Multi-Tree Guided Efficient Robot Motion Planning

TL;DR

This paper introduces a multi-tree guided motion planning algorithm that enhances exploration efficiency in complex environments, outperforming existing methods by maintaining multiple trees that collaboratively explore and exploit the state space.

Contribution

The paper proposes a novel multi-tree based algorithm that improves robot motion planning by combining exploration and exploitation through multiple trees.

Findings

Achieves faster planning times in complex environments.

Outperforms existing algorithms on various metrics.

Effectively balances exploration and exploitation.

Abstract

Motion Planning is necessary for robots to complete different tasks. Rapidly-exploring Random Tree (RRT) and its variants have been widely used in robot motion planning due to their fast search in state space. However, they perform not well in many complex environments since the motion planning needs to simultaneously consider the geometry constraints and differential constraints. In this article, we propose a novel robot motion planning algorithm that utilizes multi-tree to guide the exploration and exploitation. The proposed algorithm maintains more than two trees to search the state space at first. Each tree will explore the local environment. The tree starts from the root will gradually collect information from other trees and grow towards the goal state. This simultaneous exploration and exploitation method can quickly find a feasible trajectory. We compare the proposed algorithm with other popular motion planning algorithms. The experiment results demonstrate that our algorithm achieves the best performance on different evaluation metrics.