Marsupial robot teams consist of carrier robots that transport and deploy multiple passenger robots, such as a team of ground robots that carry and deploy multiple aerial robots, to rapidly explore complex environments. We specifically address the problem of planning the deployment times and locations of the carrier robots to best meet the objectives of a mission while reasoning over uncertain future observations and rewards. While prior work proposed optimal, polynomial-time solutions to single-carrier robot systems, the multiple-carrier robot deployment problem is fundamentally harder as it requires addressing conflicts and dependencies between deployments of multiple passenger robots. We propose a centralized heuristic search algorithm for the multiple-carrier robot deployment problem that combines Monte Carlo Tree Search with a dynamic programming-based solution to the Sequential Stochastic Assignment Problem as a rollout action-selection policy. Our results with both procedurally-generated data and data drawn from the DARPA Subterranean Challenge Urban Circuit show the viability of our approach and substantial exploration performance improvements over alternative algorithms.
翻译:由运货机器人组成的飞行器团队运输和部署多个客运机器人,如携带和部署多个航空机器人的地面机器人团队,以迅速探索复杂的环境。我们具体解决了规划载货机器人部署时间和地点的问题,以最好地实现飞行任务的目标,同时推理未来不确定的观察和奖赏。虽然先前的工作是对单载机器人系统提出的最佳、多元时数解决方案,但多载机器人部署问题从根本上更为困难,因为它需要解决多个客运机器人部署之间的冲突和依赖性。我们建议对多载货机器人部署问题采用集中的超载搜索算法,将蒙特卡洛树搜索与基于动态程序的解决办法相结合,作为推出的行动选择政策。我们从DARPA Subterrane Challenian Challenger City Cird中获取的程序性数据和数据的结果显示了我们方法的可行性,以及对替代算法的大幅探索性改进。