Name
Abstract | ||
|---|---|---|
In recent years, with the development of the unmanned aerial vehicle (UAV) and battlefield environments, the UAV swarm has attracted significant research attention. To solve problems regarding poor state consensus among swarm individuals due to a small number of individuals easily falling into local minima upon encountering an obstacle, this paper proposes a flocking obstacle avoidance algorithm with local interaction of obstacle information. To make the UAV swarm follow the desired trajectory with better state consensus, we improved the flocking control algorithm of agents according to the characteristics and requirements of the UAV swarm. The obstacle avoidance algorithm for the UAV swarm is based on Olfati-Saber's multi-agent obstacle avoidance algorithm. The proposed method has individuals in the swarm communicate obstacle information with their neighbors, and we present a simple analysis of this method. The method improves the cooperative obstacle avoidance capability of the flocking control algorithm. The simulation results showed that the proposed flocking control algorithm provides a better tracking effect and consensus for the UAV swarm when avoiding obstacles. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2895643 | IEEE ACCESS |
Keywords | Field | DocType |
Fixed-wing UAV swarm,multi-agent system,flocking control,cooperative obstacle avoidance,consensus,local information communication | Obstacle avoidance,Flocking (texture),Obstacle,Wing,Battlefield,Swarm behaviour,Computer science,Maxima and minima,Real-time computing,Trajectory,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Zhao | 1 | 1 | 0.36 |
| Hairong Chu | 2 | 5 | 1.93 |
| Mingyue Zhang | 3 | 4 | 4.14 |
| Tingting Sun | 4 | 6 | 5.56 |
| Lihong Guo | 5 | 294 | 11.57 |