Large scale multi-robot coordination under network and geographical constraints

This paper addresses the problem of coordinating great numbers of vehicles in large geographical areas under network connective constraints. We leverage previous work on hierarchical potential fields to create advanced skills in multi-robot systems. Skills group together various field objectives to accomplish the performance requirements in response to highlevel commands. Our framework calculates trajectories that comply with priority constraints while optimizing the desired task objectives in their null spaces. We use a model-based dynamics approach that provides a direct map from field objectives to vehicle accelerations, yielding smooth and accurate trajectory generation. We develop a real-time software system that implements the proposed methods and simulates the coordinated behaviors in a 3D graphical environment. To validate the methodology, we simulate a large exploration task and demonstrate that we can effectively enforce the required constraints while optimizing the exploration goals.