Depth ImageBased Obstacle Avoidance for an InDoor Patrol Robot Imagebased obstacle avoidance has been studied for decades. One weak point of imagebased approaches is that the performance usually depends on the lighting condition. That is, the performance can be very poor in dark environments. In this research, we investigate the possibility of the depth imagebased approach for fulltime indoor patrolling. As the first step, we consider a 3class problem. Each depth image is classified as xe2x80x9cdangerxe2x80x9d if some obstacle is too close, as xe2x80x9cnoticexe2x80x9d if the obstacle is close, and as xe2x80x9cnormalxe2x80x9d if there is no obstacle in the vicinity. The label of each depth image is defined based on the RGB image captured at the same time, and an AlexNet, which is a welltrained convolutional neural network, is retrained via transfer learning, and used for classification. In our primary experiment, we collected 102,776 image data in the Research Quadrangle of the University of Aizu. Test results show that the performance of the depth imagebased approach is good during both day and night, and in most cases, it is better than the RGB imagebased approach. This result can provide new insights when designing more practical fulltime patrol robots.

The obstacles detection for outdoor robot based on computer vision in deep learning In this thesis, we propose a guided robot with deep learning techniques and machine vision in order to predict distance and avoid obstacles. The training data images are captured by the stereo camera, and output results are disparity of the single image. Then, the distances are converted by using the triangulation method of computer vision. The back propagation neural network retrains to obtain the actual distance of each pixel in the image. Therefore, the robot with the monocular camera could know the distance between obstacles and itself. Semantic segmentation is utilized to a to distinguish road and obstacles in the image. Fuzzy theory for calculating the area of the road which be cut is designed to avoid walking into the intersection. The obstacle depth and the walkable area are taken as an information for avoiding obstacles control. Because of the robot is proposed to walk on the right side along the road, the edge of road is necessary to navigate the robot. The Hough method is used to find the straight line, and then choose the line we need. The path planning is also achieved in through of navigation and obstacle avoidance control. As a result, the robot can arrive the destination safely and precisely.