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This paper addresses the Scheduling and Motion Planning problem, focusing on optimizing the execution of predefined tasks under resource, time, and motion constraints in multi-object navigation scenarios. They introduce a framework that iteratively combines scheduling and motion planning, where a scheduler proposes plans and a motion planner provides symbolic feedback on feasibility, including spatial conflicts and timing adjustments. Experiments on logistics and job-shop scheduling benchmarks demonstrate the framework's ability to generate valid plans under complex constraints where synchronized motion is crucial.
Achieve efficient task execution in shared workspaces by interleaving scheduling and motion planning, using symbolic feedback to guide the scheduler towards motion-feasible solutions.
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.
