Heterogeneous Multi-robot Task Allocation for Long-Endurance Missions in Dynamic Scenarios

TL;DR

This paper introduces a comprehensive framework for heterogeneous multi-robot task allocation in long-endurance, dynamic scenarios, incorporating recharging, task fragmentation, and coalition tasks, with theoretical analysis and practical algorithms.

Contribution

It formulates a new class of heterogeneous MRTA problems, provides an optimal MILP model, and develops a heuristic and replanning framework for real-time adaptive mission execution.

Findings

The MILP formulation effectively models complex heterogeneous MRTA scenarios.

The heuristic algorithm provides near-optimal solutions efficiently.

The replanning framework successfully adapts to unexpected events online.

Abstract

We present a framework for Multi-Robot Task Allocation (MRTA) in heterogeneous teams performing long-endurance missions in dynamic scenarios. Given the limited battery of robots, especially for aerial vehicles, we allow for robot recharges and the possibility of fragmenting and/or relaying certain tasks. We also address tasks that must be performed by a coalition of robots in a coordinated manner. Given these features, we introduce a new class of heterogeneous MRTA problems which we analyze theoretically and optimally formulate as a Mixed-Integer Linear Program. We then contribute a heuristic algorithm to compute approximate solutions and integrate it into a mission planning and execution architecture capable of reacting to unexpected events by repairing or recomputing plans online. Our experimental results show the relevance of our newly formulated problem in a realistic use case for inspection with aerial robots. We assess the performance of our heuristic solver in comparison with other variants and with exact optimal solutions in small-scale scenarios. In addition, we evaluate the ability of our replanning framework to repair plans online.