Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

翻译：在符号推理与几何推理相结合的多智能体系统中，因智能体与环境间的交互导致的规划、调度及同步问题是一项具有挑战性的任务。现有研究忽视了此类系统固有的任务持续时间与几何可行性可变性。我们提出一种结合任务与运动的规划方法，以优化在时空可变性下的任务排序、分配与执行。该框架通过解耦任务与动作（动作是符号任务的一种可能几何实现）实现：在任务层面，基于时间线的规划处理时间约束、持续时间可变性及任务的协同分配；在动作层面，在线运动规划针对实际移动进行规划以应对环境变化。我们在协作制造场景中验证了该方法的有效性——机械臂与人类工人需以最短时间协作完成马赛克拼装。与现有研究相比，我们的方法适用于更广泛的应用场景，并显著缩短了过程执行时间。