LEA*: An A* Variant Algorithm with Improved Edge Efficiency for Robot Motion Planning

TL;DR

This paper introduces LEA*, a new graph search algorithm for robot motion planning that improves edge efficiency over A* and is simple to implement, with the weighted version near-optimal in edge efficiency.

Contribution

The paper presents LEA*, a lazy edged A* variant that enhances edge efficiency and is easy to implement, along with a weighted version, wLEA*, for near-optimal performance.

Findings

LEA* and wLEA* are faster than previous algorithms in various planning scenarios.

LEA* maintains optimal vertex efficiency similar to A*.

wLEA* achieves near-optimal edge efficiency close to LazySP.

Abstract

In this work, we introduce a new graph search algorithm, lazy edged based A* (LEA*), for robot motion planning. By using an edge queue and exploiting the idea of lazy search, LEA* is optimally vertex efficient similar to A*, and has improved edge efficiency compared to A*. LEA* is simple and easy to implement with minimum modification to A*, resulting in a very small overhead compared to previous lazy search algorithms. We also explore the effect of inflated heuristics, which results in the weighted LEA* (wLEA*). We show that the edge efficiency of wLEA* becomes close to LazySP and, thus is near-optimal. We test LEA* and wLEA* on 2D planning problems and planning of a 7-DOF manipulator. We perform a thorough comparison with previous algorithms by considering sparse, medium, and cluttered random worlds and small, medium, and large graph sizes. Our results show that LEA* and wLEA* are the fastest algorithms to find the plan compared to previous algorithms.