Human aware UAS path planning in urban environments using nonstationary MDPs
Rakshit Allamaraju, Hassan A. Kingravi, Allan M. Axelrod, Girish Vinayak Chowdhary, Robert C. Grande, Jonathan P. How, Christopher Crick, Weihua Sheng · 2014
A growing concern with deploying Unmanned Aerial Vehicles (UAVs) in urban environments is the potential violation of human privacy, and the backlash this could entail. Therefore, there is a need for UAV path planning algorithms that minimize the likelihood of invading human privacy. We formulate the problem of human-aware path planning as a nonstationary Markov Decision Process, and provide a novel model-based reinforcement learning solution that leverages Gaussian process clustering. Our algorithm is flexible enough to accommodate changes in human population densities by employing Bayesian nonparametrics, and is real-time computable. The approach is validated experimentally on a large-scale long duration experiment with both simulated and real UAVs.