Dual Arm Whole Body Motion Planning: Leveraging Overlapping Kinematic Chains

2025-09-30

IEEE-RAS International Conference on Humanoid Robots

AI Summary

Real-time whole-body motion planning for high-degree-of-freedom (DOF) dual-arm robots in dynamic, unknown environments remains challenging due to the curse of dimensionality in high-dimensional configuration spaces and complex collision-avoidance constraints. Method: This paper proposes a Dual Dynamic Roadmap (DRM) approach leveraging kinematic overlap induced by shared joints (e.g., torso). Specifically, it constructs two topologically structured DRMs—one for the left arm–torso subsystem and another for the right arm–torso subsystem—and employs a structured joint search strategy to coordinate optimization across both roadmaps. Contribution/Results: The method effectively mitigates the dimensionality curse by exploiting shared-joint topology to guide intelligent, low-dimensional search in the high-dimensional configuration space. Evaluated in realistic supermarket scenarios, it achieves >2,000 planning trials with an average computation time of 0.4 seconds and a success rate of 99.9%, demonstrating substantial improvements in efficiency, robustness, and practical deployability.

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

• 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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

High degree-of-freedom dual-arm robots are becoming increasingly common due to their effectiveness for operating in human environments and their similarity to the human form factor. However, motion planning in real time within unknown, changing environments remains a challenge for such robots due to the high dimensionality of the configuration space and the complex collision constraints that must be obeyed. In this work, we propose a novel way to alleviate the curse of dimensionality by leveraging the structure imposed by shared joints (e.g. torso joints) in a dual-arm robot. First, we build two dynamic roadmaps (DRM) for each kinematic chain (i.e. left arm + torso, right arm + torso) with specific structure induced by the shared joints. Then we show that we can leverage this structure to intelligently search through the composition of the two roadmaps. We show that this substantially alleviates the curse of dimensionality while being much more efficient than naive search through the Cartesian product of the roadmaps. We ran several experiments in a real-world grocery store with this motion planner on a 19 DoF mobile manipulation robot executing a grocery fulfillment task, achieving $approx 0.4 ~mathrm{s}$ average planning times with 99.9% success rate across more than 2000 motion plans.

Problem

• Real-time motion planning for high-DoF dual-arm robots
• Overcoming dimensionality curse through shared joint structures
• Achieving efficient collision avoidance in dynamic environments

Innovation

• Leveraging shared joints structure for dual-arm robots
• Building dynamic roadmaps for each kinematic chain
• Efficiently searching composed roadmaps to avoid dimensionality curse