Trajectory tracking and obstacle avoidance in dynamic environments using an improved artificial potential field method

Long Di, Naiwei Huang, Jiaqi He, Xuxiang Wu, Hansheng Huang, Yongbin Su and Tundong Liu

PLOS ONE, 2025, vol. 20, issue 7, 1-33

Abstract: Ensuring that a robot employing demonstration learning models can simultaneously achieve accurate trajectory tracking of demonstrated paths and effective avoidance of moving obstacles in dynamic environments remains a critical research challenge. This paper proposes a real-time trajectory planning framework based on an enhanced artificial potential field (APF) approach to address this dual-objective problem. Specifically, the proposed method deploys a sequence of virtual target points along the demonstrated trajectory to guarantee both path-following precision and goal convergence for robotic systems. A dynamic obstacle repulsion model is developed by integrating velocity-coupled and acceleration-associated force components, enabling proactive obstacle motion anticipation and adaptive trajectory reconfiguration. Furthermore, a probabilistic obstacle motion prediction framework is established through motion pattern analysis to actively optimize the robot’s motion strategy and reduce tracking errors. Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. These quantitative improvements validate the framework’s superior robustness and safety performance in unstructured environments, with all evaluations systematically conducted in simulated settings.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0326879

DOI: 10.1371/journal.pone.0326879

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