Unmanned vehicle path planning method based on improved multi-objective particle swarm algorithm

A multi-objective particle swarm and path planning technology, applied in vehicle position/route/height control, motor vehicles, two-dimensional position/course control, etc.

Active Publication Date: 2018-05-04
北京艾上智能科技有限公司
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Problems solved by technology

[0004] The method of the present invention aims at the deficiencies of the prior art, and proposes an unmanned vehicle path planning method based on an improved multi-objective particle swarm algorithm. The particle swarm optimization method (MOPSO/D

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  • Unmanned vehicle path planning method based on improved multi-objective particle swarm algorithm
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  • Unmanned vehicle path planning method based on improved multi-objective particle swarm algorithm

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[0039] 1. Introduction to the theoretical basis

[0040] 1. Multi-objective optimization problem

[0041]

[0042] Where x=(x 1 ,x 2 ,...x n ) Is the n-dimensional decision variable, m is the number of objective functions, g(x) is the q inequality constraints of the objective function, h(x) is the p equality constraints of the objective function, all these decision variables satisfy the conditions Expressed by the set Ω, Y={F(x)|x∈Ω} is the target space. Next are four important definitions of multi-objective problems:

[0043] Definition 1. Pareto dominates: solution d,e∈Ω,d dominates e, denoted as: Meet the following two relations:

[0044]

[0045] Definition 2. Pareto optimal: If x is the Pareto optimal solution, then in Ω, Make Established.

[0046] Definition 3. Pareto optimal solution set (PS):

[0047] Definition 4. Pareto Frontier (PF): PF={F(x)│xεPS}.

[0048] 2. Direction vector

[0049] Let the reference point be R(r 1 ,...,R m ), where r i =min{fi i (x)|x∈Ω}, i=1, 2,...m,...

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Abstract

The invention belongs to the technical field of intelligent unmanned vehicles and control, and in particular relates to an unmanned vehicle path planning method based on an improved multi-objective particle swarm algorithm. The method mainly comprises the steps of performing environment modeling and objective function construction, inputting path parameters, initializing information on effective particles, performing target space decomposition, calculating objective function values and particle direction vectors, using new adaptive value formulas for particle classification update, calculatingnew particle direction vectors, using information on global optimum and individual optimal particles to generate a new generation of particles, and performing circulation; when the maximum number ofiterations is reached, stepping out, and planning an optimal unmanned vehicle path according to an optimal solution set. According to the unmanned vehicle path planning method, the optimal path suitable for the unmanned vehicle driving can be quickly planned by using global or local position information in the complex environment and state parameters of a pending unmanned vehicle.

Description

technical field [0001] The invention belongs to the technical field of intelligent unmanned vehicles and control, and in particular relates to an unmanned vehicle path planning method based on an improved multi-objective particle swarm algorithm. Background technique [0002] Unmanned vehicle technology has attracted much attention in the current industry 4.0 era, and it has a wide range of applications in both civil industry and national defense. At the same time, with the in-depth research of artificial intelligence, unmanned vehicle technology is also developing and updating rapidly, and the problem of path planning is particularly important in unmanned vehicle technology. The main task of path planning is to find obstacles in the complex environment. A viable, safe path from origin to destination. As an important part of the driving technology of intelligent unmanned vehicles, the quality of path planning results directly determines the practicability and overall perfor...

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0214G05D1/0221G05D1/0276G05D2201/02
Inventor 葛洪伟钱小宇葛阳
Owner 北京艾上智能科技有限公司
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