A Common-Neural-Pattern Based Reasoning for Mobile Robot Cognitive Mapping
A Common-Neural-Pattern Based Reasoning for Mobile Robot Cognitive Mapping
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DOI：
10.1007/978-3-642-02490-0_4
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发表时间：
2008-11
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影响因子：
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通讯作者：
Aram Kawewong;Yutaro Honda;M. Tsuboyama;O. Hasegawa
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文献类型：
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作者：
Aram Kawewong;Yutaro Honda;M. Tsuboyama;O. Hasegawa
提出了一种基于神经模式推理的机器人导航方法。基于模式推理的概念，该方法使一个移动的机器人解决面向目标的导航问题，在一个非常短的时间内，低内存消耗。给定一个简单的学习环境，观察到的输入向量由自组织增量神经网络（SOINN）处理，以生成空间公共模式（CP），这是有用的，在其他不熟悉的环境。在不熟悉的环境中执行目标导向导航，既没有地图也没有目标的先验信息，机器人通过参考最近的CP并形成称为A模式的CP模式来识别部分区域。连续的A模式用于导出环境的地图。该地图基于推理进行优化，因为区域之间的新过渡可以自动生成。通过求解一个真实世界的迷宫和三个Webots模拟迷宫来评估该方法。结果表明，所提出的方法使机器人能够找到显着更短的路径，在只有一个插曲，而使用强化学习需要更多的插曲。该地图包含比当前混合地图构建或拓扑地图更多的信息。地图不依赖于坐标，导致对自姿态估计中的误差不敏感。
Neural Pattern-Based Reasoning for real-world robot navigation problems is proposed. Based on the concept of Pattern-Based Reasoning, the method enables a mobile robot to solve goal-oriented navigation problems in a remarkably short time with low memory consumption. Given a simple learning environment, the observed input vectors are processed by a Self-Organizing Incremental Neural Network (SOINN) to generate Spatial Common Patterns (CPs), which are useful in other unfamiliar environments. Performing goal-oriented navigation in unfamiliar environments, with prior information neither of the map nor the goal, the robot recognizes the partial area by referring to the nearest CPs and forming a pattern of CPs called A-Pattern. The sequential A-Patterns are used to derive the map of the environment. This map is optimized based on reasoning, as the new transitions between areas could be generated automatically. The method is evaluated by solving one real-world maze and three Webots simulated mazes. The results show that the proposed method enables the robot to find the markedly shorter path in only one episode, whereas use of the Reinforcement Learning requires more episodes. The map contains more information than the current hybrid map building or topological map. The map does not rely on coordinate, resulting in non-sensitivity to the error in self-pose estimation.