CMDragons: Dynamic passing and strategy on a champion robot soccer team

Bruce, James; Zickler, Stefan; Licitra, Mike; Veloso, Manuela

doi:10.1109/robot.2008.4543837

Cited by 17 publications

(13 citation statements)

References 15 publications

(18 reference statements)

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“…The main kicker is a solenoid attached directly to a kicking plate; the chip-kicking device is implemented by a flat solenoid located under the main kicker, which strikes an angled wedge to kick the ball at an angle. Ball catching and handling are performed by a motorized rubber-coated dribbling bar [11]. Robot behavior architecture overview Figure 1(b) shows the behavior architecture for our offboard control.…”

Section: Ssl: a Cmdragons Perspectivementioning

confidence: 99%

AutoRef: Towards Real-Robot Soccer Complete Automated Refereeing

Zhu

Biswas

Veloso

2015

RoboCup 2014: Robot World Cup XVIII

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Abstract. Preparing for robot soccer competitions by empirically evaluating different possible game strategies has been rather limited in leagues using real robots. Such limitation comes from factors related to the difficulty of extensively experimenting with games with real robots, such as their inevitable wear and tear and their usual limited number. RoboCup real robot teams have therefore developed simulation environments to enable experimentation. However, in order to run complete games in such simulation environments, an automated referee is needed. In this paper, we present AutoRef, as a contribution towards a complete automated referee for the RoboCup Small-Size League (SSL). We have developed and used AutoRef in an SSL simulation to run full games to evaluate different strategies, as we illustrate and show results. AutoRef is designed as a finite-state machine that transitions between the states of the game being either on or required to stop. AutoRef purposefully only uses the same visual and game information provided in SSL games with physical robots, which it uses to compute the features needed by the rules and to make decisions to transition between its states. Due to this real input to AutoRef, we have partially applied it to games of the physical robots. As AutoRef does not include all the rules of the real SSL games, we currently view it as an aid to human referees of SSL games, and discuss the challenges in automating several specific SSL game rules. AutoRef could be extended to other RoboCup real soccer leagues if a combined view of the game field, ball, and players is available.

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Section: Ssl: a Cmdragons Perspectivementioning

confidence: 99%

AutoRef: Towards Real-Robot Soccer Complete Automated Refereeing

Zhu

Biswas

Veloso

2015

RoboCup 2014: Robot World Cup XVIII

Self Cite

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“…These requirements led to a number of solutions developed, therefore, we recommend the readers interested by the topic of this paper to also read the papers describing the CMDragons [11], the B-Smart [22], and the Skuba [35] teams.…”

Section: Overviewmentioning

confidence: 99%

On the construction of a RoboCup small size league team

et al. 2011

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The Robot Soccer domain has become an important artificial intelligence test bench and a widely studied research area. It is a domain with real, dynamic, and uncertain environment, where teams of robots cooperate and face adversarial competition. To build a RoboCup Small Size League (SSL) team able to compete in the world championship requires multidisciplinary research in fields like robotic hardware development, machine learning, multi-robot systems, computer vision, control theory, and mechanics, among others.This paper intends to provide insights about the aspects involved on the development of the RoboFEI RoboCup SSL robot soccer team and to present the contributions produced over its course. Among these contributions, a computer vision system employing an artificial neural network (ANN) to recognize colors, a heuristic algorithm to recognize partially detected objects, an implementation of the known rapidlyexploring random trees (RRT) path planning algorithm with Murilo Fernandes Martins thanks the support of CAPES through the grant 5031/06-0. additional rules, enabling the angle of approach of the robot to be controlled, and a layered strategy software system.Experimental results on real robots demonstrate the high performance of the vision system and the efficiency of the RRT algorithm implementation. Some strategy functions are also experimented, with empirical results showing their effectiveness.

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“…We implemented our tag domain within the CMDragons robot soccer system [14,16], a champion robot soccer team from the Small Size League of the RoboCup robot soccer world championship [49]. In the tag domain, three holonomic (omni-directional) robots move on a playing field.…”

Section: The Robot Tag Domainmentioning

confidence: 99%

“…As a specific example, the CMDragons robot soccer team [16] defines robot behaviors hierarchically using the Skills, Tactics, and Plays (STP) architecture [13]. At the base of the hierarchy, skills are the lowest level behaviors of the robot, such as dribbling the ball or moving to a specific field location.…”

Section: Rolesmentioning

confidence: 99%

Conditional random fields for activity recognition

Vail

Veloso

Lafferty

2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

262

136

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To act intelligently in the presence of others, robots must use information from their sensors to recognize the behaviors and activities of the other agents in their environment. Robots must map from low-level, difficult to interpret data, such as position information extracted from video, to abstract states, in particular, the activities of the other agents. In this thesis, we explore how to bridge the gap from noisy, continuous observations about the world to high-level, discrete activity labels for robots in the environment.We contribute the use of conditional random fields (CRFs) for activity recognition in multirobot domains. We explore the appropriateness of CRFs with an empirical comparison to hidden Markov models. We elucidate the properties of CRFs that make them well suited to the activity recognition, namely discriminative training, the ability to robustly incorporate rich features of the observations, and their nature as conditional models, with a variety of synthetic and real robot data.Accurate activity recognition requires complex and rich features of the observations. We choose the most informative features from a large set of candidates using feature selection. We adapt two feature selection algorithms, grafting and 1 regularization, to conditional random fields. We also investigate a third feature selection algorithm, which was originally proposed for CRFs in a natural language processing domain, in an activity recognition context. In particular, we focus on scaling feature selection to very large sets of candidate features that we define succinctly using a rich relational feature specification language.The reduced feature sets that we discover via feature selection enable efficient, real-time inference. However, feature selection and training for conditional random fields is computationally expensive. We adapt an M-estimator, introduced by Jeon and Lin for log-density estimation in ANOVA models, for fast, approximate parameter estimation in CRFs. We provided an in depth, empirical evaluation of the properties of the M-estimator and then we introduce a new, efficient feature selection algorithm for CRFs based around M-estimation to identify the most important features.

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CMDragons: Dynamic passing and strategy on a champion robot soccer team

Cited by 17 publications

References 15 publications

AutoRef: Towards Real-Robot Soccer Complete Automated Refereeing

AutoRef: Towards Real-Robot Soccer Complete Automated Refereeing

On the construction of a RoboCup small size league team

Conditional random fields for activity recognition

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