Batch Iterative Dual Optimization for Collision-Free Robot Motion Generation

Collision-free robot motion planning is crucial in robotic applications. Traditional sampling-based methods struggle with kinematic/dynamic constraints and intermediate process constraints, limiting their use to point-to-point motion generation. Optimization-based methods, such as sequential convex...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2025-01, p.1-9
Main Authors: Lin, Shize, Hu, Chuxiong, Yu, Jichuan, Liang, Yixuan
Format: Article
Language:English
Subjects:
Batch iterative dual optimization (BIDopt)
Charge coupled devices
Collision avoidance
continuous collision detection (CCD)
Convergence
Informatics
Iterative methods
Planning
Robot motion
robot motion planning
Robots
Service robots
Trajectory optimization
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Summary:Collision-free robot motion planning is crucial in robotic applications. Traditional sampling-based methods struggle with kinematic/dynamic constraints and intermediate process constraints, limiting their use to point-to-point motion generation. Optimization-based methods, such as sequential convex programming, often face issues of artificial feasibility and soft failure. To enhance both the success rate and quality of robot motion generation, this article presents a novel iterative motion planning framework grounded in a dual collision constraint formulation. A smooth and differentiable continuous collision detection method is developed based on the strong duality of convex body collision constraints. Building on this, trajectory optimization problem is simplified and an iterative algorithm is designed for collision information updating and batch gradient descent. Simulation and physical experimental results demonstrate that the proposed method performs excellently in both free-space point-to-point motion tasks and continuous task-space tracking trajectory generation with comparison to multiple classical methods, suggesting its promising applications in various robotic automation scenarios.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3507955