Hierarchical Weighting Vicsek Model for Flocking Navigation of Drones
Hierarchical Weighting Vicsek Model for Flocking Navigation of Drones
- New search for: Xingyu Liu
- New search for: Xiaojia Xiang
- New search for: Yuan Chang
- New search for: Chao Yan
- New search for: Han Zhou
- New search for: Dengqing Tang
- New search for: Xingyu Liu
- New search for: Xiaojia Xiang
- New search for: Yuan Chang
- New search for: Chao Yan
- New search for: Han Zhou
- New search for: Dengqing Tang
Flocking navigation, involving alignment-guaranteed path following and collision avoidance against obstacles, remains to be a challenging task for drones. In this paper, we investigate how to implement flocking navigation when only one drone in the swarm masters the predetermined path, instead of all drones mastering their routes. Specifically, this paper proposes a hierarchical weighting Vicsek model (WVEM), which consists of a hierarchical weighting mechanism and a layer regulation mechanism. Based on the hierarchical mechanism, all drones are divided into three layers and the drones at different layers are assigned with different weights to guarantee the convergence speed of alignment. The layer regulation mechanism is developed to realize a more flexible obstacle avoidance. We analyze the influence of the WVEM parameters such as weighting value and interaction radius, and demonstrate the flocking navigation performance through a series of simulation experiments.
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Hierarchical Weighting Vicsek Model for Flocking Navigation of Drones
2021
Article (Journal)
Electronic Resource
Unknown
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