With the wide application of Bioinspired Neural Network in the field of robot path planning, the environmental scale of robot path planning is getting larger, and the environmental resolution requirements are getting ...
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With the wide application of Bioinspired Neural Network in the field of robot path planning, the environmental scale of robot path planning is getting larger, and the environmental resolution requirements are getting higher. However, with the increase of the environment size and resolution requirement, the neuronal activity value calculation cost and the time cost of the Bioinspired Neural Network will increase sharply. Aiming at this problem, this paper proposes an improved Bioinspired Neural Network path planning method based on Scaling Terrain. Using a multi-scale map method and Dijkstra algorithm, the optimal path of a Coarse scalemap is calculated. The optimal path obtained from the Coarse scalemap is used to guide the neural network planning weights of the Fine scalemap from the same terrain. Thus, the optimal path of the Fine scalemap can be calculated by the improved BNN algorithm. Introducing this multi-scale map method into the Bioinspired Neural Network can greatly reduce the time cost of the Bioinspired Neural Network path planning algorithm and reduce the mathematical complexity. Simulation results in some computer integrated virtual environments further demonstrate the superiority of this method and the experimental results are encouraging.
3D Path planning for multi-robot one-target pursuit is an interesting topic. Bioinspired neural network is frequently implemented for path planning of multi-robot, and the bioinspired neural network neural activity va...
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ISBN:
(纸本)9781728142425
3D Path planning for multi-robot one-target pursuit is an interesting topic. Bioinspired neural network is frequently implemented for path planning of multi-robot, and the bioinspired neural network neural activity value computational cost and time cost will increase sharply with the increase of the number of neurons. This paper explores an improved 3D path planning method based on multi-scale map method to reduce the time cost. Combining the multi-scale map method with the Dijkstra algorithm, the optimal paths of 3D coarse-scalemap of multi-robot one-target can be generated. The weights created from the coarse-scalemap are employed to yield the 3D path planning of the fine-scalemap for the same terrain. Therefore, this improved bioinspired neural network algorithm has proven the ability to calculate the multi-robot 3D optimal paths. By introducing this multi-scale map method into the multirobot bioinspired neural network algorithm, the time cost and mathematical complexity of the path planning algorithm can be greatly reduced. MATLAB simulation results further reveal the effectiveness and superiority of this method.
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