Planning and Control for Deformable Linear Object Manipulation

2025-03-06

AI Summary

Manipulating deformable linear objects (DLOs) in cluttered, obstacle-rich environments remains challenging due to their high degrees of freedom and susceptibility to collisions. Method: This paper proposes a lightweight, deployable planning-control co-design framework. It introduces the first integration of rigid-chain DLO modeling with control barrier functions (CBFs) to guarantee real-time collision avoidance safety. The approach synergistically combines A*-inspired global path planning with a position-based dynamics (PBD)-informed dynamical compensation model, balancing accuracy and real-time performance—without requiring custom planners or large-scale training data. Results: Evaluated on a real mobile manipulator platform, the framework achieves 100% success rate across 1,000 trials on complex tasks—including tent-pole transport and corridor navigation—while significantly reducing planning latency compared to state-of-the-art methods. It establishes a unified breakthrough in computational efficiency, safety assurance, and practical deployability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Manipulating a deformable linear object (DLO) such as wire, cable, and rope is a common yet challenging task due to their high degrees of freedom and complex deformation behaviors, especially in an environment with obstacles. Existing local control methods are efficient but prone to failure in complex scenarios, while precise global planners are computationally intensive and difficult to deploy. This paper presents an efficient, easy-to-deploy framework for collision-free DLO manipulation using mobile manipulators. We demonstrate the effectiveness of leveraging standard planning tools for high-dimensional DLO manipulation without requiring custom planners or extensive data-driven models. Our approach combines an off-the-shelf global planner with a real-time local controller. The global planner approximates the DLO as a series of rigid links connected by spherical joints, enabling rapid path planning without the need for problem-specific planners or large datasets. The local controller employs control barrier functions (CBFs) to enforce safety constraints, maintain the DLO integrity, prevent overstress, and handle obstacle avoidance. It compensates for modeling inaccuracies by using a state-of-the-art position-based dynamics technique that approximates physical properties like Young's and shear moduli. We validate our framework through extensive simulations and real-world demonstrations. In complex obstacle scenarios-including tent pole transport, corridor navigation, and tasks requiring varied stiffness-our method achieves a 100% success rate over thousands of trials, with significantly reduced planning times compared to state-of-the-art techniques. Real-world experiments include transportation of a tent pole and a rope using mobile manipulators. We share our ROS-based implementation to facilitate adoption in various applications.

Problem

• Efficient manipulation of deformable linear objects (DLOs) in obstacle-rich environments.
• Combining global planning and local control for collision-free DLO manipulation.
• Reducing computational complexity without custom planners or extensive data-driven models.

Innovation

• Combines global planner with local controller
• Uses control barrier functions for safety
• Approximates DLO as rigid links for planning