Two-Stage Path Following for Mobile Manipulators via Dimensionality-Reduced Graph Search and Numerical Optimization

TL;DR

This paper introduces a two-stage path planning method for mobile manipulators that reduces high-dimensional problems into manageable subproblems, combining graph search and numerical optimization for precise and efficient path following.

Contribution

The paper proposes a novel two-stage framework that decouples high-dimensional planning into a 2-DoF base optimization and a continuous refinement, improving efficiency and accuracy.

Findings

Achieves sub-millimeter kinematic accuracy in simulation.

Demonstrates robustness and practicality through physical experiments.

Ensures computational efficiency with a graph-based initial solution.

Abstract

Efficient path following for mobile manipulators is often hindered by high-dimensional configuration spaces and kinematic constraints. This paper presents a robust two-stage configuration planning framework that decouples the 8-DoF planning problem into a tractable 2-DoF base optimization under a yaw-fixed base planning assumption. In the first stage, the proposed approach utilizes IRM to discretize the task-space path into a multi-layer graph, where an initial feasible path is extracted via a Dijkstra-based dynamic programming approach to ensure computational efficiency and global optimality within the discretized graph. In the second stage, to overcome discrete search quantization, feasible base regions are transformed into convex hulls, enabling subsequent continuous refinement via the L-BFGS algorithm to maximize trajectory smoothness while strictly enforcing reachability constraints. Simulation results demonstrate the theoretical precision of the proposed method by achieving sub-millimeter kinematic accuracy in simulation, and physical experiments on an omnidirectional mobile manipulator further validate the framework's robustness and practical applicability.