Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives

TL;DR

This paper introduces an adaptive motion primitive approach for robotic manipulator planning that improves exploration efficiency and reduces planning time by using configuration space burs, especially in complex, high-DoF environments.

Contribution

It presents a novel adaptive motion primitive method based on burs for search-based motion planning, enhancing exploration efficiency over fixed primitives.

Findings

Outperforms fixed-primitive planning in complex scenarios

Reduces planning time and number of expansions

Effective for manipulators with high degrees of freedom

Abstract

This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approach is the usage of burs of free configuration space (C-space) as adaptive motion primitives within the graph search algorithm. Due to their feature to adaptively expand in free C-space, burs enable more efficient exploration of the configuration space compared to fixed-sized motion primitives, significantly reducing the time to find a valid path and the number of required expansions. The algorithm is implemented within the existing SMPL (Search-Based Motion Planning Library) library and evaluated through a series of different scenarios involving manipulators with varying number of degrees-of-freedom (DoF) and environment complexity. Results demonstrate that the bur-based approach outperforms fixed-primitive planning in complex scenarios, particularly for high DoF manipulators, while achieving comparable performance in simpler scenarios.