Application of grazing-inspired guidance laws to autonomous information gathering

Domestic grazing animals follow simple, scalable rules to assign themselves trajectories to cover a pasture. We explain how to adapt these rules for an information gathering system based on a realistic robot motion model and Kalman-filter based evidence grid that accounts for both bandwidth and sensor limitations. Our results show that this algorithm can meet or exceed the performance of state of the art field robotics systems, particularly when scalability and robustness to failure are required.