We would like to enable a collaborative multiagent team to navigate at long length scales and under uncertainty in real-world environments. In practice, planning complexity scales with the number of agents in the team, with the length scale of the environment, and with environmental uncertainty. Enabling tractable planning requires developing abstract models that can represent complex, high-quality plans. However, such models often abstract away information needed to generate directly-executable plans for real-world agents in real-world environments, as planning in such detail, especially in the presence of real-world uncertainty, would be computationally intractable. In this paper, we describe the deployment of a planning system that used a hierarchy of planners to execute collaborative multiagent navigation tasks in real-world, unknown environments. By developing a planning system that was robust to failures at every level of the planning hierarchy, we enabled the team to complete collaborative navigation tasks, even in the presence of imperfect planning abstractions and real-world uncertainty. We deployed our approach on a Clearpath Husky-Jackal team navigating in a structured outdoor environment, and demonstrated that the system enabled the agents to successfully execute collaborative plans.

翻译：我们期望实现协作式多智能体团队在真实环境中进行长距离导航并应对不确定性。实践中，规划复杂度随智能体数量、环境尺度及环境不确定性增长。为实现可解规划，需开发能表征复杂高质量规划的抽象模型。然而，这类模型常缺失生成可直接执行规划所需的信息——以如此精细程度进行规划，尤其在现实不确定性场景下，会因计算不可解而难以实现。本文阐述了一个采用规划器分层结构、在未知真实世界中执行协作式多智能体导航任务的规划系统部署方案。通过构建对规划层级各层级故障均具鲁棒性的规划系统，即使存在不完善的规划抽象与现实不确定性，智能体团队仍能完成协作导航任务。我们在结构化室外环境中将所提方法部署于Clearpath Husky-Jackal团队，实验表明该系统能使智能体成功执行协作规划。