An Economical Robotic Arm for Playing Chess Using Visual Servoing

The proposed paper outlines the design of an economical robotic arm which is used to visualize the chess board and play with the opponent using visual servoing system. We have used the FaBLab RUC's mechanical design prototype proposed and have further used Solidworks software to design the 4 jointed gripper. The proposed methodology involves detecting the squares on the corners of the chessboard and further segmenting the images. This is followed by using convolutional neural networks to train and recognize the image in order to determine the movement of the chess pieces. To trace the manipulator, Kanade-Lucas-Tomasi method is used in the visual servoing system. An Arduino uses Gcode commands to interact with the robotic arm. Game Decisions are taken with the help of chess game engine the pieces on the board are moved accordingly. Thus a didactic robotic arm is developed for decision making and data processing, serving to be a good opponent in playing chess.

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Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

Electronics ◽

10.3390/electronics8101154 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1154 ◽

Cited By ~ 1

Author(s):

Cristian del Toro ◽

Carlos Robles-Algarín ◽

Omar Rodríguez-Álvarez

Keyword(s):

Visual Servoing ◽

Mechanical Design ◽

Vision System ◽

Cost Effective ◽

Artificial Vision ◽

Robotic Arm ◽

Chess Game ◽

Human Opponent ◽

The University ◽

Design And Construction

This paper presents the design and construction of a robotic arm that plays chess against a human opponent, based on an artificial vision system. The mechanical design was an adaptation of the robotic arm proposed by the rapid prototyping laboratory FabLab RUC (Fabrication Laboratory of the University of Roskilde). Using the software Solidworks, a gripper with 4 joints was designed. An artificial vision system was developed for detecting the corners of the squares on a chessboard and performing image segmentation. Then, an image recognition model was trained using convolutional neural networks to detect the movements of pieces on the board. An image-based visual servoing system was designed using the Kanade–Lucas–Tomasi method, in order to locate the manipulator. Additionally, an Arduino development board was programmed to control and receive information from the robotic arm using Gcode commands. Results show that with the Stockfish chess game engine, the system is able to make game decisions and manipulate the pieces on the board. In this way, it was possible to implement a didactic robotic arm as a relevant application in data processing and decision-making for programmable automatons.

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Augmented decision‐making for acral lentiginous melanoma detection using deep convolutional neural networks

Journal of the European Academy of Dermatology and Venereology ◽

10.1111/jdv.16185 ◽

2020 ◽

Vol 34 (8) ◽

pp. 1842-1850 ◽

Cited By ~ 4

Author(s):

S. Lee ◽

Y.S. Chu ◽

S.K. Yoo ◽

S. Choi ◽

S.J. Choe ◽

...

Keyword(s):

Neural Networks ◽

Decision Making ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Acral Lentiginous Melanoma ◽

Melanoma Detection

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Development of a Chinese Chess Robotic System for the Elderly Using Convolutional Neural Networks

Sustainability ◽

10.3390/su12103980 ◽

2020 ◽

Vol 12 (10) ◽

pp. 3980 ◽

Cited By ~ 2

Author(s):

Pei-Jarn Chen ◽

Szu-Yueh Yang ◽

Chung-Sheng Wang ◽

Muslikhin Muslikhin ◽

Ming-Shyan Wang

Keyword(s):

Neural Networks ◽

Elderly People ◽

Convolutional Neural Networks ◽

Great Influence ◽

The Elderly ◽

Robotic Arm ◽

Life Long Learning ◽

Dementia Patients ◽

Reduce Risk ◽

Aging Societies

According to the data from Alzheimer’s Disease International (ADI) in 2018, it is estimated that 10 million new dementia patients will be added worldwide, and the global dementia population is estimated to be 50 million. Due to a decline in the birth rate and the development and great progress of medical technology, the proportion of elderly people has risen annually in Taiwan. In fact, Taiwan has become one of the fastest-growing aged countries in the world. Consequently, problems related to aging societies will emerge. Dementia is one of most prevailing aging-related diseases, with a great influence on daily life and a great economic burden. Dementia is not a single disease, but a combination of symptoms. There is currently no medicine that can cure dementia. Finding preventive measures for dementia has become a public concern. Older people should actively increase brain-protective factors and reduce risk factors in their lives to reduce the risk of dementia and even prevent the occurrence of dementia. Studies have shown that engaging in mental or creative activities that stimulate brain function has a relative risk reduction of nearly 50%. Elderly people should develop the habit of life-long learning to strengthen effective neural bonds between brain cells and preserve brain cognitive functions. Playing chess is one of the suggested activities. This paper aimed to develop a Chinese robotic chess system for the elderly. It mainly uses a camera to capture the contour of the Chinese chessman, recognizes the character and location of the chessman, and then transmits this information to the robotic arm, which will grab and place the chessman in the appropriate position on the chessboard. The camera image is transmitted to MATLAB for image recognition. The character of the chessman is recognized by convolutional neural networks (CNNs). Forward and inverse kinematics are used to manipulate the robotic arm. Even if the chessmen are arbitrarily placed, the experiment showed that their coordinates can be found through the camera as long as they are located within the working scope of the camera and the robotic arm. For black chessmen, no matter how many degrees they are rotated, they can be recognized correctly, while the red ones can be recognized 100% of the time within 90° of rotation and 98.7% with more than a 90° rotation.

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Model Architecture of CNN for Recognition the Pandava Mask

Jurnal INFORM ◽

10.25139/inform.v0i1.2740 ◽

2020 ◽

Vol 5 (2) ◽

pp. 99

Author(s):

Andi Sanjaya ◽

Endang Setyati ◽

Herman Budianto

Keyword(s):

Neural Networks ◽

Data Processing ◽

Convolutional Neural Networks ◽

Research Result ◽

Trial Result ◽

Trial Data ◽

The Other ◽

Architectural Model ◽

Input Layer

This research was conducted to observe the use of architectural model Convolutional Neural Networks (CNN) LeNEt, which was suitable to use for Pandava mask objects. The Data processing in the research was 200 data for each class or similar with 1000 trial data. Architectural model CNN LeNET used input layer 32x32, 64x64, 128x128, 224x224 and 256x256. The trial result with the input layer 32x32 succeeded, showing a faster time compared to the other layer. The result of accuracy value and validation was not under fitted or overfit. However, when the activation of the second dense process as changed from the relu to sigmoid, the result was better in sigmoid, in the tem of time, and the possibility of overfitting was less. The research result had a mean accuracy value of 0.96.

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