Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection

Anna Guerra, Francesco Guidi, Davide Dardari, Petar M. Djuric

Abstract: In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a reinforcement learning (RL) algorithm to infer its own policy of navigation (i.e., the control law). Numerical results show the feasibility of the proposed idea, highlighting the UAV’s capability of autonomously exploring areas with high probability of target detection while reconstructing the surrounding environment.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 1004 - 1013
Cite this article: Guerra, Anna, Guidi, Francesco, Dardari, Davide, Djuric, Petar M., "Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 1004-1013.
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