S&AS: FND: Long-Term Planning and Robust Plan Execution for Multi-Robot Systems

多机器人团队如何在紧凑和杂乱的环境中进行机动，当与现实接触时，没有一个计划幸存下来？传统的方法为理想化的情况做好准备，当传感器或执行器不精确时，必须修补机动，使它们既不结实也不安全。这个项目是人工智能和机器人学的PI的合作，将调查基础研究，以捕获和使用大型机器人导航和协调问题中的时间和不确定性约束。目标应用是即时制造和自动化仓储，但其结果将扩展到需要可靠和安全规划的智能和自主系统的许多应用。该项目将研究多智能体路径寻找(MAPF)，这是一个NP-Hard规划问题，属于一类重要的规划问题，即具有时间和空间约束的多智能体导航问题。这项研究将放松MAPF解算器通常所做的简化假设，即计划执行是完美的，一旦所有机器人都到达目标位置，就会停止。许多已经开发的人工智能规划方法没有在机器人上使用，因为规划/调度使用理想化的环境模型，计划执行永远不会完美，如果执行偏离计划，往往没有足够的时间重新规划。该项目将开发基于概率和时间推理的有理有据的规划和计划执行方法，融合机器人学和人工智能的想法。特别是，PI将结合人工智能社区(即简单时态网络(STN))在规划算法方面的进步，并通过添加及时执行约束以及传感器、执行器和模型的不确定性来使其适应机器人领域。他们将在他们的网页上提供项目成果(如论文、视频和代码)，向人工智能和机器人研究社区提供关于他们研究成果的教程，开发用于多机器人规划的教材，并将本科生纳入他们的研究活动。

How can multi-robot teams maneuver in tight and cluttered environments when "no plan survives contact" with reality? Traditional approaches plan for idealized situations and must patch up maneuvers when sensors or actuators are imprecise, making them neither robust nor safe. This project, a collaboration of PIs from artificial intelligence and robotics, will investigate fundamental research to capture and use timing and uncertainty constraints in large robot navigation and coordination problems. The target applications are just-in-time manufacturing and automated warehousing, but the results will extend beyond to many applications of smart and autonomous systems that need reliable and safe planning. The project will study Multi-Agent Path Finding (MAPF), which is an NP-hard planning problem that belongs to a class of important planning problems, namely multi-agent navigation problems with temporal and spatial constraints. The research will relax simplifying assumptions typically made by MAPF solvers, namely that plan execution is perfect and stops once all robots have reached their goal locations. Many AI planning methods that have been developed are not used on robots, since planning/scheduling uses idealized models of the environment and plan execution is never perfect, and there is often insufficient time for re-planning if execution deviates from the plan. This project will develop well-founded planning and plan-execution methods, based on probabilistic and temporal reasoning, that fuse ideas from robotics and artificial intelligence. In particular, the PIs will combine advances in planning algorithms from the AI community, namely Simple Temporal Networks (STN), and adapt them to the robotics domain by adding timely execution constraints, as well as sensor, actuator, and model uncertainties. They will make project results (such as papers, videos and code) available on their web pages, present tutorials on their research results to the artificial intelligence and robotics research communities, develop teaching material for multi-robot planning, and integrate undergraduate students into their research activities.