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.

翻译：高速无人水面艇的路径规划需要更复杂的解决方案以减少航行时间并节约能源。本文提出了一种融合时间信息与预测势场的新型预测人工势场法，用于规划更平滑的路径。该方法深入探讨了人工势场法的基本原理，同时考虑了艇体动力学特性与局部极小值可达性问题。研究首先分析了最先进的传统人工势场法及其在全局与局部路径规划中的局限性，随后针对预测人工势场法提出三项改进措施——角度限制、速度调节与预测势场，以提升生成路径的可行性与平顺性。通过传统方法与预测人工势场法的对比实验表明，后者能有效限制最大转向角、缩短航行时间并实现智能避障。仿真结果进一步验证了预测人工势场法能够解决凹形局部极小值问题，提升特殊场景下的可达性，最终为无人水面艇生成更高效的路径，实现航行时间与能源消耗的双重优化。