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5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016

作者： Eriksson, André Kjellström, Hedvig Computer Vision and Active Perception Lab. KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9789897581731

While many advances towards effective anomaly detection techniques targeting specific applications have been made in recent years, little work has been done to develop application-agnostic approaches to the subject. In this article, we present such an approach, in which anomaly detection methods are treated as formal, structured objects. We consider a general class of methods, with an emphasis on methods that utilize structural properties of the data they operate on. For this class of methods, we develop a decomposition into sub-methods-simple, restricted objects, which may be reasoned about independently and combined to form methods. As we show, this formalism enables the construction of software that facilitates formulating, implementing, evaluating, as well as algorithmically finding and calibrating anomaly detection methods. © Copyright 2016 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

关键词： Formal methods

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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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How behavior trees generalize the Teleo-Reactive paradigm and and-or-trees

How behavior trees generalize the Teleo-Reactive paradigm an...

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016

作者： Colledanchise, Michele Ögren, Petter Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication KTH - Royal Institute of Technology StockholmSE-100 44 Sweden

ISBN: (纸本)9781509037629

Behavior Trees (BTs) is a way of organizing the switching structure of a control system, that was originally developed in the computer gaming industry but is now also being used in robotics. The Teleo-Reactive programs (TRs) is a highly cited reactive hierarchical robot control approach suggested by Nilsson and And-Or-Trees are trees used for heuristic problems solving. In this paper, we show that BTs generalize TRs as well as And-Or-Trees, even though the two concepts are quite different. And-Or-Trees are trees of conditions, and we show that they transform into a feedback execution plan when written as a BT. TRs are hierarchical control structures, and we show how every TR can be written as a BT. Furthermore, we show that so-called Universal TRs, guaranteeing that the goal will be reached, are a special case of so-called Finite Time Successful BTs. This implies that many designs and theoretical results developed for TRs can be applied to BTs. © 2016 IEEE.

关键词： Forestry

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Adaptive object centered teleoperation control of a mobile manipulator

Adaptive object centered teleoperation control of a mobile m...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Fredrik Båberg Yuquan Wang Sergio Caccamo Petter Ögren Computer Vision and Active Perception Lab Royal Institute of Technology (KTH)

Teleoperation of a mobile robot manipulating and exploring an object shares many similarities with the manipulation of virtual objects in a 3D design software such as AutoCAD. The user interfaces are however quite different, mainly for historical reasons. In this paper we aim to change that, and draw inspiration from the 3D design community to propose a teleoperation interface control mode that is identical to the ones being used to locally navigate the virtual viewpoint of most computer Aided Design (CAD) softwares. The proposed mobile manipulator control framework thus allows the user to focus on the 3D objects being manipulated, using control modes such as orbit object and pan object, supported by data from the wrist mounted RGB-D sensor. The gripper of the robot performs the desired motions relative to the object, while the manipulator arm and base moves in a way that realizes the desired gripper motions. The system redundancies are exploited in order to take additional constraints, such as obstacle avoidance, into account, using a constraint based programming framework.

关键词： Robot sensing systems Orbits End effectors Mobile communication Three-dimensional displays

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active exploration using Gaussian Random Fields and Gaussian Process Implicit Surfaces

Active exploration using Gaussian Random Fields and Gaussian...

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sergio Caccamo Yasemin Bekiroglu Carl Henrik Ek Danica Kragic Computer Vision and Active Perception Lab. Royal Institute of Technology (KTH) Stockholm SE

ISBN: (纸本)9781509037636

In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an online probabilistic framework that merges visual and tactile measurements using Gaussian Random Field and Gaussian Process Implicit Surfaces. The system investigates incomplete point clouds in order to find a small set of regions of interest which are then physically explored with a robotic arm equipped with tactile sensors. We show experimental results obtained using a PrimeSense camera, a Kinova Jaco2 robotic arm and Optoforce sensors on different scenarios. We then demostrate how to use the online framework for object detection and terrain classification.

关键词： Surface treatment Gaussian processes Three-dimensional displays Robot sensing systems Shape Surface reconstruction

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active perception and modeling of deformable surfaces using Gaussian processes and position-based dynamics

Active perception and modeling of deformable surfaces using ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sergio Caccamo Püren Güler Hedvig Kjellström Danica Kragic Computer Vision and Active Perception Lab. Royal Institute of Technology (KTH) Stockholm Sweden

ISBN: (纸本)9781509047192

Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters. The most common approaches model deformable properties from sets of offline observations using computationally expensive force-based simulators. In this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions. The method takes advantage of Gaussian Processes for constructing a model of the environment geometry surrounding a robot. A fast Position-based Dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment. Gaussian Process Regression maps the local deformability on the whole environment in order to generate a deformability distribution map. We show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm and an Optoforce sensor on different deformable surfaces.

关键词： Shape Deformable models Gaussian processes Robot sensing systems Ground penetrating radar Surface treatment Computational modeling

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How Behavior Trees generalize the Teleo-Reactive paradigm and And-Or-Trees

How Behavior Trees generalize the Teleo-Reactive paradigm an...

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Michele Colledanchise Petter Ögren Computer Vision and Active Perception Lab. KTH - Royal Institute of Technology Stockhalm Sweden

关键词： Service robots Batteries Control systems Industries Problem-solving computers

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A sensorimotor reinforcement learning framework for physical Human-Robot Interaction

A sensorimotor reinforcement learning framework for physical...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Ali Ghadirzadeh Judith Butepage Atsuto Maki Danica Kragic Marten Bjorkman Computer Vision and Active Perception Lab (CVAP) CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781509037636

Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-efficient reinforcement learning framework which enables a robot to learn how to collaborate with a human partner. The robot learns the task from its own sensorimotor experiences in an unsupervised manner. The uncertainty in the interaction is modeled using Gaussian processes (GP) to implement a forward model and an action-value function. Optimal action selection given the uncertain GP model is ensured by Bayesian optimization. We apply the framework to a scenario in which a human and a PR2 robot jointly control the ball position on a plank based on vision and force/torque data. Our experimental results show the suitability of the proposed method in terms of fast and data-efficient model learning, optimal action selection under uncertainty and equal role sharing between the partners.

关键词： Robot sensing systems Predictive models Trajectory Impedance Learning (artificial intelligence) Uncertainty

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Adaptive control for pivoting with visual and tactile feedback

Adaptive control for pivoting with visual and tactile feedba...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Francisco E. Viña B. Yiannis Karayiannidis Christian Smith Danica Kragic Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH Stockholm Sweden Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Chalmers University of Technology Sweden Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH Sweden

In this work we present an adaptive control approach for pivoting, which is an in-hand manipulation maneuver that consists of rotating a grasped object to a desired orientation relative to the robot's hand. We perform pivoting by means of gravity, allowing the object to rotate between the fingers of a one degree of freedom gripper and controlling the gripping force to ensure that the object follows a reference trajectory and arrives at the desired angular position. We use a visual pose estimation system to track the pose of the object and force measurements from tactile sensors to control the gripping force. The adaptive controller employs an update law that accommodates for errors in the friction coefficient, which is one of the most common sources of uncertainty in manipulation. Our experiments confirm that the proposed adaptive controller successfully pivots a grasped object in the presence of uncertainty in the object's friction parameters.

关键词： Friction Force Grippers Robot kinematics Planning Robot sensing systems

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Adaptive control for pivoting with visual and tactile feedback

Adaptive control for pivoting with visual and tactile feedba...

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2016 IEEE International Conference on Robotics and Automation, ICRA 2016

作者： Vina, Francisco E.B. Karayiannidis, Yiannis Smith, Christian Kragic, Danica Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH StockholmSE-100 44 Sweden Dept. of Signals and Systems Chalmers University of Technology GothenburgSE-412 96 Sweden

ISBN: (纸本)9781467380263

关键词： Friction

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