Multiple Unmanned Aerial Vehicles Self-organizing Control Based On Particle Swarm Optimization

Realizing multi-UAVs self-organizing control can reduce the dependence of UAVs on infrastructures and improves the robustness of multi-UAVs systems.It is also one of the research hotspots in the field of multi-UAV cooperation.There are many aspects in self-organizing control of multi-UAVs.This thesis mainly uses particle swarm optimization to study the self-organizing control of multi-UAVs from the following three aspects: routing control,area coverage control and multi-task scheduling control.This thesis takes multi-UAVs self-organizing control as the research goal.Firstly,this thesis proposes an ad-hoc routing protocol for UAV Flying Ad-Hoc networks based on greedy forwarding and limited flooding;Secondly,this thesis proposes a multi-UAVs area coverage deployment algorithm based on particle swarm genetic algorithm.And in this part this thesis also proposes a multi-UAVs route recovery and coverage recovery method.Finally,this thesis proposes a multi-UAVs task scheduling control algorithm based on improved discrete particle swarm optimization.And in this part this thesis also proposes a multi-UAVs mission self-organizing bidding method based on contract mechanism.The innovation of this thesis is mainly reflected in the following three aspects:(1)Considering the characteristics of UAV Flying Ad-Hoc networks,this thesis proposes an ad-hoc routing protocol for UAV Flying Ad-Hoc networks based on greedy forwarding and flooding,and chaotic particle swarm optimization is used to optimize greedy forwarding,and distance-based limited flooding is used to optimize flood routing.The simulation results show that the routing protocol in this thesis has lower network delay and energy consumption.(2)In order to solve the problem that the particle swarm algorithm is easy to fall into the local extremum when solving the problem,the genetic algorithm is integrated into the iterative step of the particle swarm optimization algorithm.This thesis proposes a multi-UAVs area coverage deployment algorithm based on particle swarm genetic algorithm.At the same time,this thesis also proposes a multi-UAVs self-organizing route recovery and area coverage recovery method.The simulation results show that the proposed algorithm has higher efficiency,and the results also show that the recovery method in this thesis can ensure network connectivity and coverage,when some of the UAVs fail.(3)This thesis proposes a multi-UAVs task scheduling control algorithm based on improved discrete particle swarm optimization algorithm,which integrates the constraints of task scheduling control into the particle update process of discrete particle swarm.At the same time,this thesis also proposes a multi-UAVs mission self-organizing bidding method based on contract mechanism.The Simulation results show that the proposed algorithm has good convergence and optimization ability,and the results also show that the bidding method in this thesis can realize the self-organizing bidding of surviving drones for the remaining tasks,when the some UAVs are destroyed.