
Analysis and solution for the curling stone throwing of a novel six-legged curling robot in curling competition
Yuguang XIAO, Ke YIN, Yue ZHAO, Zhijun CHEN, Feng GAO
Front. Mech. Eng. ›› 2025, Vol. 20 ›› Issue (3) : 19.
Analysis and solution for the curling stone throwing of a novel six-legged curling robot in curling competition
In curling competitions, the throwing strategy has a decisive influence on the outcome of the game. When robots are applied to the sport of curling, they first need to understand the various throwing strategies in curling competitions and then adjust their motion control parameters to achieve the corresponding strategic throws. However, current curling strategy research lacks mathematical analysis and descriptive methods for throwing strategies tailored to robots. Moreover, research on how robots can solve for corresponding throwing strategies is lacking. These limitations have restricted the application and development of curling robots in the sport. Here, the concepts of the curling stone’s hitting domain and hitting tree are introduced to analyze and describe the curling strategies for robots by constructing the curling hitting domain through a curling collision model and by building the hitting tree through operations such as combination, permutation, and pruning. Furthermore, based on the solution methods for hitting domains and hitting trees, a search solution method for the control parameters of robots is developed. The research findings are integrated into a curling robot auxiliary decision-making software. With the help of the auxiliary software, the curling robot achieves victory in competitions against humans. The research outcomes are of great importance for the application and development of curling robots and legged robots.
curling robot / curling strategy / legged robot / control parameters
[1] |
Bradley J L. The sports science of curling: a practical review. Journal of Sports Science & Medicine, 2009, 8(4): 495–500
|
[2] |
Denny M. Curling rock dynamics. Canadian Journal of Physics, 1998, 76(4): 295–304
|
[3] |
Denny M. Curling rock dynamics: Towards a realistic model. Canadian Journal of Physics, 2002, 80(9): 1005–1014
CrossRef
Google scholar
|
[4] |
Jensen E T, Shegelski M R. The motion of curling rocks: Experimental investigation and semi-phenomenological description. Canadian Journal of Physics, 2004, 82(10): 791–809
CrossRef
Google scholar
|
[5] |
KawamuraT, Kamimura R, SuzukiS, IizukaK. A study on the curling robot will match with human result of one end game with one human. In: the IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2015, 489–495
|
[6] |
XiangS. Design and simulation of curling robot based on sysmac control platform. Shandong Industrial Technology, 2017, 22: 127–129 (in Chinese)
|
[7] |
ChoiJ H, Song C, KimK, OhS. Development of stone throwing robot and high precision driving control for curling. In: the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid: IEEE, 2018, 2434–2440
|
[8] |
Kim K, Song C, Jin W. Design and implementation of wheeled curling robots. Workshop on Curling Informatics. Information Processing Society of Japan, 2018,
|
[9] |
WonD OKim B DKimH JEomT SMullerK R LeeS W. Won D OKimB DKimH JEomT S MullerK RLee S W. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18). Stockholm: AAAI Press, 2018, 5883–5885
|
[10] |
Won D O, Müller K R, Lee S W. An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions. Science Robotics, 2020, 5(46): eabb9764
CrossRef
Google scholar
|
[11] |
PangJ. Research on visual detection and control method of curling robot. Thesis for the Master’s Degree. Harbin: Harbin Institute of Technology, 2022 (in Chinese)
|
[12] |
Willoughby K A, Kostuk K J. An analysis of a strategic decision in the sport of curling. Decision Analysis, 2005, 2(1): 58–63
CrossRef
Google scholar
|
[13] |
Park S G, Lee S. Curling analysis based on the possession of the last stone per end. Procedia Engineering, 2013, 60: 391–396
CrossRef
Google scholar
|
[14] |
AhmadZ FHolte R CBowlingM. Action selection for hammer shots in curling. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16). New York: AAAI Press, 2016, 561–567
|
[15] |
Kostuk K J, Willoughby K A, Saedt A P H. Modelling curling as a Markov process. European Journal of Operational Research, 2001, 133(3): 557–565
CrossRef
Google scholar
|
[16] |
OhtoKTetsuro T. A curling agent based on the Monte-Carlo tree search considering the similarity of the best action among similar states. In: the 15th International Conferences of Advances in Computer Games (ACG 2017). Leiden, The Netherlands: Springer International Publishing, 2017, 151–164
|
[17] |
Lee J, Jeon W, Kim G H, Kim K E. Monte-Carlo tree search in continuous action spaces with value gradients. In: Proceedings of the AAAI conference on artificial intelligence. AAAI Press, 2020, 34(4): 4561–4568
|
[18] |
Silva C R, Bowling M, Lelis L H S. Teaching people by justifying tree search decisions: An empirical study in curling. Journal of Artificial Intelligence Research, 2021, 72: 1083–1102
CrossRef
Google scholar
|
[19] |
Han Y, Zhou Q, Duan F. A game strategy model in the digital curling system based on NFSP. Complex & Intelligent Systems, 2022, 8(3): 1857–1863
CrossRef
Google scholar
|
[20] |
YamamotoM, Kato S, IizukaH. Digital curling strategy based on game tree search. In: 2015 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2015: 474–480
|
[21] |
Lee K, Kim S A, Choi J, Lee S W. Deep reinforcement learning in continuous action spaces: a case study in the game of simulated curling. the International conference on machine learning, 2018,
|
[22] |
LinJGongY ZhaoJZhou WLiH. Mastering curling with RL-revised decision tree. In: 2023 IEEE Conference on Games (CoG). IEEE, 2023, 1–8
|
[23] |
LozowskiE PSzilder KMawSMorrisAPoirierL KleinerB. Towards a first principles model of curling ice friction and curling stone dynamics. In: the International Ocean and Polar Engineering Conference (ISOPE). ISOPE, 2015, ISOPE-I-15–487
|
[24] |
HarringtonE L. An experimental study of the motion of curling stones. Proceedings and Transactions of Royal Society of Canada, 1924: 247–258
|
[25] |
Penner A R. The physics of sliding cylinders and curling rocks. American Journal of Physics, 2001, 69(3): 332–339
CrossRef
Google scholar
|
[26] |
Macaulay W H, Smith G E. Curling: letter to the editor. Nature, 1930, 125(3150): 408–409
CrossRef
Google scholar
|
[27] |
Maeno N. Dynamics and curl ratio of a curling stone. Sports Engineering, 2014, 17(1): 33–41
CrossRef
Google scholar
|
[28] |
Maeno N. Curl mechanism of a curling stone on ice pebbles. Bulletin of Glaciological Research, 2010, 28: 1–6
CrossRef
Google scholar
|
[29] |
Nyberg H, Alfredson S, Hogmark S, Jacobson S. The asymmetrical friction mechanism that puts the curl in the curling stone. Wear, 2013, 301(1–2): 583–589
CrossRef
Google scholar
|
[30] |
Nyberg H, Hogmark S, Jacobson S. Calculated trajectories of curling stones sliding under asymmetrical friction: validation of published models. Tribology Letters, 2013, 50(3): 379–385
CrossRef
Google scholar
|
Abbreviations | |
AI | Artificial intelligence |
KR-UCT | Kernel regression-upper confidence bound applied to trees |
MCTS | Monte-Carlo tree search |
PTFE | Polytetrafluoroethylene |
RL | Reinforcement learning |
Variables | |
ai | Coefficients of a seven-degree polynomial trajectory curve (i = 0,1,2,...,7) |
acs | Acceleration of the robot and the stone |
ar | Curling stone’s radial acceleration |
at | Curling stone’s tangential acceleration |
d | Gliding distance of the robot in this stage |
dk | Movement distance of the robot kicking |
dp | Distance of the robot pushing stone |
Ev | Overall kinetic energy of the robot and stone |
Ef | Work done by the friction at this stage |
F | Friction force |
Fb | Frictions of the back parts of the curling stone |
Fbr | Radial frictions of the back parts of the curling stone |
Fbt | Tangential frictions of the back parts of the curling stone |
Ff | Frictions of the front parts of the curling stone |
Ffr | Radial frictions of the front parts of the curling stone |
Fft | Tangential frictions of the front parts of the curling stone |
Fr | Radial frictions of the curling stone |
Ft | Tangential frictions of the curling stone |
I | Inertia moment of the curling stone |
m | Curling stone mass |
mA | Mass of curling stone A |
mb | Robot mass |
mB | Mass of curling stone B |
mc | Mass of curling stone |
mf | Mass of the front parts of the curling stone |
n | Total number of curling stones on the ice |
Ob1 | Center of the robot body before angle adjustment |
Ob1−xyz | Robot body coordinate frame |
Ob2 | Center of the robot body after angle adjustment |
OL−xyz | LiDAR coordinate system |
OM | Center of the tips of the rear legs |
OW−xyz | World coordinate system |
P | Position vector of the front/back part of the curling stone |
Pb1 | Position of Ob1 in the world coordinate system |
b1Pb2 | Robot body position in the robot body coordinate frame (Ob1−xyz) |
PNM | Position vector from curling stone N to curling stone M |
Ptipi | Positions of the tips of each leg in the world coordinate system |
qr | Real-time position of the robot |
q(t0) | Robot position at the beginning of the kicking stage |
q(t1) | Robot position at the end of the kicking stage |
r | Rotation radius |
r | Position vector of the front/back part of the curling stone with respect to the center of the curling stone |
R | Position vector of the center of the curling stone |
WRb1 | Robot body posture in the world coordinate frame before the angle adjustment |
SDD″ | Distance from point D to point D″ |
t | Real time |
t0 | Time of the beginning of the kicking stage |
t1 | Time of the end of the kicking stage |
t2 | Time of the end of the gliding stage |
t3 | Time of the end of the pushing curling stone stage |
tg | Glide time of the robot and the stone |
vb | Curling robot velocity |
vc | Curling stone velocity |
vcs | Velocity of the robot and the stone |
vk | Robot’s velocity after kicking |
vp | Forearm push velocity reach |
vr | Radial velocity component |
vt | Tangential velocity component |
vX | Speed of stone at the time of the collision (X = A,B) |
vXn | Normal velocity component of stone at the time of the collision (X = A,B) |
Normal velocity component of stone after collision (X = A,B) | |
vXt | Tangential velocity component of stone at the time of the collision (X = A,B) |
Tangential velocity component of stone after collision (X = A,B) | |
VD | Stone D collision velocity |
VD′′ | Velocity of the stone D at point D′′ |
VX′ | Speed of stone after collision (X = A,B) |
θ | Target angle |
φ | Angle between the curling stone’s central position vector and the polar axis |
ω | Rotational angular velocity |
μ | Friction coefficient between curling stone and ice surface |
μAct | Actual friction coefficient between curling stone and ice surface |
μb | Friction coefficient between the curling stone back part and ice surface |
μc | Friction coefficient between the stone and the ice |
μCal | Calculated friction coefficient between curling stone and ice surface |
μf | Friction coefficient between the curling stone front part and the ice surface |
Angle between tangential velocity and radial velocity | |
Angle of the position vector | |
Angle of the position vector | |
Angle of the position vector | |
Angle of the position vector | |
Angle of the position vector |
/
〈 |
|
〉 |