Multi modal user interaction in an automatic pool trainer

Larsen, Lars Bo; Jensen, Morten Damm; Vodzi, Wisdom Kobby

doi:10.1109/icmi.2002.1167022

Cited by 11 publications

(8 citation statements)

References 1 publication

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“…Another interesting aspect to be investigated was the usage of augmented reality tools to help guide and teach new players how to play the game or better select shots on the table. Attempts were made in [17] and [13] with interesting results. Some work has even been done in video analysis in [14] Manuscript received September 24, 2012 to extract information from tournaments played on television to receive game summaries.…”

Section: Introductionmentioning

confidence: 98%

A Heuristic-Based Planner and Improved Controller for a Two-Layered Approach for the Game of Billiards

Landry¹,

Dussault²,

Mahey³

2013

IEEE Trans. Comput. Intell. AI Games

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In the past , we have proposed a two-layered approach to compute a winning strategy for the game of Billiards. AI tools as well as robust optimization routines for noisy environments were combined to plan the sequence of shots. We complete the modeling here by introducing significant developments for the highlevel planner which guides the precise optimal controller to generate a plan given at any random initial state. We will first resume the general model for this particular class of problems and then propose several domain-specific heuristics to guide our search and render the problem tractable. Several improvements to the optimal robust controller, including refinements in the objective function, will also be presented in order to improve single-shot optimization. Results are presented demonstrating the full potential of the methods proposed making it the state of the art in regards to the game of Billiards.

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Section: Introductionmentioning

confidence: 98%

A Heuristic-Based Planner and Improved Controller for a Two-Layered Approach for the Game of Billiards

Landry¹,

Dussault²,

Mahey³

2013

IEEE Trans. Comput. Intell. AI Games

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“…The first effort to develop a robotic system to play a cue sport was the SNOOKER MACHINE of Khodabandehloo et al (Shu, 1994), which was developed in the early 1990s. There are currently a number of such systems under development, including DEEP GREEN (Long et al, 2004), the POOL SHARC (Alian et al, 2004), and others (Larsen, Jensen, and Vodzi, 2002;Chua et al, 2002;Chua, Wong, and Koo, 2003;Lin, Yang, and Yang, 2004). To play pool robotically is a challenging objective; the even greater objective is to achieve a level of play strong enough to compete against a proficient human.…”

Section: Introductionmentioning

confidence: 99%

Pool Physics Simulation by Event Prediction 1: Motion Transitions

Leckie¹,

Greenspan²

2005

ICG

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A method to simulate the physics of the game of pool is presented. The method is based upon a parametrization of ball motion which allows the time of occurrence of events, such as collisions and transitions between motion states, to be solved analytically. The method is both accurate, returning exact analytical solutions for both time and space parameters, and efficient, requiring no iterative numerical methods. It is suitable for use within a game-tree search, which requires a great many potential shots to be modelled efficiently, and within a robotic pool system, which requires high accuracy in predicting shot outcomes.

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“…The APT has existed in a number of different versions ( [1], [3], [5], [7], [9], [10], [11], [12], [13]). Information about the the automatic pool trainer, movie clips, articles etc., can be downloaded from the pool trainer homepage 1 .…”

Section: Introductionmentioning

confidence: 99%