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Swarm intelligence optimization algorithm in the path planning

Author: MaQianZhi
Tutor: LeiXiuJuan
School: Shaanxi Normal University
Course: Applied Computer Technology
Keywords: path planning swarm intelligence ant colony optimization algorithm particle swarm optimization algorithm artificial bee colony algorithm artificial fish swarm algorithm
CLC: O221
Type: Master's thesis
Year: 2010
Downloads: 264
Quote: 1
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Abstract

Path planning is to find a trajectory from the initial point to the target point which satisfies a certain performance optimal index under some constraints. The quality of path planning usually has an immense impact for the task, therefore, the study on path planning become the basis of the research in the relative field.Swarm intelligence optimization algorithm resource from simulating the group behavior of creature. It is a class of optimization algorithm that base on groups and can self-adaptive search. The swarm intelligence optimization algorithm include:ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and artificial fish swarm algorithm. It has aroused the universal concern because of its characteristic of simple theory, easier operation, and no special requirements for optimized function. In this paper, several swarm intelligence optimization algorithm were applied in solving path planning problem in different fields.At first, the significance of the research and the status of study at home and abroad were analyzed, and the path planning-related issues and traditional path planning methods were introduced, then the methods based on several swarm optimization algorithms were given to solve the static environment and dynamic path planning under uncertain environment:Ant colony optimization algorithm was applied in the robotic global path planning. The environment of the robot was described using grid method for modeling, and the application design method of ant colony algorithm was introduced, then we used the basic ant colony optimization algorithm and the two species of improved ant colony optimization algorithm to simulate in environment of different sizes and densities of obstacles.For the path planning in a static complex environment, considering from two angles of solving constrained optimization problems, different fitness evaluation functions were designed according to the characteristics for robot path planning, and two kinds of improved particle swarm optimization algorithms were used for the global path planning. One is path planning based on second-order oscillating particle swarm optimization algorithm. This method was proposed in view of that the planning space was divided into more obvious areas by constraints and second-order oscillating particle swarm optimization can search by stage. The other one was hybrid particle swarm optimization algorithm which combined the global optimization of orthogonal experimental design method and particle swarm optimization algorithm to improve the global search performance for algorithm in solving the robot path planning problem. The simulation experimental results compared with other algorithms showed the effectiveness of the algorithm.In a dynamic uncertain environment, this paper presented a path planning method combined time rolling window and artificial bee colony algorithm for mobile robot. The dynamic movement environment of robot and the strategy of rolling time window were described in detail, and coding method, fitness function, collision detection methods were given when artificial bee colony algorithm was used in rolling time window for local planning. Finally the realization principles and procedures of path planning using this algorithm were described. Through simulation experiments the feasibility of the algorithm was verified when the robot in dynamic uncertain environment.This paper also proposed UCAV route planning method based on artificial fish swarm algorithm under the 2-D radar threat environment. According to the route planning requirements, threat detection method and artificial fish coding and individual evaluation were designed, then the simulation results by comparison with other experiments showed the algorithm was reasonable.

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