Disseminating Collaborative Robotics and Artificial Intelligence Through a Board Game Demo

Carbonari, Luca; Forlini, Matteo; Scoccia, Cecilia; Costa, Daniele; Palpacelli, Matteo-Claudio

doi:10.1109/mesa55290.2022.10004477

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Several other publications utilize AI for various purposes. An example is provided by Carbonari et al in [6], who apply artificial intelligence to vision in an informative context. They use the collaborative robot Yumi to play the game Connect Four, thereby demonstrating the effectiveness of AI in a playful activity that requires both visual perception and decision-making capabilities.…”

Section: Artificial Intelligence and Its Importancementioning

confidence: 99%

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Bajrami,

Palpaceli

2024

Preprint

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This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

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Section: Artificial Intelligence and Its Importancementioning

confidence: 99%

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Bajrami,

Palpaceli

2024

Preprint

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“…However, collaborative robotics is not a privilege of the industrial sector but has also been applied for entertainment purposes thanks to the development of proper AI algorithms. Several examples of entertainment robotic systems can be found in the literature, demonstrating that robots can safely cooperate with humans while playing board games (such as Tic Tac Toe [3] and Connect 4 [4]), painting [5], or playing music [6], just to mention a few applications of collaborative robotics in this field. Particularly, the possibility of playing chess and checkers against an artificial player has been widely investigated, developing several solutions in order to accomplish this purpose [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Playing Checkers with an Intelligent and Collaborative Robotic System

Fabris,

Scalera,

Gasparetto

2023

Robotics

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Collaborative robotics represents a modern and efficient framework in which machines can safely interact with humans. Coupled with artificial intelligence (AI) systems, collaborative robots can solve problems that require a certain degree of intelligence not only in industry but also in the entertainment and educational fields. Board games like chess or checkers are a good example. When playing these games, a robotic system has to recognize the board and pieces and estimate their position in the robot reference frame, decide autonomously which is the best move to make (respecting the game rules), and physically execute it. In this paper, an intelligent and collaborative robotic system is presented to play Italian checkers. The system is able to acquire the game state using a camera, select the best move among all the possible ones through a decision-making algorithm, and physically manipulate the game pieces on the board, performing pick-and-place operations. Minimum-time trajectories are optimized online for each pick-and-place operation of the robot so as to make the game more fluent and interactive while meeting the kinematic constraints of the manipulator. The developed system is tested in a real-world setup using a Franka Emika arm with seven degrees of freedom. The experimental results demonstrate the feasibility and performance of the proposed approach.

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