Fast and inexpensive color image segmentation for interactive robots

Bruce, James; Balch, Tucker; Veloso, Manuela

doi:10.1109/iros.2000.895274

Cited by 325 publications

(238 citation statements)

References 4 publications

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“…The robots are controlled via radio commands sent by its team's offboard computer. Overhead cameras observe the field, one for each half, and the vision data is processed on a neutral computer by the SSL Vision System (SSL-Vision) [1] based on the early successful CMVision [9]. SSL-Vision broadcasts the detected locations and orientations of all the robots and the ball on the field to the team computers via a wired Ethernet network.…”

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

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“…Thus, before processing the captured frames, we undistort the images with a set of calibration parameters discussed in [22]. The marker positions are extracted using the technique presented in [23]. As we know that there is uncertainty in the image, assuming the extraction process treats the noise properly, the least amount of uncertainty associated is therefore 0.5 pixel.…”

Section: Implementation Of the Algorithmmentioning

confidence: 99%

Hierarchical learning approach for one-shot action imitation in humanoid robots

Demiris

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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Abstract-We consider the issue of segmenting an action in the learning phase into a logical set of smaller primitives in order to construct a generative model for imitation learning using a hierarchical approach. Our proposed framework, addressing the "how-to" question in imitation, is based on a oneshot imitation learning algorithm. It incorporates segmentation of a demonstrated template into a series of subactions and takes a hierarchical approach to generate the task action by using a finite state machine in a generative way. Two sets of experiments have been conducted to evaluate the performance of the framework, both statistically and in practice, through playing a tic-tac-toe game. The experiments demonstrate that the proposed framework can effectively improve the performance of the one-shot learning algorithm and reduce the size of primitive space, without compromising the learning quality.

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“…For example, Bruce et al [3] implemented a segmentation system capable of tracking several hundred regions. It can classify each pixel in a full resolution captured color image.…”

Section: Related Workmentioning

confidence: 99%

Landmark manipulation system for mobile robot navigation

Elmogy

2010

The 2010 International Conference on Computer Engineering &Amp; Systems

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Abstract-In mobile robot scenarios, it is expected that the robot autonomously navigates through home or office environments and processes objects/landmarks during navigation. Landmark manipulation is identified as one important research area in robot navigation systems. We have developed an online robot landmark processing system (RLPS) to detect, classify, and localize different types of landmarks during robot navigation. The RLPS is based on a two-step classification stage which is robust and invariant towards scaling and translations. It provides a good balance between fast processing time and high detection accuracy by combining the strengths of appearancebased and model-based object classification techniques. The experimental results showed that the RLPS is more powerful as it recognizes a wide range of landmarks and efficiently handles landmarks with occlusions, viewpoint variances, and illumination changes.

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Fast and inexpensive color image segmentation for interactive robots

Abstract: Abstract

Cited by 325 publications

References 4 publications

AutoRef: Towards Real-Robot Soccer Complete Automated Refereeing

AutoRef: Towards Real-Robot Soccer Complete Automated Refereeing

Hierarchical learning approach for one-shot action imitation in humanoid robots

Landmark manipulation system for mobile robot navigation

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