Human aware UAS path planning in urban environments using nonstationary MDPs

Rakshit Allamaraju, Hassan Kingravi, Allan Axelrod, Girish Chowdhary, Robert Grande, Jonathan P. How, Christopher Crick, Weihua Sheng

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Article number6907000
Pages (from-to)1161-1167
Number of pages7
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - Sep 22 2014
Externally publishedYes
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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