Filtering Motion Data Through Piecewise Polynomial Approximation

In this work we propose a system to filter human movement data and store them into a compact representation. We are interested both in noise reduction and in segmentation. The method described in this paper relies on a iterative optimization and guarantee to converge to a local optimum: it proved anyway to produce stable results and to provide an accurate segmentation on the analyzed data . We analyze the Three ball cascade Juggling as case study: This provides us the challenge to represent both low-pass dynamics of human limbs and juggled balls and the discontinuities produced by contact forces

Haptic Rendering of Juggling with Encountered Haptic Interfaces.

Haptic interaction in a virtual world can be tool mediated or direct; and, among direct interactions, the encountered haptic interfaces provide physical contact only when there is contact with a virtual object. This paper deals with the haptic rendering of the catching and throwing of objects by means of this type of interface. A general model for the rendering of the impact is discussed with the associated formalism for managing multiple objects and multiple devices. Next, a key parameter for simulating the impact is selected by means of a psychophysical test. Finally, a working system is presented with the application of the rendering strategy to the case of haptic juggling, showing the possibility of effectively performing basic juggling patterns with two balls

Haptic Rendering of Sharp Objects Using Lateral Forces

Achieving realistic rendering of thin and spatially sharp objects (needles, for example) is an important open problem in computer haptics. Intrinsic mechanical properties of users, such as limb inertia, as well as mechanical and bandwidth limitations in haptic interfaces make this a very challenging problem. A successful rendering algorithm should also provide stable contact with a haptic virtual object. Here, perceptual illusions have been used to overcome some of these limitations to render objects with perceived sharp features. The feasibility of the approach was tested using a haptics-to-vision matching task. Results suggest that lateral-force-based illusory shapes can be used to render sharp objects, while also providing stable contact during virtual object exploratio

A cost-effective sensor system to train light weight juggling using an interactive virtual reality interface

This paper presents a novel approach to train how to juggle using a sensor system based on one of the most commercial, sophisticated and accessible input devices, the Wii Remote controller. This platform, particularly its infrared camera, is used to develop a real-time sensor system for hand motion tracking and fit it into a virtual reality interface. The quality of our algorithm was tested through this 3D virtual interface, which has the finality to give the user visual feedback of his/her hand positions, and created for the user the sensation of juggling

A Lightweight SLAM algorithm for indoor autonomous navigation

Simultaneous Localization and Mapping (SLAM) algorithms require huge computational power. Most of the state-of-the-art implementations employ dedicated computational machines which in most cases are off-board the robotic platform. In addition, as soon as the environment become large, the update rate of such algorithms is no more suitable for real-time control. The latest implementations rely on visual SLAM, adopting a reduced number of features. However, these methods are not employable in environments with low visibility or that are completely dark. We present here a SLAM algorithm designed for mobile robots requiring reliable solutions even in harsh working conditions where the presence of dust and darkness could compromise the visibility conditions. The algorithm has been optimized for embedded CPUs commonly employed in light-weight robotic platforms. In this paper the proposed algorithm is introduced and its feasibility as SLAM solution for embedded systems is proved both by a simulated and a real testing scenario

Uncontrolled manifold and Juggling: Retrieving a set of Controlled Variables from Data

In this paper we analyze the concept of UnControlled Manifold (UCM), that consists in the kinematic variables that are not controlled by the user, being not relevant to the task. We proceed testing a set of controlled variables inspired by the literature about tracking task, then we propose a procedure to identify them on the basis of captured data. We are interested in the analysis of behavior in a Virtual Environment and in the real world. In particular we analyze the three ball cascade juggling and its simulation through a platform named Light Weight Juggling focusing on the task of ball tossing. Users arm kinematics is represented as a robotic manipulator with 7 degrees of freedom. Joint angles are retrieved through an optical tracking system. The variables controlled in the virtual environment are a subset of the ones controlled in the real world, that leads to an UM that differs from the one in the real world. A comparison between the statistics computed in the two cases is performed to explore behavioral differences in the two cases