The optimism principle: A unified framework for optimal robotic network deployment in an unknown obstructed environment

We consider the problem of deploying a team of robots in an unknown, obstructed environment to form a multi-hop communication network. As a solution, we present a unified framework, onLinE rObotic Network formAtion (LEONA), that is general enough to permit optimizing the communication network for different utility functions in non-convex environments. LEONA adopts the principle of “optimism in the face of uncertainty” to allow the team of robots to form optimal network configurations efficiently and rapidly without having to map link qualities in the entire area. We demonstrate and evaluate this framework on two specific scenarios concerning the formation of a multi-hop communication path between fixed end-points: one minimizing the total path cost, and another maximizing the bottleneck communication rate. Our simulation-based evaluation shows that the use of the optimism principle can significantly reduce resources spent in exploring and mapping the entire region prior to network optimization. We also present a mathematical modeling of how the searched area scales with various relevant parameters in each case.