Addressing uncertainty in UAV navigation decision-making

We present an analytic framework for modeling and measuring uncertainty for the scenario of unmanned aerial vehicles (UAVs) cooperatively searching for a moving target. Uncertainty exists in a UAVs assessment of teammate locations, target locations, and sensor results. As is frequently done, our framework employs probabilistic maps to represent uncertain information regarding the UAVs environment. We present new methods to update the probabilistic maps when information arrives from onboard sensors or teammate UAVs. When new information is missing or delayed, we propose a novel and straightforward diffusion approach to update probabilistic maps. The UAVs make navigation decisions based on response to potential fields generated by the probabilistic maps. Since map data have uncertainty, this leads to decision-making in uncertainty. We conclude by describing how uncertainty in the environment translates into a unique measure, velocity vector dispersion (D_V), which describes the uncertainty in the UAVs navigation decision. Thresholds related to D_V may be useful to guide real-time decision policies. We present simulation results that show how the use of diffusion affects the time to locate targets. We also describe how D_V varies during UAV flight and comment on its utility.