ICUAS 2020 Paper Abstract


Paper ThA3.1

Bhagat, Sarthak (IIIT Delhi), P B, Sujit (IISER Bhopal)

UAV Target Tracking in Urban Environments Using Deep Reinforcement Learning

Scheduled for presentation during the Regular Session "UAS Applications I" (ThA3), Thursday, September 3, 2020, 10:00−10:20, Edessa

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on September 25, 2020

Keywords UAS Applications, Autonomy, Simulation


Persistent target tracking in urban environments using UAV is a difficult task due to the limited field of view, visibility obstruction from obstacles, and uncertain target motion. The vehicle needs to plan intelligently in 3D such that the target visibility is maximized. In this paper, we introduce Target Following DQN (TF-DQN), a deep reinforcement learning technique based on Deep Q-Networks with a curriculum training framework for the UAV to persistently track the target in the presence of obstacles and target motion uncertainty. The algorithm is evaluated through simulations. The results show that the UAV tracks the target persistently in diverse environments while avoiding obstacles on the trained environments as well as on unseen environments.



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