Motion Planning of Cooperative Nonholonomic Mobile Manipulators

2026-05-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

This paper introduces a real-time motion planning technique for cooperative object transportation by nonholonomic mobile manipulator robots (MMRs) operating in environments with both static and dynamic obstacles. The method employs a two-step process: an offline visibility vertices-based path planning algorithm generates a global piece-wise linear path and defines static obstacle-free convex polygons. Subsequently, an online Nonlinear Model Predictive Control (NMPC) technique plans feasible motion for the nonholonomic MMRs, jointly considering the mobile base and manipulator arm capabilities. This approach accounts for kinodynamic constraints, static obstacles (including concave polygonal shapes), and dynamic obstacles. The efficiency and collision avoidance capabilities of the proposed technique were validated through numerical simulations with five MMRs in a 10m x 10m environment and hardware experiments using two in-house developed ROS-enabled MMRs in a 4m x 4m environment, demonstrating real-time performance.

Key takeaway

For research scientists developing cooperative robotics, this work demonstrates a robust approach to motion planning for nonholonomic mobile manipulators. You should consider adopting a two-stage planning architecture, leveraging offline path generation with convex polygon representation for static obstacles, and integrating NMPC for real-time kinodynamic and dynamic obstacle avoidance. This strategy effectively addresses the complexities of nonholonomic constraints and collaborative object transportation in cluttered environments.

Key insights

A two-step motion planning approach enables nonholonomic MMRs to cooperatively transport objects amidst complex obstacles.

Principles

• Separate global path planning from local motion planning.
• Convexify obstacle-free space for NMPC efficiency.
• Jointly plan for mobile base and manipulator arm.

Method

The method combines offline visibility vertices-based path planning with online NMPC. It generates a global path, computes convex polygons around path segments, and then uses NMPC for kinodynamically feasible, collision-free trajectory generation in receding horizons.

In practice

• Use CasADi and Ipopt for NMPC problem solving.
• Approximate nonholonomic dynamics with Runge-Kutta.
• Implement circumscribing bounding circles for collision geometry.

Topics

• Cooperative Mobile Manipulators
• Nonholonomic Motion Planning
• Nonlinear Model Predictive Control
• Visibility Vertices Algorithm
• Object Transportation

Best for: Research Scientist, Robotics Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.