@inproceedings{bc79c61295a343b398420780c24d825a,
title = "Decentralized UAV Swarm Control for Multitarget Tracking using Approximate Dynamic Programming",
abstract = "We develop a decentralized control method for a UAV swarm for a multitarget tracking application using the theory of decentralized Markov decision processes (Dec-MDPs). This study develops a UAV control strategy to maximize the overall target tracking performance in a decentralized setting. Motivation for this case study comes from the surveillance applications using UAV swarms. Decision-theoretic approaches are very difficult to solve due to high dimensionality and being computationally expensive. We extend an approximate dynamic programming method called nominal belief-state optimization (NBO) to solve the UAV swarm control problem for target tracking application. We also implement a centralized MDP approach as a benchmark to compare the performance of the Dec-MDP approach.",
keywords = "ADP, Swarm intelligence, decentralized MDP, multitarget tracking",
author = "Azam, {Md Ali} and Shawon Dey and Mittelmann, {Hans D.} and Shankarachary Ragi",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE World AI IoT Congress, AIIoT 2021 ; Conference date: 10-05-2021 Through 13-05-2021",
year = "2021",
month = may,
day = "10",
doi = "10.1109/AIIoT52608.2021.9454229",
language = "English (US)",
series = "2021 IEEE World AI IoT Congress, AIIoT 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "457--461",
editor = "Rajashree Paul",
booktitle = "2021 IEEE World AI IoT Congress, AIIoT 2021",
}