Research on Mobile Robot Bionic Location Algorithm Based on Growing Self-Organizing Map
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摘要: 为提高移动机器人在同步定位和地图构建(SLAM)中的定位精度，该文提出一种基于自组织可增长映射 (GSOM)的仿生定位算法。该方法将位置细胞的激活特性和神经网络输出层神经元建立响应连接，通过GSOM神经网络构建空间的拓扑地图，利用感知距离信息实现位置细胞的激活响应从而估计机器人位置，以此还原机器人的运行路径。实验结果表明细胞间隔R对定位精度有较大影响，选取合适的细胞间隔能有效地减少神经网络的学习时间，提高定位精度，该文算法平均误差在0.153 m以内，定位精度达到90.243%，均优于原有算法。经验证该文算法建立的模型能够实现机器人的空间位置表征，提高了机器人在实验场景下的定位精度，表现出良好的位置估计性能。Abstract: In order to improve the positioning accuracy of mobile robots in Simultaneous Localization And Mapping (SLAM), a bionic localization algorithm based on Growing Self-Organizing Map(GSOM) neural network is proposed. The method connects the activation characteristics of the place cells with the neural network output layer neurons to establish a response, and constructs a spatial topology map through the GSOM neural network, and uses the perceived distance information to realize the activation response of the place cells to estimate the position of the robot. The running path of the robot is restored in this way. The experimental results show that the cell spacing R has a great influence on the positioning accuracy. Choosing the appropriate cell spacing can effectively reduce the learning time of the neural network and improve the positioning accuracy. The average error of the algorithm is less than 0.153 m, and the positioning accuracy is 90.243%, which is better than the original algorithm. It is verified that the model established by the algorithm can realize the spatial position representation of the robot, improves the positioning accuracy of the object under the experimental scene, and shows good position estimation performance.
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表 1 GSOM学习算法
输入：环境样本${{{x}}_k} = \left[ {{x_1},{x_2}, ··· ,{x_m}} \right]$ 输出：映射2维结构${{{y}}_k} = \left[ {{y_1},{y_2}, ··· ,{y_n}} \right]$ for输出单元$j$, do 计算输入到输出的欧氏距离${d_j}$； 计算最小相似度量$\min {d_j}$； if ${j^*} = \min {d_j}$； 即选定单元为最佳匹配单元； else 更新胜出单元${j^*}$的邻域内所有单元的连接权重为式(14) end if if误差${\left[ {{x_i}(t) - {w_{ij}}(t)} \right]^2} > \delta $； 调整学习因子，缩小胜出单元${j^*}$的邻域范围，为式(12)； else ${y_k} = {j^*}$ end if end for 
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表 2 两种模型的定位误差
定位方法 最大绝对误差MAE(m) 平均误差(m) 准确率(%) 改进VP-SLAM 0.3018 0.1525 90.2436 原始VP-SLAM 0.4237 0.3116 87.7264
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