Table 2.
Self-Organizing distributed algorithms.
Ref. | Performed Function | Technique Deployed |
---|---|---|
[66] | Deployed to perform distributed cooperative search in Multi-UAV system | Implements the distributed decision making based on receding horizon techniques |
[67] | Resolve slot access problem for neighbor UAV swarm network cooperation | Implements a collision discovery method to ensure slot access is not delayed by topology information exchanges |
[68] | Implements self-organized collision avoidance in autonomous UAVs | Algorithm Compute’s safe reaction distance on which UAVs begin collision avoidance movement |
[69] | Tackles sensing coverage constraints in multi-UAV swarm operations | Reciprocal decision-based approach performed between neighbor UAVs to reduce trajectory oscillations |
[70] | Provides formation control for UAV and Mobile Robots communication networks | Distributed asymmetric control to implement formation control based on ‘mergeable nervous systems’ approach |
[71] | Plays the role of implementing distributed control laws in swarm indexing and position-free density control | Pseudo-localization method used to localize agents to a new coordination frame with distributed control policies for desired coverage extensions |
[72] | Solves limitations in transmission extensions in UAV wireless networks | Deploys relay selection techniques to improve coverage transmission constraints |
[73] | Provides solution for slot access transmission problem in UAV Swarms | Makes use of game model as collision detection method to derive feedback in a common control channel for access slots |
[74] | Performs flock distribution for UAVs to fly and coordinate as a unit | UAVs coordinates without a leader and on constant basis broadcast and as well receive movement information to share their common goal |
[75] | Deployed to perform formation control in multi-UAV system | Formulates formation solution based on circle and arbitrary polygon formations techniques |