Open AccessContinuous patrolling in uncertain environment with the UAV swarm
Author(s) -
Xin Zhou,
Weiping Wang,
Tao Wang,
Xiaobo Li,
Tian Jing
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0202328
Subject(s) - patrolling , swarm behaviour , computer science , partially observable markov decision process , swarm intelligence , heuristic , task (project management) , scalability , computation , particle swarm optimization , distributed computing , mathematical optimization , real time computing , markov chain , artificial intelligence , algorithm , machine learning , markov model , engineering , mathematics , systems engineering , database , political science , law
The research about unmanned aerial vehicle (UAV) swarm has developed rapidly in recent years, especially the UAV swarm with sensors which is becoming common means of achieving situational awareness. Due to inadequate researches of the UAV swarm with complex control structure currently, we propose a patrolling task planning algorithm for the UAV swarm with double-layer centralized control structure under the uncertain and dynamic environment. The main objective of the UAV swarm is to collect environment information as much as possible. To summarized, the primary contributions of this paper are as follows. We first define the patrolling problem. After that, the patrolling problem is modeled as the Partially Observable Markov Decision Process (POMDP) problem. Building upon this, we put forward a myopic and scalable online task planning algorithm. The algorithm contains online heuristic function, sequential allocation method, and the mechanism of bottom-up information flow and top-down command flow, reducing the computation complexity effectively. Moreover, as the number of control layers increases, this algorithm guarantees the performance without increasing the computation complexity for the swarm leader. Finally, we empirically evaluate our algorithm in the specific scenarios.