Computer Vision Based Chess Playing Capabilities for the Baxter Humanoid Robot

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1 International Conference on Control, Robotics, and Automation 2016 Computer Vision Based Chess Playing Capabilities for the Baxter Humanoid Robot Andrew Tzer-Yeu Chen, Kevin I-Kai Wang {andrew.chen,

2 Baxter Humanoid Robot Designed for industrial automation Compliant, safe to use around humans (no safety cage!) Possible human-robot interaction applications

3 Chess Robots The Mechanical Turk, c.1783 KUKA Monster and CHESSka robots,

4 Chess Robots There are three problems to solve: Perception What is the current game state? Actuation How do I play the move? Computation What is the best next move?

5 Perception Most implementations simplify the problem significantly DGT boards use sensors embedded in the board and pieces to detect position or occupancy Boards often have to be in fixed positions When CV is used, simplifications include: Non-standard colours [2] Mounting the camera directly above the board [3][4]

6 Perception Baxter has an embedded camera in the arm

7 Perception We process a 1280x800 image in OpenCV Camera held at 0.4 radians from the perpendicular START (Input Image) Image Preparation Morphologically Closed and Gaussian Blurred Board Detection Square Detection Piece Detection Output Board Configuration Dynamic Hough Line Transform Final output with detected squares and pieces Dilated Canny Edge Detection, Merged Piece Detection

8 Dynamic Hough Line Transform Iterative determination of appropriate vote threshold Segments image and uses different thresholds Reduces impact of perspective distortion Vote thresholds of 100, 150, 200, 250, 300, our dynamic hough line transform, final output

9 Robust Occupancy Detection Method A: Canny edge detection + morphological closing Method B: Morphological edge detection Hybrid approach more resistant to lighting changes/grads a) greyscale, b) after contrasting and morphological closing, c) occupancy for method A, d) occupancy for method B, e) final occupancy. False positives from method A and B alone highlighted in red

10 Perception Game state perception reasonably resistant to position, orientation, and lighting changes Differential image approach What changed in the game state between frames? Square numbers of moved piece(s) identified Converted to UCI algebraic format (e.g. g1f3) Move is handed over to the computation subsystem

11 Computation Last moved piece fed in from perception subsystem Chessnut 1 for move validation Open-source chess engine Stockfish 2 is used PyStockfish 3 used to wrap it into Python User selects difficulty level corresponding to search depth Baxter s move is sent to mechatronics subsystem

12 Actuation Baxter s arm has 7 degrees of freedom We use IKFast! to do inverse kinematics calculations Co-ordinates dynamically calculated based on CV

13 Overall System Control Finite State Machine Model Baxter s screen (face) used to improve HCI Baxter concentrates when it is his move Baxter goes to sleep when the human takes too long Baxter celebrates when he wins (he always wins)

14 Future Work Maybe replace mechatronics subsystem with accurate arm that is also much faster Fault tolerance to automatically correct situations where pieces are knocked over Use of artificial intelligence (AI) to identify piece types Further development of facial expressions (HCI) Investigating other small scale manipulation tasks Other board or card games Solving pick-and-place style puzzles Dispensing medication

15 Conclusions Chess playing robot Using CV techniques without unnecessary simplifcations No DGT board Without non-standard board colours Without a fixed camera position directly above the board With variable lighting conditions With small variations in board orientation With changes in board position High-level open-source tools used to integrate OpenCV, Stockfish, IKFast for perceive-compute-actuate cycle Useful case study for HCI research with Baxter Improvement upon existing CV chess analysis Video:

16 Perception Tested with varying board orientations and lighting conditions, including extreme (unlikely) scenarios Computer Vision Algorithm Detection Accuracy (%) Test Set BOARD Square Method A Method B Hybrid Piece Piece Piece Full Set (N=270) With Correct Square Detection (N=241) Normal lighting conditions (N=38)

17 Perception START (Input Image) Convert to Greyscale Image Preparation Apply Gaussian Blurring Determine Otsu Threshold Canny Edge Detection, Dilate Board Detection Find Square Contours Slice Image, Calculate Angles Square Detection Apply CLAHE Contrasting Piece Detection Morphological Edge Detection Apply Gaussian Blurring Canny Edge Detection, Dilate Dynamic Hough Line Transform Morphological Closing Merge Images and Remove Noise Find Square Contours, Sort Create Masks for each Square Calculate Intensity Average Calculate Centroid Intensity Determine Piece Presence Output Board Configuration

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