Development of Stone Throwing Robot and High Precision Driving Control for Curling

Choi, Jung Hyun; Song, Changyong; Kim, Kyung-Hwan; Oh, Sehoon

doi:10.1109/iros.2018.8594026

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…Of the artificially intelligent (AI) curling robots, fabricated features included throwing controls to test precision and throwing accuracy, AI-based strategy simulators, vision technology to recognize the curling field and stone configuration/trajectory, and sweeping systems to deliver path planning strategies for sweeping ( 35 , 47 – 49 ). Artificial agents were equipped with autonomous driving and friction feedback for traction control ( 45 , 47 ). Of the studies that orchestrated curling matches, AI machines established a winning record while outperforming human counterparts ( 35 , 45 , 46 ).…”

Section: Resultsmentioning

confidence: 99%

An examination of studies related to the sport of curling: a scoping review

Zacharias,

Robak,

Passmore

2024

Front. Sports Act. Living

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IntroductionThere has been growth in research in the sport of curling over the past few decades. The need for a scoping review is warranted. This study's purpose was to identify and synthesize research evidence regarding quantitative variables for a series of components within the sport of curling.MethodsA scoping review of studies published and established within four databases was performed. One independent reviewer selected studies based on a systematic procedure. Inclusion criteria for studies were: (1) interventions that focused on the sport of curling; (2) quantitative in nature; (3) written in English; and (4) published within a peer-reviewed journal, a conference presentation, or a published thesis.ResultsSearching identified 8,467 articles and 94 met the inclusion criteria. Data were organized and synthesized based on the devised research variables from the sport of curling: The curl mechanism of the curling stone; the impact of sweeping on stone trajectory; curling delivery mechanics; wheelchair curling; technology analysis; strategy and tactics; psychological factors; injury occurrences; facility and arena infrastructure; and assessment of curling training and ability. The findings confirm the strong knowledge base that exists across game variables and unveil controversy between the underlying physics that produces curl, as well as the mechanisms of sweeping responsible for manipulating the stone trajectory.ConclusionsKnowledge derived from this review can assist researchers, coaches, and curlers in addressing the specific variables of the sport that influence stone trajectory and game results. Such awareness will expose gaps in the current understanding and foster new research endeavors to further the knowledge of the sport.

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Section: Resultsmentioning

confidence: 99%

An examination of studies related to the sport of curling: a scoping review

Zacharias,

Robak,

Passmore

2024

Front. Sports Act. Living

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“…As a result, our robot has an accuracy of 0.1° and 0.01 m/s error with respect to target velocity and angle [c.f. (55) for further details about the precision driving control applied to Curly]. The third step is the hog line release phase, one of the important rules to obey in the curling game.…”

Section: Precision-controlled In Thrower-curlymentioning

confidence: 99%

An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions

2020

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The game of curling can be considered a good test bed for studying the interaction between artificial intelligence systems and the real world. In curling, the environmental characteristics change at every moment, and every throw has an impact on the outcome of the match. Furthermore, there is no time for relearning during a curling match due to the timing rules of the game. Here, we report a curling robot that can achieve human-level performance in the game of curling using an adaptive deep reinforcement learning framework. Our proposed adaptation framework extends standard deep reinforcement learning using temporal features, which learn to compensate for the uncertainties and nonstationarities that are an unavoidable part of curling. Our curling robot, Curly, was able to win three of four official matches against expert human teams [top-ranked women’s curling teams and Korea national wheelchair curling team (reserve team)]. These results indicate that the gap between physics-based simulators and the real world can be narrowed.

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“…The expected human-robot distance with respect to time is depicted in the left figure of Figure 8, where it can be found that the robot stays at its position till the distance reaches r s , and the distance is kept less than r d by the proposed algorithm even when the user moves (area B). The deceleration reference can be given by (7), as the robot approaches the human, the velocity reference decreases until it becomes 0 when r n approaches r s . Aligning mode is required when the orientation of the robot is not towards the user.…”

Section: Velocity and Orientation Reference Generation Algorithmmentioning

confidence: 99%

“…Recently in sports, there has been developments of robots that indirectly interact with humans even though their functions are limited to basic levels of sports activities such as catching a baseball [5], kicking a football [6], and even throwing the stone in a curling game [7]. In this regard, golf, being another popular sport, has had various robots being developed and applied for training sessions.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Human Following Caddie Robot with High-Level Driving Functions

et al. 2020

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Nowadays, mobile robot platforms are utilized in various fields not only for transportation but also for other diverse services such as industrial, medical and, sports, etc. Mobile robots are also an emerging application as sports field robots, where they can help serve players or even play the games. In this paper, a novel caddie robot which can autonomously follow the golfer as well as provide useful information such as golf course navigation system and weather updates, is introduced. The locomotion of the caddie robot is designed with two modes: autonomous human following mode and manual driving mode. The transition between each mode can be achieved manually or by an algorithm based on the velocity, heading angle, and inclination of the ground surface. Moreover, the transition to manual mode is activated after a caddie robot has recognized the human intention input by hand. In addition, the advanced control algorithm along with a trajectory generator for the caddie robot are developed taking into consideration the locomotion modes. Experimental results show that the proposed strategies to drive various operating modes are efficient and the robot is verified to be utilized in the golf course.

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Development of Stone Throwing Robot and High Precision Driving Control for Curling

Cited by 7 publications

References 15 publications

An examination of studies related to the sport of curling: a scoping review

An examination of studies related to the sport of curling: a scoping review

An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions

An Autonomous Human Following Caddie Robot with High-Level Driving Functions

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