期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 7, 期 2, 页码 4464-4471出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3148489
关键词
Routing; Task analysis; Robots; Resource management; Planning; Robot kinematics; Costs; Multi-robot systems; path planning for multiple mobile robots or agents; planning; scheduling and coordination
类别
资金
- Army Research Laboratory [W911NF-17-2-0181]
This study examines a class of multi-robot task allocation problems and proposes a hierarchical planner that breaks down the complexity of the problem into high-level and low-level subproblems. The results show that using a Graph Neural Network as a heuristic and a GNN-based estimator can provide an excellent trade-off between solution quality and computation time.
This letter considersa particular class of multi-robot task allocation problems, where tasks correspond to heterogeneous multi-robot routing problems defined on different areas of a given environment. We present a hierarchical planner that breaks down the complexity of this problem into two subproblems: the high-level problem of allocating robots to routing tasks, and the low-level problem of computing the actual routing paths for each subteam. The planner uses a Graph Neural Network (GNN) as a heuristic to estimate subteam performance for specific coalitions on specific routing tasks. It then iteratively refines the estimates to the real subteam performances as solutions of the low-level problems become availableon a testbed problem having a heterogeneous multi-robot area inspection problem as the base routing task, we empirically show that our hierarchical planner is able to compute optimal or near-optimal (within 7%) solutions approximately 16 times faster (on average) than an optimal baseline that computes plans for all the possible allocations in advance to obtain precise routing times. Furthermore, we show that a GNN-based estimator can provide an excellent trade-off between solution quality and computation time compared to other baseline (non-learned) estimators.
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