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In the trash-collection challenge of the Nakanoshima Robot Challenge, an autonomous robot must collect trash (bottles, cans, and bentos) scattered in a defined area within a time limit. A method for collecting the trash is to use machine learning to recognize the objects, move to the target location, and grasp the objects. An autonomous robot can achieve the target position and posture by rotating on the spot at the starting point, moving in a straight line, and rotating on the spot at the destination, but the rotation requires stopping and starting. To achieve faster movement, we implemented a smooth movement approach without sequential stops using a spline curve. When using the training data previously generated by the authors in their laboratory for object recognition, the robot could not correctly recognize objects in the environment of the robot competition, where strong sunlight shines through glass, because of the varying brightness and darkness. To solve this problem, we added our newly generated training data to YOLO, an image-recognition algorithm based on deep learning, and performed machine learning to achieve object recognition under various conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a method for the recognition of the opened/closed states of automatic sliding glass doors to allow for automatic robot-controlled movement from outdoors to indoors and vice versa by a robot. The proposed method uses an RGB-D camera as a sensor for extraction of the automatic sliding glass doors region and image recognition to determine whether the door is opened or closed. The RGB-D camera measures the distance between the opened or moving door frames, thereby facilitating outdoor to indoor movement and vice versa. Several automatic sliding glass doors under different experimental conditions are experimentally investigated to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]