Switching Sampling Space of Model Predictive Path-Integral Controller to Balance Efficiency and Safety in 4WIDS Vehicle Navigation

TL;DR

This paper introduces a switching sampling space method for Model Predictive Path-Integral control in 4WIDS vehicle navigation, balancing efficiency and safety by adapting to real-time situations.

Contribution

It proposes a novel approach that switches sampling spaces in MPPI control to improve navigation performance in complex environments.

Findings

Switching sampling spaces enhances navigation performance.

Adaptive control achieves a balance between safety and efficiency.

Simulation results validate the effectiveness of the proposed method.

Abstract

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