Efficient Task Planning for Complex Orders in Robotized Warehouses

The rapid growth of e-commerce has driven an increasing demand for robotized warehouses to handle large-scale logistics orders. Upon receiving orders, a warehouse engages in task planning that involves two crucial stages: matching the orders with racks that contain the required items, and planning the robot routes to deliver those racks for order fulfillment. Hence, effective task planning is essential for maximizing order throughput. However, while existing techniques perform well for orders that involve items from a single rack, they exhibit low efficiency and poor performance when dealing with complex orders that require multiple items from different racks. In this paper, we introduce the robotized warehouse complex task planning problem and propose a novel Complex Order Online Planning (COOP) framework to address the challenge. Specifically, the framework matches orders with racks using a maximal coverage matching method, optimized through vector similarity search and a residual matching strategy. Then, it adopts an effective progressive prioritized pathfinding algorithm to transport matched racks with minimal delivery cost. Finally, the framework introduces an enhanced pathfinding-aware rack selection model that considers rack delivery costs from the pathfinding stage to collaboratively optimize rack matching and overall planning scheme. Extensive experiments on real-world and synthetic datasets demonstrate that our approaches exhibit strong performance across various parameter configurations.