CO-DOSP: A hierarchical optimization-based motion planner for multi-robot manipulation in confined and task-constrained workspace

Abstract

Efficient and robust motion planning for multi-robot systems is critical to advancing industrial automation tasks such as logistics, assembly, and surface finishing. However, achieving reliable coordination under complex task constraints, obstacle avoidance, and high-dimensional configuration spaces remains challenging. This paper presents CO-DOSP, a novel hierarchical optimization framework that integrates convex decomposition with duality-aware second-order optimization for multi-arm systems operating in constrained environments. The proposed method sequentially decomposes complex planning problems into tractable subproblems and utilizes the strong duality properties of second-order approximations to enable efficient null-space searches and manifold projections, effectively avoiding local minima. Extensive simulations and real-world experiments on redundant collaborative robots demonstrate that CO-DOSP achieves the highest planning success rate and over threefold faster computation times compared to the best baseline. These results validate the framework’s scalability, robustness, and practical applicability, offering a valuable contribution to industrial automation manufacturing and confined collaborative operations.