An intelligent docking-and-gripping framework for composite robots, based on Material Partitioning via Virtual Shelves (MPVS) and Mutation-Based Coverage Optimization (MBCO), is developed to overcome the limitations of traditional static workstations and single-task mappings in high-mix, high-complexity warehouse environments. Multi-dimensional and disordered material distributions are reorganized by MPVS into an ordered one-dimensional representation along the aisle centerline, thereby reducing the complexity of docking-and-gripping planning. The docking–workstation planning task is formulated as a coverage problem on a specified plane. Docking positions are then optimized by a multi-objective method that integrates a greedy maximum-coverage-circle heuristic with MBCO to cope with dynamically varying inventories. Across diverse scenarios and material distributions, docking frequency is reduced by 58.3% relative to unplanned schemes while 100% coverage of target items is preserved. Compared with an initial greedy solution, docking frequency is further reduced by 37.5%, and planning time is decreased in comparison with alternative algorithms. In large-scale cases with up to 2205 items, MBCO maintains sublinear planning time below 0.2 s on average and reduces the mean number of docking workstations by about 20% compared with a pure greedy strategy. The spatial relationship between robot docking stations and the manipulator workspace is further analyzed, and seventh-order polynomial time allocation refined by a weight-normalized ant colony optimization (ACO) algorithm is employed for joint-trajectory optimization, enhancing tracking accuracy, improving motion smoothness, and reducing energy consumption, while a composite trajectory cost is lowered by more than 30% under a balanced weight setting. The feasibility and effectiveness of the composite-robot storage–retrieval system in dynamic industrial environments are validated experimentally in realistic 3C-component warehousing scenarios.
Zhang et al. (Mon,) studied this question.