Abstract
Advanced multi-agent systems are capable of executing tasks in complex and unknown domains that are unsuitable for humans. However, the design of multi-agent systems faces challenges in balancing global and local decision-making and in enabling the system to adaptively generate desired behaviors in a dynamic environment. To address these challenges, in this paper we propose a multi-agent system design framework based on bi-level closed-loop planning. We use the multi-agent box-pushing problem as an example to verify the framework. Within this framework, the upper-level planning (which is used for box position prediction) and the lower-level planning (which is used for agent position allocation) are designed to connect and coordinate between the global and local decisions. The influence of states based on planning creates a closed-loop control mechanism with temporary targets as input, allowing the system to adapt to various environments. In this paper we use Webots as the simulation platform to conduct multi-agent box-pushing experiments and compare the results with rule-based method, to demonstrate the effectiveness and advantages of our approach.
