提出RRT-GPMP2，让机器人在复杂迷宫中更高效规划路径。
RRT-GPMP2: A Motion Planner for Mobile Robots in Complex Maze Environments
- 融合RRT采样与GPMP2优化，兼顾搜索效率与路径质量。
- 在仿真与虚拟海洋机器人场景中验证，路径平滑且成功率高。
- 适合需要高精度路径规划的复杂环境机器人应用。
随着科技发展，移动机器人在新一轮全球变革中扮演着重要角色，有望在众多领域替代或辅助人类。为提升移动机器人的自动化水平，需集成先进运动规划算法以应对多样化环境。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。本文提出一种新型运动规划算法——基于快速探索随机树的高斯过程运动规划器2（RRT-GPMP2），旨在解决移动机器人在复杂迷宫环境中的运动规划问题。该方法有效结合了高斯过程运动规划器2（GPMP2）在轨迹优化方面的优势与快速探索随机树（RRT）在采样搜索上的高效性。为验证所提算法的性能与实用性，我们在矩阵实验室（MATLAB）中进行了多项仿真测试，并将其应用于机器人操作系统（ROS）中的虚拟海洋移动机器人场景，结果表明该算法在复杂环境中具有良好的可行性和规划效果。
原文摘要 · Abstract (English)
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.
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