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Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filters (extended and unscented) and particle filters. Key components of each Bayes filter are probabilistic prediction and observation models. Recently, Gaussian processes have been introduced as a(More)
We present an integrated approach to multirobot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting communications infrastructure. We present a set of algorithms that have been both implemented and experimentally verified on teams—of what we refer to as Centibots—consisting of as(More)
This paper considers the use of non-parametric system models for sequential state estimation. In particular, motion and observation models are learned from training examples using Gaussian process (GP) regression. The state estimator is an unscented Kalman filter (UKF). The resulting GP-UKF algorithm has a number of advantages over standard (parametric)(More)
GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filters and extended and unscented Kalman filters. GPBayesFilters have been shown to be extremely well suited for systems for which accurate parametric models are difficult to obtain.(More)
Blimps are a promising platform for aerial robotics and have been studied extensively for this purpose. Unlike other aerial vehicles, blimps are relatively safe and also possess the ability to loiter for long periods. These advantages, however, have been difficult to exploit because blimp dynamics are complex and inherently non-linear. The classical(More)
An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the relative locations of the individual robots. This paper(More)
A set of robots mapping an area can potentially combine their information to produce a distributed map more efficiently than a single robot alone. We describe a general framework for distributed map building in the presence of uncertain communication. Within this framework, we then present a technical solution to the key decision problem of determining(More)
ly, the zippering process lets us take any partial maps produced by any robots and put them together, once a common location (colocation) between their trajectories has been identified (note that colocation is transitive). In order to determine these common locations, we developed an efficient algorithm that sequentially estimates the relative locations(More)
Human level of dexterity has not been duplicated in a robotic form to date. Dexterity is achieved in part due to the biomechanical structure of the human body and in part due to the neural control of movement. We have developed an anatomically correct testbed (ACT) hand to investigate the importance and behavioral consequences of anatomical features and(More)
We have built an anatomically correct testbed (ACT) hand with the purpose of understanding the intrinsic biomechanical and control features in human hands that are critical for achieving robust, versatile, and dexterous movements, as well as rich object and world exploration. By mimicking the underlying mechanics and controls of the human hand in a hardware(More)