Assessing Reputation to Improve Team Performance in Heterogeneous Multi-Robot Coverage

When agents in a multi-robot team have limited knowledge about their relative performance, their teammates, or the environment, robots must observe individual performance variations and adapt accordingly. We propose robot reputation to assess the historical performance of agents and make future adaptations in a persistent coverage task. We consider a heterogeneous multi-robot team, where robots are equipped with different capabilities to serve discrete events in an environment. We utilize a heterogeneous coverage control approach to partition the space according to robot capabilities and the estimated probability density, such that the robot is responsible for serving the events in its assigned region. As the team serves events, we assign each robot a reputation, which is then used to adjust the size of a robot’s region, thus adjusting the amount of space a robot is responsible for serving. Our simulations show that using reputation to weigh the size of the Voronoi cells outperforms the case where we neglect reputation.