DynFloR: A Flow Approach for Data Delivery Optimization in Multi-Robot Network Patrolling

Deploying fleets of mobile robots in real scenarios and environments raises several scientific challenges. One of them concerns the ability of the robots to adapt to the dynamics of their environment. We introduce DynFloR, a dynamic network flow based approach for finding optimal policies for data delivery in multi-robot network patrolling where the robots can communicate instantly and free of charge one to another when they meet, there is a periodicity of the robot meetings and the distribution of the data collected during the patrol is regular. Experiments on randomly generated synthetic examples are performed for evaluating the performance of the DynFloR method. The performed experiments empirically show that independent of the problem setting (such as number of robots, memory of the robots) the amount of data transferred to a base station per unit of time converges to an equilibrium state. The case of lost data has been also examined through various experiments, but it requires further experimentation as well as in-depth analysis.