Multi-Agent Goal Reasoning with the CLIPS Executive in the RoboCup Logistics League

Production processes in smart factories moved away from a process-centered paradigm into a modular production paradigm, facing the variations in demanded product configurations and deadlines with a flexible production. The RoboCup Logistics League (RCLL) is a robotics competition in the context of in-factory logistics, in which a team of three autonomous mobile robots manufacture dynamically ordered products. The main challenges include task reasoning, multi-agent coordination, and robust execution in a dynamic environment. We present a multi-agent goal reasoning approach where agents continuously reason about which objectives to pursue rather than only planning for a fixed objective. We describe an incremental, distributed formulation of the RCLL problem implemented in the goal reasoning system CLIPS Executive. We elaborate what kind of goals we use in the RCLL, how we use goal trees to define an effective production strategy and how agents coordinate effectively by means of primitive lock actions as well as goal-level resource allocation. The system utilizes a PDDL model to describe domain predicates and actions, as well as to determine the executability and effects of actions during execution. Our agent is able to react to unexpected events, such as a broken machine or a failed action, by monitoring the execution of the plan, re-evaluating goals, and taking over goals which were previously pursued by another robot. We present a detailed evaluation of the system used on real robots.