Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition
Abstract
Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering significantly higher performance than single-robot systems. A fundamental building block in most multi-robot systems is task allocation: assigning robots to tasks (e.g., patrolling an area, or servicing a transportation request) as they appear based on the robots' states to maximize reward. In many practical situations, the allocation must account for heterogeneous capabilities (e.g., availability of appropriate sensors or actuators) to ensure the feasibility of execution, and to promote a higher reward, over a long time horizon. To this end, we present the FlowDec algorithm for efficient heterogeneous task-allocation achieving an approximation factor of at least 1/2 of the optimal reward. Our approach decomposes the heterogeneous problem into several homogeneous subproblems that can be solved efficiently using min-cost flow. Through simulation experiments, we show that our algorithm is faster by several orders of magnitude than a MILP approach.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2020
- DOI:
- 10.48550/arXiv.2011.03603
- arXiv:
- arXiv:2011.03603
- Bibcode:
- 2020arXiv201103603S
- Keywords:
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- Computer Science - Robotics;
- Computer Science - Data Structures and Algorithms;
- Computer Science - Multiagent Systems;
- Electrical Engineering and Systems Science - Systems and Control
- E-Print:
- Extended version of a conference paper that appeared in the International Conference on Robotics and Automation (ICRA), 2021