Task and Motion Planning combines high-level task sequencing (what to do) with low-level motion planning (how to do it) to generate feasible, collision-free execution plans. However, in many real-world domains, such as automated warehouses, tasks are predefined, shifting the challenge to if, when, and how to execute them safely and efficiently under resource, time and motion constraints. In this paper, we formalize this as the Scheduling and Motion Planning problem for multi-object navigation in shared workspaces. We propose a novel solution framework that interleaves off-the-shelf schedulers and motion planners in an incremental learning loop. The scheduler generates candidate plans, while the motion planner checks feasibility and returns symbolic feedback, i.e., spatial conflicts and timing adjustments, to guide the scheduler towards motion-feasible solutions. We validate our proposal on logistics and job-shop scheduling benchmarks augmented with motion tasks, using state-of-the-art schedulers and sampling-based motion planners. Our results show the effectiveness of our framework in generating valid plans under complex temporal and spatial constraints, where synchronized motion is critical.

翻译：任务与运动规划结合了高层任务排序（做什么）与低层运动规划（如何做），以生成可行、无碰撞的执行计划。然而，在许多现实领域（如自动化仓库）中，任务是预定义的，挑战转变为在资源、时间和运动约束下，是否、何时以及如何安全高效地执行这些任务。本文将其形式化为共享工作空间中多目标导航的调度与运动规划问题。我们提出了一种新颖的解决方案框架，该框架将现成的调度器与运动规划器交错置于增量学习循环中。调度器生成候选计划，而运动规划器检查可行性并返回符号反馈（即空间冲突与时间调整），以引导调度器寻找运动可行的解。我们在添加了运动任务的物流与作业车间调度基准上，使用最先进的调度器和基于采样的运动规划器验证了所提方法。结果表明，在同步运动至关重要的复杂时空约束下，我们的框架能有效生成有效计划。