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

2026-02-22

AI Summary

This work proposes an enhanced artificial potential field method that integrates temporal information and a predictive potential field to address challenges in path planning for high-speed unmanned surface vehicles, including prolonged travel time, high energy consumption, susceptibility to local minima, and insufficient path smoothness. By incorporating angular constraints, adaptive velocity adjustment, and a predictive potential field mechanism, the approach effectively mitigates the local minima problem commonly encountered with concave obstacles in conventional methods, while improving path smoothness, dynamic feasibility, and real-time collision avoidance capability. Simulation results demonstrate that the proposed method significantly reduces travel time and energy consumption, yielding more efficient, smoother, and dynamically executable trajectories.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Temporal Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• path planning
• unmanned surface vehicle
• artificial potential field
• local minimum
• sailing time

Innovation

• Predictive Artificial Potential Field
• Unmanned Surface Vehicle
• Path Planning
• Local Minimum Problem
• Dynamic Obstacle Avoidance