Sandwich Approach for Motion Planning and Control

TL;DR

This paper introduces a fluid mechanics-inspired approach for robot motion planning and control in obstacle-rich environments, utilizing a novel transformation and MPC-based control to generate safer, shorter paths.

Contribution

It presents a new transformation method for motion planning and an MPC-based control framework, improving path safety and efficiency compared to traditional methods.

Findings

A* search over the new planning space yields shorter paths.

The proposed MPC control enforces safety constraints effectively.

The approach outperforms existing methods in obstacle-laden environments.

Abstract

This paper develops a new approach for robot motion planning and control in obstacle-laden environments that is inspired by fundamentals of fluid mechanics. For motion planning, we propose a novel transformation between motion space, with arbitrary obstacles of random sizes and shapes, and an obstacle-free planning space with geodesically-varying distances and constrained transitions. We then obtain robot desired trajectory by A* searching over a uniform grid distributed over the planning space. We show that implementing the A* search over the planning space can generate shorter paths when compared to the existing A* searching over the motion space. For trajectory tracking, we propose an MPC-based trajectory tracking control, with linear equality and inequality safety constraints, enforcing the safety requirements of planning and control.