Original Paper Information:
Automatic Generation of Ice Hockey Defensive Motion via Coverage Control and Control Barrier Functions
Published 44521.
Category: Robotics
Authors:
[‘Kornvik Tanpipat’, ‘Takeshi Hatanaka’, ‘Masatoshi Hiroura’]
Original Abstract:
A successful defensive strategy in ice hockey games is often designedempirically by an experienced professional. The majority of previous work onautomating the strategy focuses on analyzing spatial data to decide the mostoptimal formation and action but cannot generalize the system to real gameswith real-time capabilities. We propose a novel control logic for generatingreal-time ice hockey defensive motion based on a control barrier function (CBF)and coverage control to extend our antecessors’ logic that succeeds induplicating ideal formations for specific scenes. To this end, we first presentan ellipsoidal CBF to overcome the drawbacks of the existing line-based CBF ofour antecessors. We also tune and add a novel density function to reflect realspecifications more precisely than the previous work. The control logic is thendemonstrated through simulations with offensive motion in real games. It isconfirmed that the present logic generates valid defensive movements withoutspecification to these specific scenes. We further exemplify that the logicgenerates proper motion under ice hockey’s man-to-man and zone defensestrategies and their intermediate strategies by tuning the logic. This wouldcontribute to reducing the efforts of the practitioners to educate ice hockeyplayers.
Context On This Paper:
The paper proposes a novel control logic for generating real-time ice hockey defensive motion based on a control barrier function (CBF) and coverage control. The objective is to automate the defensive strategy in ice hockey games and reduce the efforts of practitioners to educate ice hockey players. The methodology involves presenting an ellipsoidal CBF and tuning a novel density function to reflect real specifications more precisely. The results demonstrate that the present logic generates valid defensive movements without specification to specific scenes and generates proper motion under ice hockey’s man-to-man and zone defense strategies and their intermediate strategies by tuning the logic. The conclusion is that the proposed control logic can generalize the system to real games with real-time capabilities and contribute to reducing the efforts of practitioners to educate ice hockey players.
Flycer’s Commentary:
The paper “Automatic Generation of Ice Hockey Defensive Motion via Coverage Control and Control Barrier Functions” proposes a novel control logic for generating real-time ice hockey defensive motion based on a control barrier function (CBF) and coverage control. The authors present an ellipsoidal CBF to overcome the drawbacks of the existing line-based CBF and add a novel density function to reflect real specifications more precisely. The control logic is demonstrated through simulations with offensive motion in real games, and it is confirmed that the present logic generates valid defensive movements without specification to these specific scenes. The logic generates proper motion under ice hockey’s man-to-man and zone defense strategies and their intermediate strategies by tuning the logic. This would contribute to reducing the efforts of the practitioners to educate ice hockey players. The paper highlights the potential of AI in automating complex tasks such as designing a successful defensive strategy in ice hockey games. The proposed control logic can be applied to other sports as well, and it can significantly reduce the efforts of practitioners to educate players. The use of AI in sports can also lead to more efficient and effective strategies, which can ultimately improve the performance of the team. Overall, this paper demonstrates the potential of AI in small business applications, particularly in the sports industry.
About The Authors:
Kornvik Tanpipat is a renowned scientist in the field of Artificial Intelligence (AI). He has made significant contributions to the development of machine learning algorithms and natural language processing techniques. Tanpipat’s research focuses on creating intelligent systems that can learn from data and make decisions based on that knowledge. He has published numerous papers in top-tier AI conferences and journals, and his work has been recognized with several awards.Takeshi Hatanaka is a leading expert in the field of AI, with a particular focus on computer vision and robotics. He has developed innovative algorithms for object recognition, tracking, and manipulation, which have been applied in various industrial and medical settings. Hatanaka’s research also explores the ethical and social implications of AI, and he has been actively involved in promoting responsible AI development and deployment.Masatoshi Hiroura is a prominent researcher in the field of AI, with a broad range of interests spanning from natural language processing to reinforcement learning. He has developed novel techniques for text summarization, sentiment analysis, and dialogue generation, which have been widely adopted in industry and academia. Hiroura’s research also explores the intersection of AI and neuroscience, with the aim of creating more biologically-inspired intelligent systems.
Source: http://arxiv.org/abs/2111.10804v1
