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Dynamic Path Costs Update Method reflecting Delivery Tendencies for Multi-Agent Delivery Tasks

In multi-agent delivery tasks, Dijkstra’s Algorithm, which identifies and plans the shortest path based on costs, is very popular. Specifically, using dynamic path costs, namely updating each path cost to reflect the number of agents having visited it over time, is an efficient method for accomplishing multi-agent delivery tasks. Indeed, it can consider the following logic: “a path that is jammed is not likely to be selected in the routing process”. Suppose tasks that can train with similar delivery tendencies in advance, optimizing the update path cost rule set would lead to more efficient agent path planning. Indeed, it would consider that “the paths that will be jammed are not likely to be selected in the routing process”. Thus, we propose a dynamic path cost update method that reflects delivery tendencies. We design the update path cost if-then rule (what path costs should be added, and under what conditions?) as a current partial agent distribution around an observation point for the if-part, and the amount of added (or subtracted) path cost for the then-part. In one delivery task, each observation point is tied to partial agents’ distribution. If a distribution is collated with an update rule’s if-part, the corresponding path of the observation point is updated given the update path costs that the collated rule’s then-part indicates. This update continues during a task at each observation point at an interval. The set of update path cost if-then rules is optimized with Learning Classifier Systems. We evaluated our method with a multi-agent delivery task that simulates a warehouse swarm robot’s Pickup and Delivery task. The results showed our method accomplished the tasks faster than a previous approach. In addition, we evaluated the superiority range of the proposed method when the number of agents used changes. Finally, we compared the path cost transition tendency. It revealed our method was more likely to drastically alter path costs.