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This paper presents a laser scanner based reverse engineering (RE) system that generates accurate 3D solid models of bottles. The proposed system is designed for ease of use and has an open architecture, making it an ideal solution for bottle conveying system manufacturers. Conveyor system manufactures need a versatile RE system that can create 3D models of bottles to develop conveyor systems for their clients. This paper also presents a simple, robust filtering technique that can effectively remove the noise from the scanned laser data, yet retaining the shape of the scanned bottle. An error analysis is performed to compare our technique against another common data filtering technique, and sample 3D models are generated to contrast the effects of the different filtering approaches.

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. This paper proposes an approach for online learning of the inverse dynamics model using Gaussian Process Regression. The Sparse Online Gaussian Process (SOGP) algorithm is modified to allow for incremental updates of the model and hyperparameters. The influence of initialization on the performance of the learning algorithms, based on any a-priori knowledge available, is also investigated. The proposed approach is compared to existing learning and fixed control algorithms and shown to be capable of fast initialization and learning rate.

High performance control of robotic systems, including the new generation of humanoid, assistive and entertainment robots, requires adequate knowledge of the dynamics of the system. This can be problematic in the presence of modeling uncertainties as the performance of classical, modelbased controllers is highly dependant upon accurate knowledge of the system. In addition, future robotic systems such as humanoids are likely to be redundant, requiring a mechanism for redundancy resolution when performing lower degree-of-freedom tasks. In this paper, a learning approach to estimating the inverse dynamic equations is presented. Locally Weighted Projection Regression (LWPR) is used to learn the inverse dynamics of a manipulator in both joint and task space and the resulting controllers are used to drive a 3 and 4 DOF robot in simulation. The performance of the learning controllers is compared to a traditional model based control method and is also shown to be a viable control method for a redundant system.