Appl Opt. 2022 Jan 1;61(1):177-187. doi: 10.1364/AO.443535.
Accurate and effective acquisition of obstacle size parameters is the basis for environment perception, path planning, and autonomous navigation of mobile robots, and is the key to improve the walking performance of mobile robots. In this paper, a generic algorithm-back propagation (GA-BP) neural network-based method for small obstacle size prediction is proposed for mobile robots to perceive the environment quantitatively. A machine vision-based small obstacle size measurement experiment was designed, and 228 sets of sample data were obtained. A genetic algorithm optimized back propagation neural network was used to build a small obstacle size prediction model with obstacle pixel width, pixel height, pixel area, and obstacle-to-camera distance as input parameters and actual obstacle width, actual height, and actual area as output parameters. The results show that the correlation coefficient () between the predicted and expected values of the test data is higher than 0.999, the root mean square error is lower than 5.573, and the mean absolute percentage error is lower than 2.84%. The good agreement between its predicted and expected values indicates that the model can accurately predict the size of small obstacles. The GA-BP neural network-based small obstacle size prediction method proposed in this paper is simple to execute, has good real-time performance, and provides a new, to the best of our knowledge, way of thinking for mobile robots to acquire environmental data.
准确有效地获取障碍物尺寸参数是移动机器人环境感知、路径规划和自主导航的基础，也是提高移动机器人行走性能的关键。本文提出了一种基于通用算法-反向传播（GA-BP）神经网络的移动机器人环境中小型障碍物尺寸预测方法，用于对环境进行定量感知。设计了一种基于机器视觉的小型障碍物尺寸测量实验，获得了 228 组样本数据。使用遗传算法优化的反向传播神经网络建立了一个以障碍物像素宽度、像素高度、像素面积和障碍物到相机的距离为输入参数，实际障碍物宽度、实际高度和实际面积为输出参数的小型障碍物尺寸预测模型。结果表明，测试数据的预测值与期望值之间的相关系数（）高于 0.999，均方根误差低于 5.573，平均绝对百分比误差低于 2.84%。其预测值与期望值之间的良好一致性表明，该模型可以准确地预测小型障碍物的尺寸。本文提出的基于 GA-BP 神经网络的小型障碍物尺寸预测方法执行简单，具有良好的实时性，为移动机器人获取环境数据提供了一种新的、据我们所知的、新的思路。
