APP: A* Post-Processing Algorithm for Robots with Bidirectional Shortcut and Path Perturbation

TL;DR

This paper introduces APP, a post-processing algorithm for A* paths that reduces path length and unnecessary heading changes by bidirectional shortcutting and path perturbation, improving efficiency and smoothness in robotic navigation.

Contribution

The paper presents a systematic post-processing method for A* paths that enhances path optimality and smoothness using bidirectional shortcutting and iterative perturbation techniques.

Findings

APP reduces path length compared to existing methods.

APP decreases unnecessary heading changes in paths.

Experimental results show improved planning time and navigation smoothness.

Abstract

Paths generated by A* and other graph-search-based planners are widely used in the robotic field. Due to the restricted node-expansion directions, the resulting paths are usually not the shortest. Besides, unnecessary heading changes, or zig-zag patterns, exist even when no obstacle is nearby, which is inconsistent with the human intuition that the path segments should be straight in wide-open space due to the absence of obstacles. This article puts forward a general and systematic post-processing algorithm for A* and other graph-search-based planners. The A* post-processing algorithm, called APP, is developed based on the costmap, which is widely used in commercial service robots. First, a bidirectional vertices reduction algorithm is proposed to tackle the asymm- etry of the path and the environments. During the forward and backward vertices reduction, a thorough shortcut strategy is put forward to improve the path-shortening performance and avoid unnecessary heading changes. Second, an iterative path perturbation algorithm is adopted to locally reduce the number of unnecessary heading changes and improve the path smooth- ness. Comparative experiments are then carried out to validate the superiority of the proposed method. Quantitative performance indexes show that APP outperforms the existing methods in planning time, path length as well as the number of unnecessary heading changes. Finally, field navigation experiments are carried out to verify the practicability of APP.