Trajectory tracking and obstacle avoidance in dynamic environments using an improved artificial potential field method.
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 f...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326879 |
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| Summary: | 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. |
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| ISSN: | 1932-6203 |