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首页> 外文期刊>Procedia Computer Science >Human-like Artificial Intelligent Wheelchair Robot Navigated by Multi-Sensor Models in Indoor Environments and Error Analysis
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Human-like Artificial Intelligent Wheelchair Robot Navigated by Multi-Sensor Models in Indoor Environments and Error Analysis

机译：室内环境下多传感器模型导航的类人型人工智能轮椅机器人及其误差分析

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摘要

Intelligent mobile robot navigation in indoor environments is still a challenge. In this paper, we propose a method in which the wheelchair robot imitates human like navigation by interacting with the surrounding environments. Two types of sensor data are used to train neural networks, which are later used to control the robot to reach the goal location in different indoor environments. The robot navigates from the start to the goal location in the environments with obstacles. In first model, we used the Laser Range Finder (LRF) sensor data as input of the neural network. In the second model in addition to the LRF data, the processed camera sensor data are also utilized. We compare the performance of two neural networks models by analyzing the error between the human and neural network based real robot navigations. The experimental results show that our proposed models are efficient for mobile robot navigations. In addition, errors are analyzed in this paper.
机译：室内环境中的智能移动机器人导航仍然是一个挑战。在本文中，我们提出了一种轮椅机器人通过与周围环境交互来模仿人类导航的方法。两种类型的传感器数据用于训练神经网络，随后用于控制机器人以在不同的室内环境中到达目标位置。在有障碍物的环境中，机器人会从起点导航到目标位置。在第一个模型中，我们使用激光测距仪（LRF）传感器数据作为神经网络的输入。在第二个模型中，除了LRF数据外，还利用了处理后的相机传感器数据。我们通过分析基于真实机器人导航的人类和神经网络之间的误差，比较了两个神经网络模型的性能。实验结果表明，我们提出的模型对于移动机器人导航是有效的。此外，本文还分析了错误。

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