Scalable Potential-Field Multi-Agent Coordination In Resource Distribution Tasks

this paper, we look at a scalable, generic methodology for coor- dination in large, embodied multi-agent systems (MAS), operating in noisy, real-time environments. More specifically, we look at real- world task assignment problems and use the testbed of robots per- forming resource distribution in large storage facilities. The main problem when using MAS for task assignment problems in real- world environments is the limited scalability of traditional MAS approaches such as centralized planning. As a first step toward a generic methodology for embodied, dis- tributed MAS, we analyse the behavior and the scalability of an embodied MAS which is driven by simulated potential fields. Re- sources in a storage facility emit a certain simulated potential while robots emit an opposite potential. Applying principles inspired by physics to local behavior, idle robots move toward resources but away from other robots. Whenever a resource appears, a central agent system registers this appearance and creates a plan for a ro- bot to pick up the resource and deliver it to some destination area. We show that this system is highly scalable with respect to both the environment and the number of robots, while maintaining func- tionality, adaptivity and robustness. Furthermore, the integration of planning prevents local optima and increases fairness. In future work, the central agent system will be replaced by a dis- tributed system, such as a sensor network, to further increase scal- ability, adaptivity and robustness of the system.