Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection

Author(s):  
Daniel H. Stolfi ◽  
Matthias R. Brust ◽  
Grégoire Danoy ◽  
Pascal Bouvry
Keyword(s):  
Author(s):  
Daniel H. Stolfi ◽  
Matthias R. Brust ◽  
Grégoire Danoy ◽  
Pascal Bouvry
Keyword(s):  

2013 ◽  
Author(s):  
Shrawan Kumar ◽  
Arun Pratap Srivastava

Author(s):  
Ning Gao ◽  
Xiao Li ◽  
Shi Jin ◽  
Michail Matthaiou

Author(s):  
Gunasekaran Raja ◽  
Kottilingam Kottursamy ◽  
Ajay Theetharappan ◽  
Korhan Cengiz ◽  
Aishwarya Ganapathisubramaniyan ◽  
...  

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 91
Author(s):  
Md Ali Azam ◽  
Hans D. Mittelmann ◽  
Shankarachary Ragi

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.


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