Robust UAV path planning using POMDP with limited FOV sensor
Christopher Eaton, Edwin K. P. Chong, Anthony A. Maciejewski · 2017 IEEE Conference on Control Technology and Applications (CCTA) · 2017
Significant development in path planning algorithms for unmanned aerial vehicles (UAVs) has been performed using numerous different methods. One such method, Partially Observable Markov Decision Processes (POMDP), has been used effectively for tracking fixed and moving targets. One limitation of those efforts has been the assumption that the UAVs could always see the targets, with a few unique exceptions, e.g., building obscuration. In reality, there will be times when a vehicle will not be able to observe a target due to constraints such as turn requirements or tracking multiple targets that are not within a single field of view (FOV). The POMDP formulation proposed in this paper is robust enough to handle those missed observations. Monte Carlo runs of 1000 iterations per configuration are run to provide statistical confidence in the performance of the algorithm. UAV altitude and sensor configuration are varied to show robustness across multiple configurations. A sensor with a limited FOV is assumed and changes in fixed look angle are evaluated. Changes in altitude provide results equivalent to changes in sensor window or focal length. Results show that the POMDP algorithm is capable of tracking single and multiple moving targets successfully with limited FOV sensors across a range of conditions.