Name
Title | ||
|---|---|---|
Dynamic Control Scheme of Multiswarm Persistent Surveillance in a Changing Environment. |
Abstract | ||
|---|---|---|
The persistent surveillance problem has been proved to be an NP hard problem for multiple unmanned aerial vehicle systems (UAVs). However, most studies in multiple UAV control focus on control cooperative path planning in a single swarm, while dynamic deployment of a multiswarm system is neglected. This paper proposes a collective control scheme to drive a multiswarm UAVs system to spread out over a time-sensible environment to provide persistent adaptive sensor coverage in event-related surveillance scenarios. We design the digital turf model to approximate the mixture information of mission requirements and surveillance reward. Moreover, we design a data clustering-based algorithm for the dynamic assignment of UAV swarms, which can promote workload balance, while also allowing real-time response to emergencies. Finally, we evaluate the proposed architecture by means of simulation and find that our method is superior to the conventional control strategy in terms of detection efficiency and subswarm equilibrium degree. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1155/2019/6025657 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
Field | DocType | Volume |
Motion planning,Architecture,Software deployment,Swarm behaviour,Computer science,Workload,Artificial intelligence,Cluster analysis,Machine learning,Distributed computing | Journal | 2019 |
ISSN | Citations | PageRank |
1687-5265 | 0 | 0.34 |
References | Authors | |
6 | 5 | |