Human-like Artificial Intelligent Wheelchair Robot Navigated by Multi-Sensor Models in Indoor Environments and Error Analysis

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.