Research on self-adaptive decision-making mechanism for competition strategies in robot soccer

Shi, Haobin; Xu, Lincheng; Zhang, Lin; Pan, Wei; Xu, Genjiu

doi:10.1007/s11704-015-4210-7

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…This has been studied largely with UAVs (unmanned aerial vehicles) [7,8] and other deployed autonomous systems (such as the distributed sensing systems of [9] or the search-and-rescue platforms of [10]). Multi-agent approaches have also been explored [11], some of which utilize game theory models [12], while others use a "neglect"-based model [8,13]. Task-oriented approaches include [14,15], which focus on grasping and handovers, respectively, or [16,17] which focus on task scheduling and assessment within a human-specified task.…”

Section: Background 21 Robot Self-assessmentmentioning

confidence: 99%

Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots

Thierauf,

Law,

Frasca

et al. 2024

Machines

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For robots to become effective apprentices and collaborators, they must exhibit some level of autonomy, for example, recognizing failures and identifying ways to address them with the aid of their human teammates. In this systems paper, we present an integrated cognitive robotic architecture for a “robot apprentice” that is capable of assessing its own performance, identifying task execution failures, communicating them to humans, and resolving them, if possible. We demonstrate the capabilities of our proposed architecture with a series of demonstrations and confirm with an online user study that people prefer our robot apprentice compared to robots without those capabilities.

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Section: Background 21 Robot Self-assessmentmentioning

confidence: 99%

Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots

Thierauf,

Law,

Frasca

et al. 2024

Machines

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show abstract

“…En [17] se proponen estrategias que dividen el campo de juego en cuadrículas con el fin de crear vectores de posición de los jugadores y el balón, donde a partir de un vector actual de posiciones se crea un vector de posiciones deseado para los jugadores. En [18] utilizan un sistema de toma de decisiones auto adaptativo en la asignación de roles y tareas, a partir de los modelos matemáticos de las misiones que los robots deben desarrollar. Aprendizaje de sistemas de toma de decisiones mediante técnicas Q-Learning también es utilizado en [19] para la selección de estrategias y roles.…”

Section: Trabajos Relacionadosunclassified

Robot Soccer Strategy Based on Hierarchical Finite State Machine to Centralized Architectures

Guarnizo¹,

Arteche²

2016

IEEE Latin Am. Trans.

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Abstract-Coordination among the robots allows a robot soccer team to perform better through coordinated behaviors. This requires that team strategy is designed in line with the conditions of the game. This paper presents the architecture for robot soccer team coordination, involving the dynamic assignment of roles among the players. This strategy is divided into tactics, which are selected by a Hierarchical State Machine. Once a tactic has been selected, it is assigned roles to players, depending on the game conditions. Each role performs defined behaviors selected by the Hierarchical State Machine. To carry out the behaviors, robots are controlled by the lowest level of the Hierarchical State Machine. The architecture proposed is designed for robot soccer teams with a central decision-making body, with global perception. 200 games were performed against a team with constant roles, winning the 92.5% of the games, scoring more goals on average that the opponent, and showing a higher percent of ball possession. Student's t-test shows better matching with measurement uncertainty of the strategy proposed. This architecture allowed an intuitive design of the robot soccer strategy, facilitating the design of the rules for role selection and behaviors performed by the players, depending on the game conditions. Collaborative behaviors and uniformity within the players' behaviors during the tactics and behaviors transitions were observed.

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“…También se presenta alta complejidad en la programación de los generadores automáticos de reglas. En [44] utilizan un sistema de toma de decisiones auto adaptativo en la asignación de roles y tareas para un equipo de la FIRA MiroSot League, a partir de los modelos matemáticos de las misiones que los robots deben desarrollar y de la información ambiental del entorno. Una vez los roles y las tácticas son obtenidas, se asignan formaciones de juego calculando las trayectorias óptimas de los jugadores durante el partido.…”

Section: Trabajos En Arquitecturas De Equipounclassified

Arquitecturas Centralizadas de Coordinación. Extrapolación del Fútbol de Robots al Control de Modo de Operaciones de Micro-Redes

Guarnizo¹,

Guillermo²

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Research on self-adaptive decision-making mechanism for competition strategies in robot soccer

Cited by 11 publications

References 12 publications

Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots

Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots

Robot Soccer Strategy Based on Hierarchical Finite State Machine to Centralized Architectures

Arquitecturas Centralizadas de Coordinación. Extrapolación del Fútbol de Robots al Control de Modo de Operaciones de Micro-Redes

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