Toward Safe and Efficient Human-Swarm Collaboration: A Hierarchical Multi-Agent Pickup and Delivery Framework

pickup and delivery (MAPD) is crucial in intelligent storage systems (ISSs), where multiple automated guided vehicles (AGVs) are assigned to various and potentially un -certain dynamic tasks. In this work, we consider a human-swarm hybrid system consisting of human workers and a swarm of AGVs collaborating to accomplish MAPD tasks. A human-swarm hybrid system pickup and delivery ((HS)(2)PD) framework based on the receding-horizon prediction window is proposed, which facilities the development of future ISSs. This (HS)(2)PD framework is es-sentially a two-layer hierarchical decision procedure, which takes the uncertainties of human behavior and the dynamic changes of tasks into account. The first layer is a two-level programming model handling the problems of mode assignment and task allocation. The second layer calculates each vehicle's exact path by solving mixed-integer programmings. An integrated high-efficient algo-rithm for the (HS)(2)PD problem is also proposed. The practicality and validity of the above algorithm are demonstrated via several groups of numerical simulations of (HS)(2)PD tasks.