Search Results (3)

Robots are widely used to carry goods in automated warehouses. Planning collision-free paths for multiple robots which are continuously given new goals is called Lifelong Multi-Agent Pathfinding. In a lifelong environment, conflicts may emerge among the robots, and continuous replanning is needed. We propose, develop, implement, and evaluate the novel approach called the Clustered Reverse Resumable A* (CRRA*) algorithm to enhance the continuous computation of the shortest path from the changing position of a robot to its goal. The Priority Inheritance with Backtracking (PIBT) algorithm is the currently known most efficient algorithm to handle the pathfinding of thousands of robots in a warehouse. The PIBT algorithm requires that in each step each robot evaluates the distances from its surrounding positions to its goal; therefore, we integrate the CRRA* algorithm with the PIBT algorithm to evaluate CRRA*. The evaluation results show that the CRRA* leads to a significant reduction in computation time, especially in larger warehouses where the obstacles form well-separated spaces. At the same time, the degradation in solution quality is minimal. The CRRA* algorithm is more efficient in larger warehouses than the plain Reverse Resumable A* (RRA*) algorithm. The faster computation of slightly suboptimal paths can be useful in many practical applications, especially in situations where real-time planning is more important than finding the optimal paths. CRRA* can also be used as a heuristic in any multi-agent pathfinding solution to obtain a faster, nearly accurate heuristic. Full article

Lifelong multi-agent pathfinding has two interrelated aspects: one is to find conflict-free paths for the agents, and the other is to resolve the conflicts among the agents in the best possible way. We focus on the first aspect by investigating three hierarchical pathfinding approaches, while we apply the same conflict resolution method. We formally present the three pathfinding options: map reduction using fixed waypoints, map reduction using dynamic waypoints, and the classic grid region-based approach. We point out the problem of emerging conflicts in lifelong multi-agent pathfinding with turns. We describe how we evaluate the proposed solutions to example scenarios from the League of Robot Runners competition, and we formulate the goals of the empirical analysis. Based on the experimental results, we point out the need to find the sweet spot between response time and throughput. Full article

We investigate the integration of several additional efficient techniques that improve a solution method for the lifelong multiagent pickup-and-delivery (MAPD) problem to reduce the redundancy in the concurrent task execution and space usage of a warehouse map. The lifelong MAPD problem is an extended class of iterative multiagent pathfinding problems where a set of shortest collision-free travel paths of multiple agents is iteratively planned. This problem models a system in automated warehouses with robot-carrier agents that are allocated to pickup-and-delivery tasks generated on demand. In the task allocation to agents, several solution methods for lifelong MAPD problems consider the endpoints of the agents’ travel paths to avoid the deadlock situations among the paths due to the conflict of the endpoints. Since redundancies are found in the problem settings themselves and the concurrency of allocated tasks, several additional techniques have been proposed to reduce them in solution methods. However, there should be opportunities to investigate the integration of additional techniques with improvements for more practical solution methods. As analysis and an improved understanding of the additional solution techniques based on endpoints, we incrementally integrate the techniques and experimentally investigate their contributions to the quality of task allocation and the paths of the agents. Our result reveals significant complementary effects of the additionally integrated techniques and trade-offs among them in several different problem settings. Full article