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.

翻译：随着科学技术的发展，移动机器人在新一轮世界革命中扮演着至关重要的角色。此外，移动机器人有望在众多领域辅助乃至替代人类工作。为提升移动机器人的自动化水平，需为其集成先进的运动规划器以应对各类环境。复杂迷宫环境是各类移动机器人潜在应用场景中的常见挑战。本文提出一种名为基于快速扩展随机树的高斯过程运动规划器2的新型运动规划器，旨在解决移动机器人在复杂迷宫环境中的运动规划问题。具体而言，该规划器成功融合了轨迹优化运动规划算法高斯过程运动规划器2与基于采样的运动规划算法快速扩展随机树的优势。为验证所提运动规划器的性能与实用性，我们在Matrix实验室中进行了多组仿真测试，并在机器人操作系统的虚拟场景中将其应用于海洋移动机器人。