Fast Path Planning and Re-Planning for Dual Crane Cooperative Systems in Dynamic Environments via Reduced-Space Sampling and Tree-Reuse Multiple-RRT

Efficient path planning for dual crane cooperative systems is a complex challenge due to high-dimensional configuration spaces and the volatile nature of dynamic construction environments. Conventional sampling-based algorithms often suffer from computational delays and lack the ability to adapt to environmental changes without restarting the search process from scratch. This paper proposes a fast path planning and re-planning framework that integrates a 4-DOF reduced-space sampling strategy with a tree-reuse multiple-RRT mechanism. By constraining the sampling space to critical kinematic degrees of freedom and maintaining pre-explored valid nodes as a persistent knowledge base, the framework enables rapid initial planning and near-real-time response to unperceived obstacles. Experimental results demonstrate that the 4-DOF sampling significantly reduces total planning time compared to conventional 6-DOF methods, with superior performance particularly in restricted narrow-path scenarios. Furthermore, the tree-reuse strategy facilitates efficient path re-initialization, substantially reducing re-planning time and exhibiting a notable learning effect in sequential transport tasks through accelerated convergence. These findings demonstrate that the proposed framework effectively mitigates the “new node exploration” bottleneck, offering a robust and practical solution for autonomous dual crane coordination in complex construction sites.