Adaptive Fault Tolerant Execution of Multi-Robot Missions using Behavior Trees
This work addresses fault tolerance and performance issues for multi-robot teams, but it appears incremental as it builds on existing BT methods.
The paper tackled the problem of improving performance and fault tolerance in multi-robot missions by extending single-robot Behavior Trees (BTs) to multi-robot systems, combining built-in fallbacks with robot replacement capabilities and enabling parallel task execution.
Multi-robot teams offer possibilities of improved performance and fault tolerance, compared to single robot solutions. In this paper, we show how to realize those possibilities when starting from a single robot system controlled by a Behavior Tree (BT). By extending the single robot BT to a multi-robot BT, we are able to combine the fault tolerant properties of the BT, in terms of built-in fallbacks, with the fault tolerance inherent in multi-robot approaches, in terms of a faulty robot being replaced by another one. Furthermore, we improve performance by identifying and taking advantage of the opportunities of parallel task execution, that are present in the single robot BT. Analyzing the proposed approach, we present results regarding how mission performance is affected by minor faults (a robot losing one capability) as well as major faults (a robot losing all its capabilities). Finally, a detailed example is provided to illustrate the approach.