Synchronized Multi-Arm Rearrangement Guided by Mode Graphs with Capacity Constraints
This addresses task planning for multi-arm robotic systems, offering incremental improvements in efficiency for industrial or logistics automation.
The paper tackles the scalability challenge of coordinating multiple robotic arms and objects in rearrangement tasks by connecting it to multi-body path planning on graphs with capacity constraints, resulting in a heuristic that achieves good scalability and fast, high-quality solutions with anytime behavior.
Solving task planning problems involving multiple objects and multiple robotic arms poses scalability challenges. Such problems involve not only coordinating multiple high-DoF arms, but also searching through possible sequences of actions including object placements, and handoffs. The current work identifies a useful connection between multi-arm rearrangement and recent results in multi-body path planning on graphs with vertex capacity constraints. Solving a synchronized multi-arm rearrangement at a high-level involves reasoning over a modal graph, where nodes correspond to stable object placements and object transfer states by the arms. Edges of this graph correspond to pick, placement and handoff operations. The objects can be viewed as pebbles moving over this graph, which has capacity constraints. For instance, each arm can carry a single object but placement locations can accumulate many objects. Efficient integer linear programming-based solvers have been proposed for the corresponding pebble problem. The current work proposes a heuristic to guide the task planning process for synchronized multi-arm rearrangement. Results indicate good scalability to multiple arms and objects, and an algorithm that can find high-quality solutions fast and exhibiting desirable anytime behavior.