Self-optimization of resilient topologies for fallible multi-robots

Effective exchange of information in multi-robot systems is one of the grand challenges of today’s robotics. Here, we address the problem of simultaneously maximizing the (i) resilience to faults and (ii) area coverage of dynamic multi-robot topologies. We want to avoid the onset of single points of failure, i.e., situations in which the failure of a single robot causes the loss of connectivity in the overall network. Our methodology is based on (i) a three-fold control law and (ii) a distributed online optimization strategy that computes the optimal choice of control parameters for each robot. By doing so, connectivity is not only preserved, but also made resilient to failures as the network topology evolves. To assess the effectiveness of our approach, we ran experiments with a team of eight two-wheeled robots and we evaluated it against the injection of two separate classes of faults: communication and hardware failures. Results show that the proposed approach continues to perform as intended, even in the presence of these hazards.