Colorado Engineering Inc., United States
Keywords: UAVs, Mission Planning, Autonomous Operations, Artificial Intelligence, Genetic AlgorithmsHistorically, research shows that multi-vehicle, multiconstraint, surveillance problems require a combinatorial optimization solution. In many of these surveillance missions, the overall objective is to provide plans for surveillance tasks for UAVs (unmanned aerial vehicles) visiting, or “surveilling,” targets across geographically distributed areas. Surveillance plans are created with the goal of maximizing the number of targets the fleet of UAVs can surveil in a given period of time (in this paper, 24 hours) under a given set of constraints related to total energy usage (the energy available to each UAV).. Here, we present a bi-objective task planning genetic algorithm (GA) that provides a Pareto set of near optimal surveillance plans, given the above conflicting energy and surveillance objectives. Future work will expand these algorithms to support multi-objective mission planning, including speed, distance, weather conditions, and other factors that would affect the overall surveillance opportunities.