2022 年 37 巻 3 号 p. A-L84_1-13
Multi-Agent Path Finding (MAPF) problem is an extended class of pathfinding problems where multiple paths are assigned to multiple agents without collision. MAPF problem can been applied to various practical domains including path-planning of robot carriers in warehouses, automated taxiing of airplanes, and video games. Multi Agent Pickup and Delivery (MAPD) problem that is a continuous MAPF problem has been studied for more practical cases such as automated warehouses in which robot carriers are assigned to tasks that appear over time. A major goal of MAPD problems is to reduce the execution time or the total path length for the tasks. Token Passing (TP) has been proposed as a solution method for MAPD problems. TP is a task allocation method that also employs a pathfinding algorithm such as Cooperative A* (CA*) or Multi-Label A* (MLA*) algorithm. With TP, pickup-and-delivery tasks are sequentially allocated to agents by employing a shared memory called token where the agents reserve their paths of tasks. In this study, we focus on two issues of the TP. Firstly, TP targets well-formed problems which satisfy the sufficient condition for solvable MAPD instances. The well-formed MAPDs also limit the passable nodes on a graph representation of a warehouse and decreases the utilization of warehouse space in pathfinding. However, if this restriction is relaxed, the collision between agents’ paths might occur. Secondly, the task allocation is greedily performed depending on a default order in which unallocated agents access the token. However, there might be another agent whose current task is almost completed and its current destination is nearer the pickup location of a new task. Without considering such an agent, it might generate a redundant path by allocating a new task to an inappropriate agent. To address the first problem, we relax the movement limitation and introduce a reservation method for stays at destinations that maintains the consistency of reserved paths of agents. For the second problem, we propose a task allocation method that estimates the pickup time of agents to determine more appropriate agents to be allocated to new tasks. Experimental results show that our proposed algorithm reduces the total path length and the total task execution time by reducing the length of pickup paths in comparison to the existing method.
