Path Planning for Pickup and Delivery with Air-Ground Collaborative Robots in Smart Logistics

Unmanned aerial vehicles (UAVs) are widely used in smart logistics, but their limited endurance and payload capacity pose significant challenges. To address this, unmanned ground vehicles (UGVs) are proposed as mobile warehouses, enabling simultaneous pickup and delivery. First, an innovative path planning model is proposed accounting for road network constraints, time window limitations, air-ground coupling constraints, and UAV payload and endurance restrictions. Then, an improved brain storm optimization (IBSO) algorithm is designed to solve the optimization problem. Through a case study and algorithm comparison, the experimental results demonstrate that the IBSO algorithm significantly outperforms the genetic algorithm in optimizing total transportation distance. Additionally, the air-ground collaborative pickup and delivery mode reduces transportation distance by 44.6 % compared to the separated pickup and delivery approach, resulting in a substantial reduction in logistics costs. The proposed model and the designed algorithm offer an effective solution for optimizing air-ground collaborative logistics systems.