Multi-robot teams keep missions running despite robot failures
MATE-BT: A Behavior-Tree-Based Framework for Hierarchically Decentralized Mission Autonomy in Heterogeneous Multi-Robot Teams
Robotics
Summary
Coordinating groups of different robots to complete missions can be tricky when things change or some robots break down. The authors designed MATE-BT, a system where one robot leads mission progress and assigns tasks, but if that leader fails, another robot takes over. Each robot uses easy-to-update behavior plans to adjust its actions locally. Tests with simulations and real robots showed missions continue smoothly even with new tasks or robot failures. This system also works efficiently onboard small computers, handling many robots and tasks quickly.
What this means in practice
- •For industrial automation teams: Coordinate heterogeneous robots on factory floors to adapt missions in real time despite equipment failures.
- •For drone fleet operators: Maintain mission progress and task assignment dynamically when drones encounter failures or new targets.
Authors
Sunbeen Lee, Hoseon Gi, Jik Hur, Inmo Jang
Abstract
Multi-robot teams executing predefined missions must adapt to runtime changes and sustain mission management despite robot failures. This paper proposes MATE-BT (Mission, Allocation, and Task Execution with Behavior Trees), a hierarchically decentralized framework that separates mission execution, allocation, and task execution. An active onboard leader manages mission progression and team-level task assignment, with authority transferable to another leader-capable robot after failure. Interchangeable decentralized methods resolve target assignments, while each robot adapts its local behavior through planning and acting with behavior trees. Webots simulations and real-robot experiments demonstrate mission continuity despite new targets and robot failures, including active-leader failure. In the physical experiments, all MATE-BT computation runs onboard. Hardware-in-the-loop resource measurements further support onboard execution on Jetson Orin Nano and Raspberry Pi 5. By updating only the affected layers without full-plan regeneration, MATE-BT maintained a response time of approximately 130 ms with 30 robots and 120 targets. Comparisons of four target-assignment plugins reveal scenario-dependent trade-offs between mission completion time and total travel distance.