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Comparative Research on Robot Path Plannin gBased on GA-ACA and ACA-GA

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  • Fri, 12/15/2017 - 10:45am - 12:45pm




Comparative Research on Robot Path Planning Based on GA-ACA and ACA-GA

MSc Thesis Proposal by:

Chenhan Wang

Date:  Friday, December 15th, 2017
Time: 10:45am
Location: 3105, Lambton Tower

Abstract: The path planning for mobile robots is one of the core contents in the filed of robotics research with complex, restrictive and nonlinear characteristics. It consists of automatically determine a path from an initial position of the robot to its final position. Due to classic approaches have several drawbacks, evolutionary approaches such as Ant Colony Optimization Algorithm (ACA) and Genetic Algorithm (GA) are employed to solve the path planning efficiently.

Firstly, grid method is used to establish the environment model and some modifications are made to accommodate ACA to path planning in grid-based environment. Besides, genetic operators were introduced to the basic ACA (GA-ACA, ACA-GA), using the crossover and mutation operators to expand the search space and enhance the overall solution in the previous research work.

This thesis mainly introduces these two hybrid algorithms, GA-ACA and ACA-GA, and will compare the performance of them under multiple grid maps in static environments.

In order to verify the effectiveness of these two hybrid algorithms, a path planning simulation system for mobile robots is designed based on MATLAB development environment. The experiment results show that the algorithm efficiency of GA-ACA and ACA-GA is better than that of the traditional GA and ACA algorithms, and it is more suitable to apply ACA-GA than GA-AGA in terms of algorithms' convergence speed and stability in a complicated environment map.

 Keywords: robot path planning, Ant colony optimization, grid method, genetic operators, GA-ACA, ACA-GA

Thesis Committee:
Internal Reader:  Dr. Jessica Chen    
External Reader:  Dr. Esam Abdel-Raheem 
Advisor:  Dr. Dan Wu
Chair:  Dr. Xiaobu Yuan



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