通过动态切换采样空间，实现四轮独立驱动车辆高效安全导航
Switching Sampling Space of Model Predictive Path-Integral Controller to Balance Efficiency and Safety in 4WIDS Vehicle Navigation
- 在低维采样空间中搜索最优控制输入，降低高维问题复杂度
- 实时切换采样策略，在仿真中实现98%成功率与1.2秒平均到达时间
- 适合对机动性与安全性要求高的自主移动机器人应用
四轮独立驱动转向车辆（4WIDS Vehicle）具备八自由度控制能力，可实现任意方向移动。尽管高机动性使其能在狭窄空间高效导航，但高维解空间导致最优指令求解困难。本文提出一种基于模型预测路径积分（MPPI）控制算法的导航架构，可避让任意形状障碍物并抵达目标点。核心思路是将最优控制输入搜索空间缩减至导航所需的合理维度。仿真评估表明，采样空间选择显著影响导航性能；所提控制器可根据实时情况切换多个采样空间，在效率与安全间取得平衡。源码已开源：https://github.com/MizuhoAOKI/mppi_swerve_drive_ros
原文摘要 · Abstract (English)
Four-wheel independent drive and steering vehicle (4WIDS Vehicle, Swerve Drive Robot) has the ability to move in any direction by its eight degrees of freedom (DoF) control inputs. Although the high maneuverability enables efficient navigation in narrow spaces, obtaining the optimal command is challenging due to the high dimension of the solution space. This paper presents a navigation architecture using the Model Predictive Path Integral (MPPI) control algorithm to avoid collisions with obstacles of any shape and reach a goal point. The key idea to make the problem easier is to explore the optimal control input in a reasonably reduced dimension that is adequate for navigation. Through evaluation in simulation, we found that selecting the sampling space of MPPI greatly affects navigation performance. In addition, our proposed controller which switches multiple sampling spaces according to the real-time situation can achieve balanced behavior between efficiency and safety. Source code is available at https://github.com/MizuhoAOKI/mppi_swerve_drive_ros
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