Playing Checkers with an Intelligent and Collaborative Robotic System †
Abstract
:1. Introduction
- the recognition of the board and positions of pieces with respect to the robot, piece color, and type, as well as the human player move;
- the decision of which is the optimal move to perform among all the possible ones according to a proper game strategy;
- the trajectory planning and control of the robot in order to physically execute the chosen move.
2. Related Works
3. Materials and Methods
3.1. Italian Checkers Rules
3.2. Game State Evaluation
3.3. Game Engine
3.4. Trajectory Planning
- in the case of a simple movement, the robot positions the piece in the target square and then returns to the neutral pose;
- if a capture (including multiple ones) has to be made, the robot, after placing its piece in the target square, proceeds to eliminate from the board the opponent piece (or pieces) captured;
- when a move results in a man becoming a king, the robot avoids stacking pieces to make king by itself; instead, it removes the man from the board and takes the king, which is prepared in a reference position outside the board.
3.5. Experimental Setup
- robotic arm;
- computer and control system;
- camera;
- board and pieces.
4. Results
5. Conclusions
- •
- Vision system: the robustness and reliability of the computer vision algorithm will be improved through the implementation of an approach based on model recognition, which is more robust with respect to illumination, and an ad hoc illumination system;
- •
- Computational times: the alpha–beta pruning algorithm will be implemented to speed up the execution times of the gaming algorithm and enhance its overall performance;
- •
- Safety of the human player: safety strategies will be implemented to track the human position in real time and stop the robot in the case of a potential collision, as in [2]. Furthermore, the safety of the gripper will also be considered to avoid accidents, such as the one in 2022, when a chess robot broke a finger of 7-year-old boy during a tournament in Moscow [34].
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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First Author | Year | Ref. | Robot | Game | Game State Estimation | Game Engine |
---|---|---|---|---|---|---|
Barakova | 2018 | [15] | Humanoid (NAO) | Checkers | Camera, computer vision, based on relation between the coordinate systems of camera and board | Minimax + alpha–beta pruning |
Bernbaum | 2018 | [9] | Franka Emika robot | Chess | Camera, computer vision, based on model recognition | Sunfish chess engine |
Brooks | 2015 | [16] | Baxter robot | Checkers | Camera, computer vision, based on model recognition | Raven Checkers |
Carrera | 2017 | [26] | Cartesian robot | Chess | Camera, computer vision, pieces recognition | Not specified |
Chen | 2019 | [13] | Baxter robot | Chess | Camera, computer vision, based on model recognition | Stockfish |
Del Toro | 2019 | [14] | Custom robot with four DOFs | Chess | Camera, computer vision, based on model recognition | Stockfish |
Elnaggar | 2014 | [24] | Lynxmotion AL5D | Checkers | Camera, computer vision, based on color information | Negamax |
Gupta | 2015 | [19] | Cartesian robot | Chess | LDR sensors under squares for pieces detection | Minimax + alpha–beta pruning |
Juang | 2022 | [11] | Humanoid (NAO) | Chess | Camera, computer vision, based on model recognition | Not implemented |
Kołosowski | 2020 | [12] | UR5 robot | Chess | Camera, computer vision, based on model recognition | Minimax + alpha–beta pruning |
Kopets | 2020 | [20] | Custom robot with three DOFs | Russian checkers | Hall sensors under black squares detect magnetic pieces | Neural network based on AlphaZero |
Larregay | 2018 | [17] | ABB model IRB120 | Chess | Camera, computer vision, dedicated lighting system | GNU Chess |
Luqman | 2016 | [18] | Custom robot with four DOFs | Chess | Camera, computer vision, based on color information | Not specified |
Manurung | 2023 | [28] | Gantry robot | Checkers | Camera, computer vision, based on color information | Minimax + alpha–beta pruning |
Matuszek | 2011 | [29] | Gambit robot | Chess | Camera, computer vision, based on model recognition and point cloud information | Not specified |
Rath | 2019 | [21] | Cartesian robot | Chess | Camera, computer vision, based on model recognition | Stockfish |
Rodriguez-Sedano | 2016 | [25] | Baxter robot | Checkers | The operator sees the board configuration through a camera | Not implemented |
Proposed approach | Franka Emika robot (minimum-time trajectories) | Italian checkers | Camera, computer vision based on color information | Minimax |
Component | Main Features |
---|---|
Computer |
|
Vision system |
|
Game engine |
|
Robotic player |
|
Checkerboard |
|
Pieces |
|
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Fabris, G.; Scalera, L.; Gasparetto, A. Playing Checkers with an Intelligent and Collaborative Robotic System. Robotics 2024, 13, 4. https://doi.org/10.3390/robotics13010004
Fabris G, Scalera L, Gasparetto A. Playing Checkers with an Intelligent and Collaborative Robotic System. Robotics. 2024; 13(1):4. https://doi.org/10.3390/robotics13010004
Chicago/Turabian StyleFabris, Giuliano, Lorenzo Scalera, and Alessandro Gasparetto. 2024. "Playing Checkers with an Intelligent and Collaborative Robotic System" Robotics 13, no. 1: 4. https://doi.org/10.3390/robotics13010004
APA StyleFabris, G., Scalera, L., & Gasparetto, A. (2024). Playing Checkers with an Intelligent and Collaborative Robotic System. Robotics, 13(1), 4. https://doi.org/10.3390/robotics13010004