Multi-agent embodied intelligence has been integrated into warehouse systems for transporting commodities,which can be formulated as the lifelong multi-agent pickup and delivery(LMAPD)problem.However,existing research...Multi-agent embodied intelligence has been integrated into warehouse systems for transporting commodities,which can be formulated as the lifelong multi-agent pickup and delivery(LMAPD)problem.However,existing research has not addressed the energy limitations associated with the LMAPD problem,rendering it inapplicable to real-world systems.In this study,we formulate the energy-limited lifelong multi-agent pickup and delivery(EL-MAPD)problem,in which each agent consumes or recharges a specific amount of energy for its actions.Furthermore,we theoretically define a realistic subclass of EL-MAPD instances known as well-formed EL-MAPD instances.This subclass ensures the existence of feasible solutions,preventing scenarios in which agents run out of energy or collide with each other.To obtain a high-quality feasible solution under the framework of concurrent planning and execution,we propose the fallback priority planning(FPP)algorithm.The FPP algorithm employs a fallback mechanism to ensure the acquisition of a feasible action in each planning episode.Experimental results demonstrate that the FPP algorithm effectively balances throughput and energy consumption.Furthermore,to enhance the warehouse system’s throughput in real-world scenarios,our findings suggest decreasing the planning time limit for each planning episode,increasing the number of agents,and implementing a recharge strategy wherein agents complete the recharging process only when their energy levels are full.展开更多
文摘Multi-agent embodied intelligence has been integrated into warehouse systems for transporting commodities,which can be formulated as the lifelong multi-agent pickup and delivery(LMAPD)problem.However,existing research has not addressed the energy limitations associated with the LMAPD problem,rendering it inapplicable to real-world systems.In this study,we formulate the energy-limited lifelong multi-agent pickup and delivery(EL-MAPD)problem,in which each agent consumes or recharges a specific amount of energy for its actions.Furthermore,we theoretically define a realistic subclass of EL-MAPD instances known as well-formed EL-MAPD instances.This subclass ensures the existence of feasible solutions,preventing scenarios in which agents run out of energy or collide with each other.To obtain a high-quality feasible solution under the framework of concurrent planning and execution,we propose the fallback priority planning(FPP)algorithm.The FPP algorithm employs a fallback mechanism to ensure the acquisition of a feasible action in each planning episode.Experimental results demonstrate that the FPP algorithm effectively balances throughput and energy consumption.Furthermore,to enhance the warehouse system’s throughput in real-world scenarios,our findings suggest decreasing the planning time limit for each planning episode,increasing the number of agents,and implementing a recharge strategy wherein agents complete the recharging process only when their energy levels are full.
