RRT-GPMP2: A Motion Planner for Mobile Robots in Complex Maze Environments

TL;DR

This paper introduces RRT-GPMP2, a novel motion planning algorithm combining Gaussian process optimization and RRT for mobile robots navigating complex maze environments, validated through simulations and virtual tests.

Contribution

It presents a new hybrid motion planner that integrates Gaussian process optimization with RRT to improve navigation in complex environments.

Findings

Successfully navigates complex maze environments in simulations.

Demonstrates effective application on marine mobile robots in virtual scenarios.

Outperforms traditional methods in planning efficiency and accuracy.

Abstract

With the development of science and technology, mobile robots are playing a significant important role in the new round of world revolution. Further, mobile robots might assist or replace human beings in a great number of areas. To increase the degree of automation for mobile robots, advanced motion planners need to be integrated into them to cope with various environments. Complex maze environments are common in the potential application scenarios of different mobile robots. This article proposes a novel motion planner named the rapidly exploring random tree based Gaussian process motion planner 2, which aims to tackle the motion planning problem for mobile robots in complex maze environments. To be more specific, the proposed motion planner successfully combines the advantages of a trajectory optimisation motion planning algorithm named the Gaussian process motion planner 2 and a sampling-based motion planning algorithm named the rapidly exploring random tree. To validate the performance and practicability of the proposed motion planner, we have tested it in several simulations in the Matrix laboratory and applied it on a marine mobile robot in a virtual scenario in the Robotic operating system.