Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition

TL;DR

The paper introduces FlowDec, an efficient algorithm for heterogeneous multi-robot task allocation that decomposes the problem into homogeneous subproblems solved via min-cost flow, achieving near-optimal rewards with high computational efficiency.

Contribution

FlowDec provides a novel decomposition-based approach for heterogeneous task allocation, offering a 1/2 approximation guarantee and significantly faster performance than MILP methods.

Findings

FlowDec achieves at least 50% of the optimal reward.

The algorithm is several orders of magnitude faster than MILP approaches.

Simulation results demonstrate the efficiency and effectiveness of FlowDec.

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.