Title: Hierarchical Temporal Logic Task and Motion Planning for Multi-Robot Systems Show Chinese title

Title: 多机器人系统的分层时间逻辑任务与运动规划

Abstract: Task and motion planning (TAMP) for multi-robot systems, which integrates discrete task planning with continuous motion planning, remains a challenging problem in robotics. Existing TAMP approaches often struggle to scale effectively for multi-robot systems with complex specifications, leading to infeasible solutions and prolonged computation times. This work addresses the TAMP problem in multi-robot settings where tasks are specified using expressive hierarchical temporal logic and task assignments are not pre-determined. Our approach leverages the efficiency of hierarchical temporal logic specifications for task-level planning and the optimization-based graph of convex sets method for motion-level planning, integrating them within a product graph framework. At the task level, we convert hierarchical temporal logic specifications into a single graph, embedding task allocation within its edges. At the motion level, we represent the feasible motions of multiple robots through convex sets in the configuration space, guided by a sampling-based motion planner. This formulation allows us to define the TAMP problem as a shortest path search within the product graph, where efficient convex optimization techniques can be applied. We prove that our approach is both sound and complete under mild assumptions. Additionally, we extend our framework to cooperative pick-and-place tasks involving object handovers between robots. We evaluate our method across various high-dimensional multi-robot scenarios, including simulated and real-world environments with quadrupeds, robotic arms, and automated conveyor systems. Our results show that our approach outperforms existing methods in execution time and solution optimality while effectively scaling with task complexity. Show Chinese abstract

Abstract: 多机器人系统的任务与运动规划（TAMP），即整合离散任务规划与连续运动规划，仍然是机器人领域的一个难题。 现有的TAMP方法往往难以有效扩展到具有复杂规格的多机器人系统中，导致解决方案不可行且计算时间延长。 本文解决了在多机器人环境中任务由表达性强的分层时序逻辑指定且任务分配未预先确定的TAMP问题。 我们的方法利用了分层时序逻辑规范在任务级规划中的效率以及基于凸集优化的图法在运动级规划中的优势，并在乘积图框架内集成两者。 在任务级别上，我们将分层时序逻辑规范转换为单个图，将任务分配嵌入其边中。 在运动级别上，我们通过配置空间中的凸集表示多个机器人的可行运动，并由基于采样的运动规划器引导。 这种表述使我们能够将TAMP问题定义为乘积图中的最短路径搜索，在其中可以应用高效的凸优化技术。 我们在温和假设下证明了我们的方法既可靠又完整。 此外，我们将框架扩展到涉及机器人之间物体交接的合作拾取和放置任务。 我们在各种高维多机器人场景中评估了我们的方法，包括带有四足机器人、机械臂和自动输送系统的模拟和真实环境。 我们的结果显示，我们的方法在执行时间和解决方案最优性方面优于现有方法，同时有效应对任务复杂性。