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Östermalm, Sweden

Karayiannidis Y.,Center for Autonomous Systems | Doulgeri Z.,Aristotle University of Thessaloniki
Robotica | Year: 2013

Fast and robust tracking against unknown disturbances is required in many modern complex robotic structures and applications, for which knowledge of the full exact nonlinear system is unreasonable to assume. This paper proposes a regressor-free nonlinear controller of low complexity which ensures prescribed performance position error tracking subject to unknown endogenous and exogenous bounded dynamics assuming that joint position and velocity measurements are available. It is theoretically shown and demonstrated by a simulation study that the proposed controller can guarantee tracking of the desired joint position trajectory with a priori determined accuracy, overshoot and speed of response. Preliminary experimental results to a simplified system are promising for validating the controller to more complex structures. Copyright © Cambridge University Press 2013. Source

Bohg J.,Center for Autonomous Systems | Kragic D.,Center for Autonomous Systems
Robotics and Autonomous Systems | Year: 2010

This paper presents work on vision based robotic grasping. The proposed method adopts a learning framework where prototypical grasping points are learnt from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labelled synthetic images. We evaluate and compare the performance of linear and non-linear classifiers. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects. © 2009 Elsevier B.V. All rights reserved. Source

Romero J.,Center for Autonomous Systems | Feix T.,Otto Bock Healthcare Gmeinbh | Kjellstrom H.,Center for Autonomous Systems | Kragic D.,Center for Autonomous Systems
IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings | Year: 2010

Understanding the spatial dimensionality and temporal context of human hand actions can provide representations for programming grasping actions in robots and inspire design of new robotic and prosthetic hands. The natural representation of human hand motion has high dimensionality. For specific activities such as handling and grasping of objects, the commonly observed hand motions lie on a lower-dimensional non-linear manifold in hand posture space. Although full body human motion is well studied within Computer Vision and Biomechanics, there is very little work on the analysis of hand motion with nonlinear dimensionality reduction techniques. In this paper we use Gaussian Process Latent Variable Models (GPLVMs) to model the lower dimensional manifold of human hand motions during object grasping. We show how the technique can be used to embed high-dimensional grasping actions in a lower-dimensional space suitable for modeling, recognition and mapping. ©2010 IEEE. Source

Rubio O.J.,Center for Autonomous Systems | Huebner K.,Center for Autonomous Systems | Kragic D.,Center for Autonomous Systems
IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings | Year: 2010

We study two important problems in the area of robot grasping: i) the methodology and representations for grasp selection on known and unknown objects, and ii) learning from experience for grasping of similar objects. The core part of the paper is the study of different representations necessary for implementing grasping tasks on objects of different complexity. We show how to select a grasp satisfying force-closure, taking into account the parameters of the robot hand and collision-free paths. Our implementation takes also into account efficient computation at different levels of the system regarding representation, description and grasp hypotheses generation. ©2010 IEEE. Source

Colledanchise M.,Center for Autonomous Systems | Marzinotto A.,Center for Autonomous Systems | Ogren P.,Center for Autonomous Systems
Proceedings - IEEE International Conference on Robotics and Automation | Year: 2014

This paper presents a mathematical framework for performance analysis of Behavior Trees (BTs). BTs are a recent alternative to Finite State Machines (FSMs), for doing modular task switching in robot control architectures. By encoding the switching logic in a tree structure, instead of distributing it in the states of a FSM, modularity and reusability are improved. In this paper, we compute performance measures, such as success/failure probabilities and execution times, for plans encoded and executed by BTs. To do this, we first introduce Stochastic Behavior Trees (SBT), where we assume that the probabilistic performance measures of the basic action controllers are given. We then show how Discrete Time Markov Chains (DTMC) can be used to aggregate these measures from one level of the tree to the next. The recursive structure of the tree then enables us to step by step propagate such estimates from the leaves (basic action controllers) to the root (complete task execution). Finally, we verify our analytical results using massive Monte Carlo simulations, and provide an illustrative example of the results for a complex robotic task. © 2014 IEEE. Source

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