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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： F. E. Vina B Y. Karayiannidis K. Pauwels C. Smith D. Kragic Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH SE-100 44 Stockholm Sweden Dept. of Signals and Systems Chalmers University of Technology Gothenburg Sweden The Computer Vision and Active Perception Lab. Royal Institute of Technology Stockholm Sweden

In this work we propose a sliding mode controller for in-hand manipulation that repositions a tool in the robot's hand by using gravity and controlling the slippage of the tool. In our approach, the robot holds the tool with a pinch grasp and we model the system as a link attached to the gripper via a passive revolute joint with friction, i.e., the grasp only affords rotational motions of the tool around a given axis of rotation. The robot controls the slippage by varying the opening between the fingers in order to allow the tool to move to the desired angular position following a reference trajectory. We show experimentally how the proposed controller achieves convergence to the desired tool orientation under variations of the tool's inertial parameters.

关键词： Friction Robot kinematics Grippers Torque Gravity

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Online Kinematics Estimation for active Human-Robot Manipulation of Jointly Held Objects

Online Kinematics Estimation for Active Human-Robot Manipula...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Yiannis Karayiannidis Christian Smith Francisco E. Vina Danica Kragic Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology (KTH) SE-100 44 Stockholm Sweden.

ISBN: (纸本)9781467363563

This paper introduces a method for estimating the constraints imposed by a human agent on a jointly manipulated object. These estimates can be used to infer knowledge of where the human is grasping an object, enabling the robot to plan trajectories for manipulating the object while subject to the constraints. We describe the method in detail, motivate its validity theoretically, and demonstrate its use in co-manipulation tasks with a real robot.

关键词： constraints object while estimating Constraint Robot kinematics trajectory Does manipulate objects AT-Rich Interactive Domain-Containing Protein 1A

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Fast shape-based road sign detection for a driver assistance system

Fast shape-based road sign detection for a driver assistance...

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IEEE International Workshop on Intelligent Robots and systems (IROS)

作者： G. Loy N. Barnes Computer Vision and Active Perception Laboratory Royal Institute of Technology (KTH) Stockholm Sweden Autonomous Systems and Sensor Technologies Program National ICT Australia Canberra Australia

A new method is presented for detecting triangular, square and octagonal road signs efficiently and robustly. The method uses the symmetric nature of these shapes, together with the pattern of edge orientations exhibited by equiangular polygons with a known number of sides, to establish possible shape centroid locations in the image. This approach is invariant to in-plane rotation and returns the location and size of the shape detected. Results on still images show a detection rate of over 95%. The method is efficient enough for real-time applications, such as on-board-vehicle sign detection.

关键词： Shape Road safety Detectors Image segmentation Robustness Image edge detection Australia Pixel Voting computer vision

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Obstacle avoidance in formation using navigation-like functions and constraint based programming

Obstacle avoidance in formation using navigation-like functi...

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2013 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Michele Colledanchise Dimos V. Dimarogonas Petter Ögren Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication The Royal Institute of Technology - KTH Stockholm Sweden Centre for Autonomous Systems Automatic Control Lab School of Electrical Engineering The Royal Institute of Technology - KTH Stockholm Sweden

In this paper, we combine navigation function-like potential fields and constraint based programming to achieve obstacle avoidance in formation. Constraint based programming was developed in robotic manipulation as a technique to take several constraints into account when controlling redundant manipulators. The approach has also been generalized, and applied to other control systems such as dual arm manipulators and unmanned aerial vehicles. Navigation functions are an elegant way to design controllers with provable properties for navigation problems. By combining these tools, we take advantage of the redundancy inherent in a multi-agent control problem and are able to concurrently address features such as formation maintenance and goal convergence, even in the presence of moving obstacles. We show how the user can decide a priority ordering of the objectives, as well as a clear way of seeing what objectives are currently addressed and what are postponed. We also analyze the theoretical properties of the proposed controller. Finally, we use a set of simulations to illustrate the approach.

关键词： Navigation Convergence Programming Collision avoidance Robots Maintenance engineering

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Predicting human intention in visual observations of hand/object interactions

Predicting human intention in visual observations of hand/ob...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dan Song Nikolaos Kyriazis Iason Oikonomidis Chavdar Papazov Antonis Argyros Darius Burschka Danica Kragic Computer Vision & Active Perception Laboratory Centre for Autonomous Systems Royal Institute of Technology Stockholm Sweden Institute of Computer Science Foundation for Research and Technology-Hellas Crete Greece Technische Universität München Munich Germany

ISBN: (纸本)9781467356404

The main contribution of this paper is a probabilistic method for predicting human manipulation intention from image sequences of human-object interaction. Predicting intention amounts to inferring the imminent manipulation task when human hand is observed to have stably grasped the object. Inference is performed by means of a probabilistic graphical model that encodes object grasping tasks over the 3D state of the observed scene. The 3D state is extracted from RGB-D image sequences by a novel vision-based, markerless hand-object 3D tracking framework. To deal with the high-dimensional state-space and mixed data types (discrete and continuous) involved in grasping tasks, we introduce a generative vector quantization method using mixture models and self-organizing maps. This yields a compact model for encoding of grasping actions, able of handling uncertain and partial sensory data. Experimentation showed that the model trained on simulated data can provide a potent basis for accurate goal-inference with partial and noisy observations of actual real-world demonstrations. We also show a grasp selection process, guided by the inferred human intention, to illustrate the use of the system for goal-directed grasp imitation.

关键词： Grasping Three-dimensional displays Data models Robot sensing systems Bayes methods Computational modeling

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Task-based Grasp Adaptation on a Humanoid Robot

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IFAC Proceedings Volumes 2012年第22期45卷 779-786页

作者： Jeannette Bohg Kai Welke Beatriz León Martin Do Dan Song Walter Wohlkinger Marianna Madry Aitor Aldóma Markus Przybylski Tamim Asfour Higinio Martí Danica Kragic Antonio Morales Markus Vincze Humanoids and Intelligence Systems Lab Institute for Anthropomatics KIT DE Autonomous Motion Lab MPI for Intelligent Systems DE Robotic Intelligence Laboratory Department of Computer Science and Engineering UJI ES Computer Vision and Active Perception lab KTH SE Automation and Control Institute TUW AT

In this paper, we present an approach towards autonomous grasping of objects according to their category and a given task. Recent advances in the field of object segmentation and categorization as well as task-based grasp inference have been leveraged by integrating them into one pipeline. This allows us to transfer task-specific grasp experience between objects of the same category. The effectiveness of the approach is demonstrated on the humanoid robot ARMAR-IIIa.

关键词： Robotic Grasping and Manipulation Task-based Grasp Synthesis Visual Servoing Attention Segmentation Object categorization System Integration

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Model-aided State Estimation for Quadrotor Micro Air Vehicles amidst Wind Disturbances

Model-aided State Estimation for Quadrotor Micro Air Vehicle...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Dinuka Abeywardena Zhan Wang Gamini Dissanayake Steven L. Waslander Sarath Kodagoda Centre for Autonomous Systems University of Technology Sydney Computer Vision and Active Perception Lab Centre for Autonomous Systems CSC The Royal Institute of Technology KTH Stockholm 100 44 Sweden Waterloo Autonomous Vehicles Laboratory University of Waterloo Waterloo ON Canada N2L 3G1

ISBN: (纸本)9781479969357

This paper extends the recently developed Model-Aided Visual-Inertial Fusion (MA-VIF) technique for quadrotor Micro Air Vehicles (MAV) to deal with wind disturbances. The wind effects are explicitly modelled in the quadrotor dynamic equations excluding the unobservable wind velocity component. This is achieved by a nonlinear observability of the dynamic system with wind effects. We show that using the developed model, the vehicle pose and two components of the wind velocity vector can be simultaneously estimated with a monocular camera and an inertial measurement unit. We also show that the MA-VIF is reasonably tolerant to wind disturbances, even without explicit modelling of wind effects and explain the reasons for this behaviour. Experimental results using a Vicon motion capture system are presented to demonstrate the effectiveness of the proposed method and validate our claims.

关键词： Mathematical model Wind speed Equations Observability Accelerometers Vectors Vehicle dynamics

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Caging Complex Objects with Geodesic Balls

Caging Complex Objects with Geodesic Balls

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Dmitry Zarubin Florian T. Pokorny Marc Toussaint Danica Kragic Machine Learning and Robotics Lab Universitat Stuttgart Stuttgart Germany Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

This paper proposes a novel approach for the synthesis of grasps of objects whose geometry can be observed only in the presence of noise. We focus in particular on the problem of generating caging grasps with a realistic robot hand simulation and show that our method can generate such grasps even on complex objects. We introduce the idea of using geodesic balls on the object's surface in order to approximate the maximal contact surface between a robotic hand and an object. We define two types of heuristics which extract information from approximate geodesic balls in order to identify areas on an object that can likely be used to generate a caging grasp. Our heuristics are based on two scoring functions. The first uses winding angles measuring how much a geodesic ball on the surface winds around a dominant axis, while the second explores using the total discrete Gaussian curvature of a geodesic ball to rank potential caging postures. We evaluate our approach with respect to variations in hand kinematics, for a selection of complex real-world objects and with respect to its robustness to noise.

关键词： Caging Complex Objects Geodesic Balls Gaussian curvature

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RCAMP: A resilient communication-aware motion planner for mobile robots with autonomous repair of wireless connectivity

arXiv

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arXiv 2017年

作者： Caccamo, Sergio Parasuraman, Ramviyas Freda, Luigi Gianni, Mario Ögren, Petter Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Sweden Purdue University West Lafayette United States ALCOR Laboratory DIAG Sapienza University of Rome Italy

Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network. In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios. Copyright © 2017, The Authors. All rights reserved.

关键词： Mobile robots

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A person following behaviour for a mobile robot

A person following behaviour for a mobile robot

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IEEE International Conference on Robotics and Automation (ICRA)

作者： H. Sidenbladh D. Kragic H.I. Christensen Computer Vision and Active Perception Lab Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden Centre for Autonomous Systems Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

In this paper, a person following behaviour for a mobile robot is presented. The head of the person is located using skin colour detection. Then, a control loop is fed with the camera movements required to put the upper part of the person in the center of the image. The algorithm was tested in different rooms of a research lab. It performed well in all lighting conditions except in direct sunlight. Since the background and lighting cannot be controlled, the vision algorithm must be robust to such changes. However, since the computing power is quite limited, the algorithm must have as low complexity as possible.

关键词： Mobile robots Robot vision systems Cameras computer vision Skin Image motion analysis Numerical analysis Lighting control Humans Machine vision

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