An Efficient Method for Extracting the Shortest Path from the Dubins Set for Short Distances Between Initial and Final Positions

TL;DR

This paper introduces a novel, efficient method for directly identifying the shortest Dubins path between two configurations, significantly reducing computational complexity for path planning in autonomous robots.

Contribution

The paper presents a classification-based approach that simplifies shortest Dubins path computation by reducing candidate paths through configuration grouping.

Findings

Method outperforms existing algorithms in computational efficiency

Classification reduces the number of path candidates to analyze

Approach is effective for real-time path planning in robotics

Abstract

Path planning is crucial for the efficient operation of Autonomous Mobile Robots (AMRs) in factory environments. Many existing algorithms rely on Dubins paths, which have been adapted for various applications. However, an efficient method for directly determining the shortest Dubins path remains underdeveloped. This paper presents a comprehensive approach to efficiently identify the shortest path within the Dubins set. We classify the initial and final configurations into six equivalency groups based on the quadrants formed by their orientation angle pairs. Paths within each group exhibit shared topological properties, enabling a reduction in the number of candidate cases to analyze. This pre-classification step simplifies the problem and eliminates the need to explicitly compute and compare the lengths of all possible paths. As a result, the proposed method significantly lowers computational complexity. Extensive experiments confirm that our approach consistently outperforms existing methods in terms of computational efficiency.