基于预测人工势场的无人水面车辆路径规划
Path planning for unmanned surface vehicle based on predictive artificial potential field. International Journal of Advanced Robotic Systems
AI总结：
本文提出一种结合时间信息和预测势场的改进方法，用于提升无人水面车辆路径规划的效率和避障能力。
摘要：
高速无人水面车辆的路径规划需要更复杂的解决方案以减少航行时间并节省能量。本文提出了一种新的预测人工势场，结合时间信息和预测势场来规划更平滑的路径。它探讨了人工势场的基本原理，考虑了车辆动力学和局部最小值可达性。研究首先分析了最先进的传统人工势场及其在全局和局部路径规划中的缺点。然后介绍了三种对预测人工势场的改进：角度限制、速度调整和预测势场，以提高生成路径的可行性和平坦度。传统与预测人工势场的比较表明，后者成功限制了最大转向角，缩短了航行时间，并智能地避障。仿真结果进一步验证了预测人工势场解决了凹陷局部最小值问题，并在特殊场景中提高了可达性，最终生成了更高效的路径，减少了航行时间并为无人水面车辆节省了能量。
英文摘要：
Path planning for high-speed unmanned surface vehicles requires more complex solutions to reduce sailing time and save energy. This article proposes a new predictive artificial potential field that incorporates time information and predictive potential to plan smoother paths. It explores the principles of the artificial potential field, considering vehicle dynamics and local minimum reachability. The study first analyzes the most advanced traditional artificial potential field and its drawbacks in global and local path planning. It then introduces three modifications to the predictive artificial potential field-angle limit, velocity adjustment, and predictive potential to enhance the feasibility and flatness of the generated path. A comparison between the traditional and predictive artificial potential fields demonstrates that the latter successfully restricts the maximum turning angle, shortens sailing time, and intelligently avoids obstacles. Simulation results further verify that the predictive artificial potential field addresses the concave local minimum problem and improves reachability in special scenarios, ultimately generating a more efficient path that reduces sailing time and conserves energy for unmanned surface vehicles.
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