A DESIGN METHOD FOR A SELF-ORGANIZING CLOSED-LOOP CONTROL SYSTEM BASED ON STIGMERGY AND BI-LEVEL PLANNING

Self-organizing systems are capable of executing tasks in complex and unknown domains that are unsuitable for humans. However, designing of self-organizing systems is challenging due to balancing global and local decision-making and in enabling the system to adaptively generate desired behaviors in different environments. To address these challenges, in this paper we propose a self-organizing system design framework based on stigmergy and bi-level 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 stigmergy creates a closed-loop control mechanism with "pheromones" 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 an ant colony handling method, to demonstrate the effectiveness and advantage of our approach.