一种利用可持续信息的在线多智能体路径搜索方法
An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information
摘要：
多智能体路径寻找（MAPF）是移动智能体到目标顶点无碰撞的问题。在在线MAPF问题中，新的代理人可以在任何时候添加到环境中，并且当前代理人没有关于未来代理人的信息。现有的在线方法不能重用以前的规划上下文，导致冗余计算，降低算法效率。因此，我们提出了一个三层的方法来解决在线MAPF利用可持续发展的信息，这可以减少其冗余计算。高级求解器，可持续重新规划算法（SR），管理规划上下文并模拟环境。中层求解器，可持续的基于冲突的搜索算法（SCBS），建立一个冲突树，并保持规划上下文。低级求解器，可持续反向安全间隔路径规划算法（SRSIPP），是一个高效的单智能体求解器，使用以前的规划上下文，以减少重复计算。实验表明，我们提出的方法在计算效率方面有显着的改善。在一个测试场景中，我们的算法在不同的代理数量设置下平均比SOTA快1.48倍。
英文摘要：
Multi-agent path finding (MAPF) is the problem of moving agents to the goal vertex without collision. In the online MAPF problem, new agents may be added to the environment at any time, and the current agents have no information about future agents. The inability of existing online methods to reuse previous planning contexts results in redundant computation and reduces algorithm efficiency. Hence, we propose a three-level approach to solve online MAPF utilizing sustainable information, which can decrease its redundant calculations. The high-level solver, the Sustainable Replan algorithm (SR), manages the planning context and simulates the environment. The middle-level solver, the Sustainable Conflict-Based Search algorithm (SCBS), builds a conflict tree and maintains the planning context. The low-level solver, the Sustainable Reverse Safe Interval Path Planning algorithm (SRSIPP), is an efficient single-agent solver that uses previous planning context to reduce duplicate calculations. Experiments show that our proposed method has significant improvement in terms of computational efficiency. In one of the test scenarios, our algorithm can be 1.48 times faster than SOTA on average under different agent number settings.
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