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

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 that our pipeline is able to circumvent the problem of local optima and produces a final local optimum that is close to the global optimum.