Abstract
This paper proposes and demonstrates a single-line discontinuous track recognition system by associating the track recognition problem of a humanoid robot with the lane detection problem. The proposal enables the robot to achieve stable running on the single-line discontinuous track. The system consists of two parts: the robot end and the graphics computing end. The robot end is responsible for collecting track information and the graphics computing end is responsible for high-performance computing. These two parts use the TCP for communication. The graphics computing side uses PolyLaneNet lane detection algorithm to train the track image captured from the first perspective of the darwin-op2 robot as the data set. In the inference, the robot end sends the collected tracking images to the graphics calculation end and uses the graphics processor to accelerate the calculation. After obtaining the motion vector, it is transmitted back to the robot end. The robot end parses the motion vector to obtain the motion information of the robot so that the robot can achieve stable running on the single-line discontinuous track. The proposed system realizes the direct recognition of the first perspective image of the robot and avoids the problems of poor stability, inability of identifying curves and discontinuous lines, and other problems in the traditional line detection method. At the same time, this system adopts the method of cooperative work between the PC side and the robot by deploying the algorithm with high computational requirements on the PC side. The data transmission is carried out by stable TCP communication, which makes it possible for the robot equipped with weak computational controllers to use deep-learning-related algorithms. It also provides ideas and solutions for deploying deep-learning-related algorithms on similar low computational robots.
Highlights
Academic Editor: Daqing Gong is paper proposes and demonstrates a single-line discontinuous track recognition system by associating the track recognition problem of a humanoid robot with the lane detection problem. e proposal enables the robot to achieve stable running on the single-line discontinuous track. e system consists of two parts: the robot end and the graphics computing end. e robot end is responsible for collecting track information and the graphics computing end is responsible for high-performance computing
The track recognition of humanoid robot in track and field competition is mainly to identify one or several lines in the picture captured by the first person of the robot. ese lines have different colours, continuous state, intersection, and other characteristics, which play a guiding role in the movement of robots, similar to the track line of human track and field competition and the lane line of vehicles driving on the road
Some of the complex algorithms are various visual algorithms based on cameras. e camera deployed on the robot captures the first person or non-first-person real-time images of the robot for recognition, including traditional recognition algorithms such as pixel-by-pixel comparison method, Hough [1] transform method, connected domain recognition method, line segments angles computation [2], and deep learning recognition algorithms [3,4,5,6,7,8,9,10,11,12,13,14] such as deep convolutional neural network recognition algorithm
Summary
E disadvantage of this algorithm is that it is easy to form false recognition to the noise or large area colour block after image preprocessing; the recognition effect of discontinuous track is poor. Is algorithm can identify the interference of discontinuous track, immune noise, and large-area colour blocks and has strong recognition ability for straight lines. The lane detection is similar to the track recognition; there are continuous line and discontinuous line, different degrees of noise interference and occlusion, and real-time problems. Scnn algorithm is a semantic segmentation algorithm, which uses the method similar to RNN to learn the spatiotemporal information It defines K lines in advance, makes K classification prediction for each pixel, and combines the points with the same results to get the lane lines to be detected, but the disadvantage is that the recognition speed is slow. Some lines with various angles are defined in advance and their offsets are learned. is detection method depends on a priori and has low flexibility in complex scenes
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