INSTITUTIONAL DIGITAL REPOSITORY

On learning multi-UAV policy for multi-object tracking and formation control

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dc.contributor.author Kaushik, P.
dc.contributor.author Garg, A.
dc.contributor.author Jha, S.S.
dc.date.accessioned 2022-08-25T15:25:10Z
dc.date.available 2022-08-25T15:25:10Z
dc.date.issued 2022-08-25
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/3902
dc.description.abstract Autonomous navigation and formation control of multi-UAV systems poses a significant challenge for the robotic systems that operate in partially-observable, dynamic and continuous environments. This paper addresses the problem of multi-UAV formation control while cooperatively tracking a set of moving objects. The objective of the multi-UAV system is to maintain the moving objects under their joint coverage along with aligning themselves in an optimal formation for maximizing the overall area coverage. We develop a multi-agent reinforcement learning model to learn a cooperative multi-UAV policy for the multi-object tracking and formation control. We design a reward function to encode the objectives of tracking, formation and collision avoidance into the model. The proposed deep reinforcement learning based model is deployed and tested against a baseline controller using the Gazebo simulator. The result indicates that the proposed model is robust against the tracking and alignment errors outperforming the baseline model. en_US
dc.language.iso en_US en_US
dc.subject Active tracking en_US
dc.subject Deep reinforcement learning en_US
dc.subject Formation control en_US
dc.subject Gazebo simulator en_US
dc.subject Unmanned aerial vehicles (UAVs) en_US
dc.title On learning multi-UAV policy for multi-object tracking and formation control en_US
dc.type Article en_US


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