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

2026-03-07

AI Summary

This work addresses the challenge of inefficient path tracking and kinematic constraints in high-dimensional configuration spaces for mobile manipulators by proposing a two-stage configuration planning framework. First, task-space discretization and a multi-layer graph are constructed to generate an initial feasible path using the Informed RRT* (IRM) and Dijkstra’s algorithm, ensuring reachability. Subsequently, the feasible region is modeled as a convex hull, and trajectory smoothness is enhanced through continuous optimization via the L-BFGS method. By decoupling high-dimensional planning into low-dimensional graph search followed by numerical refinement, the approach effectively balances global optimality with motion smoothness while rigorously satisfying reachability constraints. Both simulations and real-world experiments demonstrate sub-millimeter tracking accuracy and robust, practical performance on an omnidirectional mobile manipulator.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• mobile manipulators
• path following
• high-dimensional configuration spaces
• kinematic constraints
• trajectory smoothness

Innovation

• two-stage planning
• dimensionality reduction
• graph search
• numerical optimization
• mobile manipulator