UAV (Unmanned Aerial Vehicle) cluster obstacle avoidance method based on multi-target adaptive mutation pigeon-inspired optimization

An unmanned aerial vehicle, self-adaptive technology, applied in the direction of non-electric variable control, instrumentation, control/regulation system, etc.

Active Publication Date: 2018-11-16
BEIHANG UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It can be seen that UAV swarm obstacle avoidance control can be simplified as a multi-objective optimization problem, but the real-time performance of swarm obstacle avoidance puts forward higher requirements on the time complexity and convergence speed of the multi-objective optimization algorithm.

Method used

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  • UAV (Unmanned Aerial Vehicle) cluster obstacle avoidance method based on multi-target adaptive mutation pigeon-inspired optimization
  • UAV (Unmanned Aerial Vehicle) cluster obstacle avoidance method based on multi-target adaptive mutation pigeon-inspired optimization
  • UAV (Unmanned Aerial Vehicle) cluster obstacle avoidance method based on multi-target adaptive mutation pigeon-inspired optimization

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Embodiment Construction

[0080] See Figure 1 to Figure 7 , the effectiveness of the method proposed in the present invention is verified below through a specific example of UAV swarm obstacle avoidance control. The experimental computer configuration is Intel Core i7-6700HQ processor, 2.60Ghz main frequency, 16G memory, and the software is MATLAB 2014a version. The specific steps of the method are as follows:

[0081] Step 1: Initialize

[0082] Generate the initial state of 7 drones, that is, the initial position P of drones 1 to 7 1 to P 7 Respectively (20m, 80m, 69.2665m), (10m, 90m, 43.6688m), (10m, 70m, 11.5694m), (0m, 100m, 47.4858m), (0m, 60m, 77.5678m), (-10m ,110m,94.6116m) and (-10m,50m,82.8438m), the initial horizontal velocity Initial heading angle ψ i =0rad, the initial height change rate λ i =0m / s, initial weight w i =[1,1], where UAV number i=1,2,...,7, maximum horizontal velocity V xy_max =15m / s, minimum horizontal speed V xy_min =5m / s, maximum overload n max =10g, acceler...

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Abstract

The invention relates to a UAV cluster obstacle avoidance method based on multi-target adaptive mutation pigeon-inspired optimization. The method comprises the realization steps that 1) initializationis carried out; 2) expected UAV cluster speed change is determined according to the cluster weight; 3) the expected UAV obstacle avoidance speed is determined according to the obstacle avoidance weight; 4) multi-target adaptive mutation pigeon-inspired optimization is initialized; 5) the cluster and obstacle avoidance weights are calculated via multi-target adaptive mutation pigeon-inspired optimization; 6) a UAV model is run; and 7) whether to complete simulation is determined. The distributed UAV autonomous cluster obstacle avoidance control method can be used to make the UAV cluster more robust, more adaptive and simpler in the obstacle avoidance process, and thus, the cluster task execution capability in the complex environment is improved effectively.

Description

technical field [0001] The invention relates to an unmanned aerial vehicle group obstacle avoidance method based on multi-objective adaptive mutation pigeon group optimization, which belongs to the field of unmanned aerial vehicle control. Background technique [0002] UAV (Unmanned Aerial Vehicle, UAV) is an unmanned aerial vehicle that is controlled by its own program or radio remote control, and is used to perform specific tasks. It has the basic characteristics of "unmanned platform, manned system". When performing 3D missions - Dull, Dirty and Dangerous tasks, UAVs can show greater advantages than manned machines. [0003] Lanchester's law states that the combat power is equal to the total number of units participating in the battle multiplied by the combat efficiency of the unit. Therefore, the number of combat units is a more important determinant of the outcome of a war than the unit's combat capability. Therefore, UAV clusters can achieve a greater victory rate agai...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/10
CPCG05D1/104
Inventor 段海滨邱华鑫霍梦真杨庆张锡联杨之元邓亦敏鲜宁魏晨
Owner BEIHANG UNIV
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