| Neural network and genetic algorithm based global path planning in a static environment |
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| 作者姓名： | 杜歆 陈华华 顾伟康 |
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| 作者单位： | Department of Information Science and Electronics Engineering，Zhejiang University，Hangzhou 310027，China，Department of Information Science and Electronics Engineering，Zhejiang University，Hangzhou 310027，China，Department of Information Science and Electronics Engineering，Zhejiang University，Hangzhou 310027，China |
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| 基金项目： | 中国科学院资助项目，浙江省自然科学基金 |
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| 摘 要： | INTRODUCTION The path planning problem of a mobile robot is to find a safe and efficient path for the robot, given a start location, a goal location and a set of obstacles distributed in a workspace. The robot can go from the start location to the goal location without colliding with any obstacle along the path. In addition to the fundamental problem, we also try to find a way to optimize the plan, say to minimize the time required or distance traveled (Wu et al., 1996; Sadati and Ta-he…
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| 关 键 词： | 神经网络 移动机器人 遗传算法 广义路径通道 |
| 收稿时间： | 9 May 2004 |
| 修稿时间： | 7 October 2004 |
Neural network and genetic algorithm based global path planning in a static environment |
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| Du Xin,Chen Hua-hua,Gu Wei-kang.Neural network and genetic algorithm based global path planning in a static environment[J].Journal of Zhejiang University Science,2005,6(6):549-554. |
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| Authors: | Du Xin Chen Hua-hua Gu Wei-kang |
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| Institution: | (1) Department of Information Science and Electronics Engineering, Zhejiang University, 310027 Hangzhou, China;(2) School of Communication Engineering, Hangzhou Dianzi University, 310018 Hangzhou, China |
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| Abstract: | Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective. |
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| Keywords: | Mobile robot Neural network Genetic algorithm Global path planning Fitness function |
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