and a two-stage heuristic search strategy is designed, incorporating an intermediate node mechanism to substantially enhance search efficiency.
Xin-Guang Li, Shilong Zhao, Xiao-Qi Guo· Proceedings of the Instituti...· 0 citations
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