Generating and Optimizing Topologically Distinct Guesses for Mobile Manipulator Path Planning with Path Constraints

TL;DR

This paper introduces a pipeline that generates multiple topologically distinct paths for mobile manipulators, optimizing them to find solutions closer to the global optimum despite nonconvex constraints.

Contribution

It presents a novel method for discovering and optimizing multiple homotopically distinct paths to improve path planning outcomes for mobile manipulators.

Findings

Effective in finding diverse paths under complex constraints

Improves likelihood of approaching global optimal solutions

Demonstrated success in obstacle-rich environments

Abstract

Optimal path planning is prone to convergence to local, rather than global, optima. This is often the case for mobile manipulators due to nonconvexities induced by obstacles, robot kinematics and constraints. This paper focuses on planning under end effector path constraints and attempts to circumvent the issue of converging to a local optimum. We propose a pipeline that first discovers multiple homotopically distinct paths, and then optimizes them to obtain multiple distinct local optima. The best out of these distinct local optima is likely to be close to the global optimum. We demonstrate the effectiveness of our pipeline in the optimal path planning of mobile manipulators in the presence of path and obstacle constraints.