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Path planning method for unmanned vehicles based on improved multi-objective particle swarm optimization 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: 2020-10-23
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 / DC, Multi-objective Particle Swarm Optimization Based on Decomposition and Continuous Mutation) is used to solve the path planning problem of intelligent unmanned vehicles in complex environments. The specific implementation steps include the following:

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  • Path planning method for unmanned vehicles based on improved multi-objective particle swarm optimization algorithm
  • Path planning method for unmanned vehicles based on improved multi-objective particle swarm optimization algorithm
  • Path planning method for unmanned vehicles based on improved multi-objective particle swarm optimization algorithm

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Embodiment Construction

[0043] 1. Introduction to the theoretical basis

[0044] 1. Multi-objective optimization problem

[0045]

[0046] 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:

[0047] Definition 1. Pareto dominates: Solution d,e∈Ω,d dominates e, denoted as: Meet the following two relations:

[0048]

[0049] Definition 2. Pareto optimal: If x is the Pareto optimal solution, then in Ω, Make Established.

[0050] Definition 3. Pareto optimal solution set (PS): Definition 4. Pareto Frontier (PF): PF={F(x)│xεPS}.

[0051] 2. Direction vector

[0052] Let the reference point be R(r 1 ,...,R m ), where r i =min{f i (x)|x∈Ω}, i=1, 2,...m, where 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 main steps of the invention are: environment modeling and objective function construction, inputting path parameters, initializing effective particle information, target space decomposition, calculating objective function values ​​and particle direction vectors, using new fitness value formulas to update particle classification, and calculating new The particle direction vector uses the information of the global optimal and individual optimal particles to generate a new generation of particles, and then loops. When the maximum number of iterations is reached, it jumps out and plans the optimal path for the unmanned vehicle based on the optimal solution set. The invention can quickly plan the optimal path suitable for the unmanned vehicle by using global or local position information in a complex environment and the state parameters of the undetermined 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 has been widely used in both the civilian industry and the national defense and military. At the same time, with the in-depth research of artificial intelligence, unmanned vehicle technology is also rapidly developing and updating, and the problem of path planning is particularly important in unmanned vehicle technology. The main task of path planning is to find in the environment of complex obstacles. A feasible and safe path from the start to the end. As an important part of intelligent unmanned vehicle driving technology, the pros and cons of path planning results directly determine the practicability and overal...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02
CPCG05D1/0214G05D1/0221G05D1/0276
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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