Adaptive Task Planning for Multi-Robot Smart Warehouse

Using autonomous mobile robots is now a necessity for today's large e-commerce warehouses to save time and energy, prevent human-based errors. Robotic Mobile Fulfillment System (RMFS) controls these as well all other resources tasks in warehouse. There are challenges the management of an RMFS-based smart warehouse because high dynamics system. Limited such robots, stations, totes, item spaces should be managed efficiently after tracking their status continuously. In this study, we propose centralized task approach that adaptive system dynamics. We describe novel conversion algorithm generates from batch orders provides pile-on value. Then heuristic assign generated considering location pods, utilization age tasks. To evaluate proposed algorithms, perform extensive set simulations highly realistic environment including robot charging, replenishment process, path planning algorithms. show significantly reduces order completion even number stock-keeping units (SKU). It also balanced workload among robots. analyze optimal value size effect important parameters count, SKU. The obtained results shade light on how design with efficiency.