Path planning for unmanned surface vehicle based on predictive artificial potential field. International Journal of Advanced Robotic Systems

TL;DR

This paper introduces a predictive artificial potential field method for unmanned surface vehicle path planning, which reduces sailing time, conserves energy, and effectively avoids obstacles by incorporating time and predictive information.

Contribution

It proposes a novel predictive artificial potential field with modifications that improve path smoothness, feasibility, and obstacle avoidance over traditional methods.

Findings

Reduces maximum turning angles in planned paths

Shortens sailing time compared to traditional methods

Addresses local minimum problems in complex scenarios

Abstract

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.