Integrated Dual-Crane Positioning and Lifting Planning in Extreme Conditions: A Critical States and Spatial Constraint Optimization Approach

The intelligent planning of dual-crane cooperative lifting has emerged as a critical enabling technology for handling heavy and oversized payloads in complex engineering environments, particularly as infrastructure projects increase in scale and operational constraints become more challenging. However, existing planning methods neglect the critical influence of crane positioning on path feasibility. Besides, they remain inadequate for extreme conditions where payloads approach maximum capacity, and even minor swings may trigger catastrophic instability. This study proposes a hierarchical optimization framework for dual-crane positioning and lifting planning under such extreme scenarios, integrating critical states and spatial constraints. The upper layer systematically optimizes crane positions and motions by resolving geometric constraints between cranes and payloads, including working radii, boom lengths, slewing angles, and hoisting length. A weighted multiobjective function is formulated to balance energy consumption and time efficiency, and an improved A* algorithm is used to efficiently solve the integrated optimization problem through critical states discretization, where slewing motion is segmented into several phases. Furthermore, the lower layer enforces spatial synchronization through antiswing trajectory planning, where angular velocity profiles of both cranes are coordinated to maintain payload stability while adhering to crane-payload geometric constraints. Through simulation and software platform-based pseudo-experiments, the proposed method demonstrates a 66.57% improvement in computational efficiency and a 50.04% enhancement in safety performance compared to traditional approaches, while ensuring guaranteed collision avoidance and mechanical feasibility in extreme conditions. The proposed method significantly enhances safety and maximizes payload utilization under extreme conditions, providing a robust theoretical and practical framework that addresses fundamental challenges in modern dual-crane lifting operations.