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Constraint Based World Modeling

Göhring D, Mellmann H., Gerasymova K., Burkhard H-D.

Fundamenta Informaticae

2008

Vol. 85, nr 1-4

123-137

Common approaches for robot navigation use Bayesian filters like particle filters, Kalman filters and their extended forms. We present an alternative and supplementing approach using constraint techniques based on spatial constraints between object positions. This yields several advantages. The robot can choose from a variety of belief functions, and the computational complexity is decreased by efficient algorithms. The paper investigates constraint propagation techniques under the special requirements of navigation tasks. Sensor data are noisy, but a lot of redundancies can be exploited to improve the quality of the result. We introduce two quality measures: The ambiguity measure for constraint sets defines the precision, while inconsistencies are measured by the inconsistency measure. The measures can be used for evaluating the available data and for computing best fitting hypothesis. A constraint propagation algorithm is presented.

Cooperative World Modeling in Dynamic Multi-Robot Environments

Göhring D, Burkhard H-D.

Fundamenta Informaticae

2007

Vol. 75, nr 1-4

281-294

In this paper we describe how a group of agents can commonly estimate the position of objects. Furthermore we will show how these modeled object positions can be used for an improved self localization. Modeling of moving objects is commonly done by a single agent and in a robo-centric coordinate frame because this information is sufficient for most low level robot control and it is independent of the quality of the current robot localization. Especially when many robots cooperate with each other in a partially observable environment they have to share and to communicate information. For multiple robots to cooperate and share information, though, they need to agree on a global, allocentric frame of reference. But when transforming the egocentric object model into a global one, it inherits the localization error of the robot in addition to the error associated with the egocentric model. We propose using the relation of objects detected in camera images to other objects in the same camera image as a basis for estimating the position of the object in a global coordinate system. The spacial relation of objects with respect to stationary objects (e.g., landmarks) offers several advantages: The information is independent of robot localization and odometry and it can easily be communicated. We present experimental evidence that shows how two robots are able to infer the position of an object within a global frame of reference, even though they are not localized themselves. We will also show, how to use this object information for self localization. A third aspect of this work will cope with the communication delay, therefore we will show how the Hidden Markov Model can be extended for distributed object tracking.