CM-Pack’01: Fast Legged Robot Walking, Robust Localization, and Team Behaviors

Uther, William; Lenser, Scott; Bruce, James; Höck, Martin; Veloso, Manuela

doi:10.1007/3-540-45603-1_115

Cited by 12 publications

(15 citation statements)

References 2 publications

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“…A probabilistic implementation with collaboration (based on [11] and [16]) has been implemented in simulation and will be applied to our RoboCup legged team in 2002. For curve constraints, Gaussian distributions are aligned tangent to the curve with means computed in the same manner as the approach described here.…”

Section: Discussionmentioning

confidence: 99%

Constraint-Based Landmark Localization

Stroupe

Sikorski

Balch

2003

RoboCup 2002: Robot Soccer World Cup VI

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Abstract. We present an approach to the landmark-based robot localization problem for environments, such as RoboCup middle-size soccer, that provide limited or low-quality information for localization. This approach allows use of different types of measurements on potential landmarks in order to increase landmark availability. Some sensors or landmarks might provide only range (such as field walls) or only bearing measurements (such as goals). The approach makes use of inexpensive sensors (color vision) using fast, simple updates robust to low landmark visibility and high noise. This localization method has been demonstrated in laboratory experiments and RoboCup 2001. Experimental analysis of the relative benefits of the approach is provided.

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Section: Discussionmentioning

confidence: 99%

Constraint-Based Landmark Localization

Stroupe

Sikorski

Balch

2003

RoboCup 2002: Robot Soccer World Cup VI

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“…In the RoboCup domain, the typical approach is to create mappings (1) from the YCbCr values to the color labels [26]. Other methods include the use of decision trees [27] and axis-parallel rectangles in the color space [28].…”

Section: Color Segmentation Learning and Color Constancymentioning

confidence: 99%

“…If dAvg lies outside the range of the distance distribution of the current illumination, but within the range of the distance distribution corresponding to an illumination for which the robot has learned a model, the robot transitions to using the corresponding color and illumination models. However, if dAvg lies outside the range of all known illuminations, the robot models a new illumination (Detect ma jor ) and learns models for color distributions (lines [25][26][27][28][29][30]. This adaptation scheme (Adapt ma jor ) cannot be used with a reduced threshold to handle minor illumination changes, because it could result in a large number of color maps for changes in a few distributions.…”

Section: Planned Illumination-invariant Color Learningmentioning

confidence: 99%

Bootstrap Learning and Visual Processing Management on Mobile Robots

Sridharan

2010

Advances in Artificial Intelligence

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A central goal of robotics and AI is to enable a team of robots to operate autonomously in the real world and collaborate with humans over an extended period of time. Though developments in sensor technology have resulted in the deployment of robots in specific applications the ability to accurately sense and interact with the environment is still missing. Key challenges to the widespread deployment of robots include the ability to learn models of environmental features based on sensory inputs, bootstrap off of the learned models to detect and adapt to environmental changes, and autonomously tailor the sensory processing to the task at hand. This paper summarizes a comprehensive effort towards such bootstrap learning, adaptation, and processing management using visual input. We describe probabilistic algorithms that enable a mobile robot to autonomously plan its actions to learn models of color distributions and illuminations. The learned models are used to detect and adapt to illumination changes. Furthermore, we describe a probabilistic sequential decision-making approach that autonomously tailors the visual processing to the task at hand. All algorithms are fully implemented and tested on robot platforms in dynamic environments.

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“…(See Figure 1) All relevant objects in the world are color-coded, allowing the identification of an object by its color. The camera information is processed to produce output in the form of (x, y, θ), oriented in the robot's local coordinate system, for all of the objects in the current field of view [11]. The objects that the vision is able to recognize are six color-coded markers at known locations around the field, two goals at either end of the field, the orange ball, and the other robots, which are either blue or red.…”

Section: Sources Of Knowledge For Building Statementioning

confidence: 99%

A real-time world model for multi-robot teams with high-latency communication

Roth

Vail

Veloso

Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)

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In this paper, we present in detail our approach to constructing a world model in a multi-robot team. We introduce two separate world models, namely an individual world model that stores one robot's state, and a shared world model that stores the state of the team. We present procedures to effectively merge information in these two world models in real-time. We overcome the problem of high communication latency by using shared information on an as-needed basis. The success of our world model approach is validated by experimentation in the robot soccer domain. The results show that a team using a world model that incorporates shared information is more successful at tracking a dynamic object in its environment than a team that does not use shared information.

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CM-Pack’01: Fast Legged Robot Walking, Robust Localization, and Team Behaviors

Cited by 12 publications

References 2 publications

Constraint-Based Landmark Localization

Constraint-Based Landmark Localization

Bootstrap Learning and Visual Processing Management on Mobile Robots

A real-time world model for multi-robot teams with high-latency communication

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