A Balking Queue Approach for Modeling Human-Multi-Robot Interaction for Water Monitoring

We consider multi-robot scenarios where robots ask for operator interventions when facing difficulties. As the number of robots increases, the operator quickly becomes a bottleneck for the system. Queue theory can be effectively used to optimize the scheduling of the robots’ requests. Here we focus on a specific queuing model in which the robots decide whether to join the queue or balk based on a threshold value. Those thresholds are a trade-off between the reward earned by joining the queue and cost of waiting in the queue. Though such queuing models reduce the system’s waiting time, the cost of balking usually is not considered. Our aim is thus to find appropriate balking strategies for a robotic application to reduce the waiting time considering the expected balking costs. We propose using a Q-learning approach to compute balking thresholds and experimentally demonstrate the improvement of team performance compared to previous queuing models.