Batch Iterative Dual Optimization for Collision-Free Robot Motion Generation

Abstract

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.