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Target Tracking Algorithm for Table Tennis Using Machine Vision Hindawi Journal of Healthcare Engineering Volume 2021, Article ID 9961978, 7 pages https://doi.org/10.1155/2021/9961978 Research Article Target Tracking Algorithm for Table Tennis Using Machine Vision Hongtu Zhao1 and Fu Hao 2 1Physical Education Institute, Jilin Normal University, Siping 136000, Jilin, China 2Jilin Provincial Neuropsychiatric Hospital, Siping 136000, Jilin, China Correspondence should be addressed to Fu Hao; [email protected] Received 28 March 2021; Revised 22 April 2021; Accepted 25 April 2021; Published 13 May 2021 Academic Editor: Fazlullah Khan Copyright © 2021 Hongtu Zhao and Fu Hao. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. +e current table tennis robot system has two common problems. One is the table tennis ball speed, which moves fast, and it is difficult for the robot to react in a short time. +e second is that the robot cannot recognize the type of the ball’s movement, i.e., rotation, top rotation, no rotation, wait, etc. It is impossible to judge whether the ball is rotating and the direction of rotation, resulting in a single return strategy of the robot with poor adaptability. In this paper, these problems are solved by proposing a target trajectory tracking algorithm for table tennis using machine vision combined with Scaled Conjugate Gradient (SCG). Real human-machine game’s data are obtained in the proposed algorithm by extracting ten continuous position information and speed information frames for feature selection. +ese features are used as input data for the deep neural network and then are normalized to create a deep neural network algorithm model. +e model is trained by the position information of the successive 20 frames. During the initial sets of experiments, we found the shortcomings of the original SCG algorithm. By setting the accuracy threshold and offline learning of historical data and saving the hidden layer weight matrix, the SCG algorithm was improved. Finally, experiments verify the improved algorithm’s feasibility and applicability and show that the proposed algorithm is more suitable for table tennis robots. 1. Introduction to ensure effective table tennis trajectory prediction [6]. Only under the premise of sufficient analysis of the ball’s char- A table tennis robot [1] refers to a typical real-time intel- acteristics, the table tennis robot returns a more timely and ligent robot playing table tennis with humans. It perceives accurate action. At present, there is insufficient research on the service route and trajectory of competitive objects, trajectory prediction and a lack of research on spin trajectory makes reasonable judgments and return strategies, and recognition. As a result, the research on table tennis robots achieves flexible hitting. +e research of table tennis robot needs to be further explored. Our study aims to improve the systems involves an extensive range of fields. It integrates robot’s reaction speed, reduce the reaction time, and con- knowledge in different areas such as computer vision [2], tinue to develop in terms of distinguishing different types of artificial intelligence [3], automatic control [4], robot ki- incoming balls based on ensuring the accuracy of returning nematics [5], and computer graphics. It has exceptional balls [7]. research value and broad application prospects. +ere are some problems in the current research on table In table tennis, table tennis is small and the flying speed tennis robots. First of all, table tennis is a high-speed moving is fast, and it requires table tennis robots to have continuous object, with an average speed of 30 m/s. However, in the and rapid response capabilities. It needs to predict the high- existing research, once the speed of the sphere exceeds 10 m/s, speed table tennis trajectory in a short time and make ac- it is difficult for the robot to make a correct response in a short curate hits. It requires significant efforts to put forward a time. +e second point is that the existing system still cannot real-time and precise table tennis robot control. +erefore, judge for different types of incoming balls, for example, up table tennis robots’ development and broad application need and down spin, left and right spin, etc. +e types of incoming 2 Journal of Healthcare Engineering balls that can be returned are limited, and the return strategy which is smaller than the standard size of the ping-pong is single [8]. table 2.7 ×1.5 m, and the space for hitting the ball is limited Based on current research, most of the table tennis robot to three wireframes installed at both ends of the table and on researchers’ work is focused on analyzing the ball’s trajectory the surface of the net. +e ball’s trajectory hit by the table without spinning. A large amount of trajectory prediction tennis robot must pass through these three 0.5 × 0.5 m work is also carried out around this object. +e shelving of wireframes, and the opposite robot only needs to move the the spin problem is a lack of understanding of the spin racket within the metal frame in front of itself to intercept motion process. +erefore, this paper takes the table tennis the ball. +e table is shown in Figure 1. robot as the research background and proposes a table tennis In the late 1980s, the Swiss Federal Institute of Tech- target trajectory and tracking algorithm using deep learning. nology Zurich designed a six-degree-of-freedom table tennis +e main contributions of this paper are as follows. robot to participate in the international table tennis robot competition [11]. +e proposed designed consists of a three- (1) +e sine of the current table tennis robot system degree-of-freedom mechanical wrist and a three-degree-of- cannot determine whether the ball is rotating. +e freedom mechanical arm. It uses the parallel distributed direction of rotation causes the robot’s single return computer network of the MC series processor produced by strategy and poor resilience. +is paper proposes a Motorola to complete the measurement, prediction, and novel target trajectory and tracking algorithm for control of the robot’s trajectory of table tennis. +e table table tennis using deep learning. It enhances the tennis robot participated in the table tennis robot compe- predictive ability of the robot system on the tra- tition in Hong Kong and eventually won first place in the jectory of table tennis. event [12]. (2) +e proposed algorithm is based on the transfer In 2002, the Miyazaki Laboratory of Osaka University in learning theory in deep learning. It uses a layered Japan proposed a method to control a table tennis robot, noise reduction autoencoder to learn a general hi- which can hit the ball to the desired position within a erarchical image feature description. +e learning specified time [13]. +e proposed system has two rotations process is offline from many auxiliary data in an and two translation joints, a total of four degrees of freedom. unsupervised manner through fine-tuning training Two motors near the racket control the racket’s attitude, and to model and characterize trajectory characteristics two motors mounted on a linear guide control the racket’s of various tracking targets. position. Based on this platform, researchers from Miyazaki (3) +e simulation experiments are carried out. +e Laboratory proposed a table tennis racket motion planning experimental results show that the algorithm in this method based on the mirror image method. +ey proposed a paper can effectively improve the table tennis robot local weighted regression racket motion planning method to system’s ball returnability and improve the quality of control table tennis’s drop point successfully. man-machine sparring training. In 2007, TOSY of Vietnam and Quanta-view of Japan had successively developed table tennis robots that can play +e rest of the paper is organized as follows. In Section 2, against people. +e TOPIO developed by the Vietnam TOSY related work is studied, and methodology is given in Section company uses a binocular camera system to obtain the flight 3. +e experimental results are shown in Section 4, and trajectory of table tennis. +e third-generation table tennis Section 5 concludes the paper. robot was created in 2010. +e overview shows that table tennis robots based on deep learning and machine vision 2. Related Work have become a development trend [14]. In the 1980s, research on table tennis robots started abroad. 3. Methodology +e first table tennis robot developed was just a table tennis ball machine [9]. +is kind of robot can only serve but does In this paper, the table tennis ball’s precise rotation is re- not have the ability to fight back. It was not until later that it versed through various information such as motion tra- gradually developed into a new generation of table tennis jectory, speed, and landing point. Since no scholar has robots that can return the opponent’s ball. In 1983, Professor studied the relationship between the accurate rotation of John Billingsley of Portsmouth Polytechnic University in the table tennis and its trajectory, speed, and drop point before, United Kingdom initiated a robot-playing table tennis the premise of reversing the rotation is to prove a correlation competition. From 1985 to 1988, the robot table tennis between this information. +is paper is mainly based on the competition was successfully held four times in Europe in neural network design algorithm of machine vision and the past four years. +e participants were from universities calculates the correlation between table tennis rotation and in the United Kingdom, Finland, Sweden, and Switzerland movement trajectory. +is paper will design experiments to [10].
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