Method of planning three dimensional route of unmanned plane by means of improved artificial fish swarm algorithm

An artificial fish swarm algorithm and UAV technology, applied in the field of UAV threat prediction, can solve the threat assessment without considering the influence of time factors, the high complexity of the UAV trajectory planning algorithm, and the re-planning of the trajectory. It does not meet the actual impact and other problems, and achieves the effect of meeting the actual track planning requirements, improving the search ability, and reducing the complexity.

Inactive Publication Date: 2017-11-24
NANCHANG HANGKONG UNIVERSITY
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Problems solved by technology

[0004] The algorithm complexity of the above-mentioned UAV track planning is high, and the application of the particle swarm algorithm is easy to fall into a local op...

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  • Method of planning three dimensional route of unmanned plane by means of improved artificial fish swarm algorithm

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[0034] The present invention will be further described in detail in conjunction with the accompanying drawings, theoretical analysis and simulation experiments. The invention proposes a method for planning a three-dimensional track of an unmanned aerial vehicle by using an improved artificial fish swarm algorithm. Construct digital maps through terrain models and simplified threat models, and realize spatial segmentation based on fence adaptive algorithms; apply improved artificial fish swarm algorithm to realize static track planning; construct threat prediction models based on dynamic Bayesian, to deal with sudden threats Make predictions, consider UAV flight parameters, obtain the re-planning starting point, and apply the improved artificial fish algorithm to realize dynamic re-planning of the trajectory. Specific steps are as follows:

[0035] Step S1: Construct a digital map, and realize spatial segmentation according to the fence adaptive algorithm. details as follows:...

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Abstract

The invention discloses a method of planning three dimensional route of an unmanned plane by means of an improved artificial fish swarm algorithm. The method of planning three dimensional route of an unmanned plane by means of an improved artificial fish swarm algorithm is used for carrying out static state planning of route and real-time dynamic re-planning of route when an unmanned plane executes a single task. The method of planning three dimensional route of an unmanned plane by means of an improved artificial fish swarm algorithm includes the steps: constructing a digital map through a landform model and a simplified threat model, considering the influence of space division granularity on the complexity of an optimizing control algorithm, and realizing division of space according to a fence self-adaptive algorithm; realizing static state route planning by means of an improved artificial fish swarm algorithm; and considering the time factor, constructing a threat prediction model based on a dynamic Bayesian network, predicting the unexpected threat, combined with flight constraint of the unmanned plane, obtaining the re-planning starting point, and realizing global route dynamic re-planning by means of the improved artificial fish swarm algorithm. The method of planning three dimensional route of an unmanned plane by means of an improved artificial fish swarm algorithm has the advantages of reducing the complexity of a route optimizing control algorithm, improving the optimum route searching capability, and satisfying the practical route planning demand.

Description

technical field [0001] The invention relates to the field of track planning of unmanned aerial vehicles, and mainly relates to the application of a bionic optimization algorithm to realize the static planning of the track of the unmanned aerial vehicle and the threat prediction of the unmanned aerial vehicle. Background technique [0002] UAV (Unmanned Acrial Vehicle, UAV) trajectory planning is to plan a flight trajectory that satisfies its own constraints and avoids threats according to mission objectives, and is one of the key technologies in the autonomous flight of UAVs. [0003] Research on such problems has been carried out at home and abroad, and many trajectory planning algorithms have been proposed, such as A-Start algorithm, genetic algorithm, ant colony algorithm, etc. Large, so that the track search has problems such as excessive calculation, low efficiency, poor optimization ability, etc., which makes the group optimization algorithm such as particle swarm favo...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00G05D1/10
CPCG06Q10/04G05D1/101G06N3/006
Inventor 刘琳岚许江波骆雄辉舒坚
Owner NANCHANG HANGKONG UNIVERSITY
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