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Author

Wenlai Cai

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Conference Aug 2026

Research on obstacle avoidance path planning based on improved artificial potential field method

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. · 1 citation
Conference Aug 2026

A survey on fused path planning for mobile robots: improved A* and timed-elastic band (TEB) algorithms

Path planning stands as a pivotal technology within the realm of autonomous mobile robots. In recent years, hierarchical architectures that integrate global and local planning have emerged as a prominent research paradigm. This paper focuses on the synergistic fusion of the improved A* global planner and the Timed Elastic Band (TEB) local planner, providing a systematic review of state-of-the-art research progress. Initially, the optimization strategies/methodologies and technical attributes of both the improved A* and TEB algorithms are elucidated. Subsequently, the discourse shifts to an in-depth analysis of the design philosophy, collaborative mechanisms, and representative application scenarios of the A*-TEB hybrid framework. Finally, a comprehensive synthesis is provided, followed by the identification of several frontier research directions from the perspective of algorithmic architectural evolution, including tightly coupled optimization modeling, uncertainty-driven adaptive fusion, semantic perception augmentation, and distributed expansion for multi-robot systems.

Licheng Li, Wenlai Cai, Renqiang Li · 0 citations

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