基于生物激励神经网络的清洁机器人遍历路径规划算法的改进
Improvement of traversal path planning algorithm of cleaning robot based on biologically inspired neural network
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摘要: 针对基于生物激励神经网络的清洁机器人遍历路径规划算法的遍历面积重复率和遍历路径总长度均较大的问题，对该算法进行了改进：在脱困算法中，采用实时监测机器人邻域神经元状态的方法，使机器人脱困路径缩短；引入邻域神经元状态准则，使机器人在遇到孤岛障碍物避障时，先沿障碍物边沿遍历.仿真结果表明，改进算法可以有效降低遍历面积重复率、遍历路径总长度和转弯次数.Abstract: In view that the traversal area repetition rate and the total length of the traversal path of the traversal path planning algorithm of the cleaning robot based on the biologically inspired neural network are large, the algorithm was improved. In the relief algorithm, the method of real-time monitoring of the neurons in the neighborhood of the robot was adopted to shorten the path for the robot to get out of difficulty. The state criteria of neighboring neurons were introduced to make the robot traverse along the edge of the obstacle traversal when obstructing obstacles in an island. Simulation results showed that the improved algorithm could effectively reduce the traversal area repetition rate,the total length of the traversal path and the number of turning.
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Key words:
- cleaning robot /
- path planning /
- biologically inspired neural network
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