Multi-agent Path Planning with Obstacle Penalty Factor
In light load environments,complex obstacle areas will exacerbate local conflicts between agents,leading to a decrease in path solving efficiency.This paper proposes a multi-agent path planning(MAPF)method with obstacle penalty factors in light load environments.First,in the low-level single machine planning process based on the conflict-based search(CBS)algorithm framework,by judging the distribution type of surrounding obstacles that are about to expand the agent's position,corresponding obstacle penalty factors are assigned to them;then,the penalty factors in the path planning process are accumulated and used as the heuristic value of single machine planning to select the path;finally,combined with the upper-level conflict resolution strategy of the CBS algorithm framework,MAPF and conflict coordination are performed.The results show that in a light load environment with a 10%obstacle distribution,the proposed algorithm has a solving time of about 81.38%～83.67%of that of the CBS algorithm,and the expansion amount of the constraint tree(CT)is 60.14%～71.66%of that of the CBS algorithm.Simulation in Gazebo has shown that this method can reduce the number of passes through complex obstacle areas.
万方数据
维普
