Research on Mobile Robot Navigation Algorithm based on Ant Colony Optimization Neural Network
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The birth of mobile robot can not only improve the production efficiency of automation industry and reduce the production cost, but also replace human beings to work in some dangerous environments or inaccessible areas, which greatly promotes the social development and progress. In order to improve the efficiency of the robot to complete the task, we hope that the robot can have the function of autonomous and safe pathfinding. Path planning is an important and indispensable part of the research field of robot navigation technology. The so-called robot path planning refers to finding a safe path from the starting point to the end point in an unknown environment. There are many branches of artificial intelligence research, among which the research of intelligent robot has been paid more and more attention with the continuous progress of technology. The method adopted in this paper is ant colony optimization neural network. The best weights found by ant colony algorithm are used as the initial weights of BP algorithm, and the error between the network output and the actual output is calculated to adjust the weights. The feasibility and high efficiency of this method are proved by the research in this paper.
Ant colony optimization neural network; Mobile robot; Navigation algorithm