Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition
This addresses efficient task allocation for multi-robot systems with heterogeneous capabilities, offering a practical solution for applications like patrolling and delivery, though it is incremental as it builds on existing flow-based methods.
The paper tackles the problem of heterogeneous task allocation in multi-robot systems, presenting the FlowDec algorithm that achieves at least half of the optimal reward and is orders of magnitude faster than a MILP approach in simulations.
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.