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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Benson Christalin Michele Colledanchise Petter Ögren Richard M. Murray Department of Control and Dynamical Systems California Institute of Technology Pasadena CA USA Computer Vision and Active Perception Lab. Royal Institute of Technology (KTH) Stockholm Sweden

This paper presents a method for the reactive synthesis of fault-tolerant optimal control protocols for a finite deterministic discrete event system subject to safety specifications. A Deterministic Finite State Machine (DFSM) and Behavior Tree (BT) were used to model the system. The synthesis procedure involves formulating the policy problem as a shortest path dynamic programming problem. The procedure evaluates all possible states when applied to the DFSM, or over all possible actions when applied to the BT. The resulting strategy minimizes the number of actions performed to meet operational objectives without violating safety conditions. The effectiveness of the procedure on DFSMs and BTs is demonstrated through three examples of switched electrical power systems for commercial application and analyzed using run-time complexity analysis. The results demonstrated that for large order system BTs provided a tractable model to synthesize an optimal control policy.

关键词： Power systems Safety Protocols Computational modeling Aerospace electronics Switches Dynamic programming

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Analytic grasp success prediction with tactile feedback

Analytic grasp success prediction with tactile feedback

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Robert Krug Achim J. Lilienthal Danica Kragic Yasemin Bekiroglu AASS Re-search Center Orebro University Orebro Sweden CSC KTH Stockholm Computer Vision and Active Perception Lab Stockholm Sweden University of Birmingham Birmingham Birmingham GB

ISBN: (纸本)9781467380270

Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.

关键词： Force Tactile sensors Uncertainty Predictive models Computational modeling Training data Friction

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Large-scale supervised learning of the grasp robustness of surface patch pairs

Large-scale supervised learning of the grasp robustness of s...

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IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

作者： Daniel Seita Florian T. Pokorny Jeffrey Mahler Danica Kragic Michael Franklin John Canny Ken Goldberg Department of Industrial Engineering and Operations Research and Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA Computer Vision and Active Perception Lab KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781509046171

The robustness of a parallel-jaw grasp can be estimated by Monte Carlo sampling of perturbations in pose and friction but this is not computationally efficient. As an alternative, we consider fast methods using large-scale supervised learning, where the input is a description of a local surface patch at each of two contact points. We train and test with disjoint subsets of a corpus of 1.66 million grasps where robustness is estimated by Monte Carlo sampling using Dex-Net 1.0. We use the BIDMach machine learning toolkit to compare the performance of two supervised learning methods: Random Forests and Deep Learning. We find that both of these methods learn to estimate grasp robustness fairly reliably in terms of Mean Absolute Error (MAE) and ROC Area Under Curve (AUC) on a held-out test set. Speedups over Monte Carlo sampling are approximately 7500x for Random Forests and 1500x for Deep Learning.

关键词： Robustness Robots Friction Force Machine learning Probabilistic logic Uncertainty

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Robot navigation under uncertainties using event based sampling

Robot navigation under uncertainties using event based sampl...

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IEEE Conference on Decision and Control

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

In many robot applications, sensor feedback is needed to reduce uncertainties in environment models. However, sensor data acquisition also induces costs in terms of the time elapsed to make the observations and the computations needed to find new estimates. In this paper, we show how to use event based sampling to reduce the number of measurements done, thereby saving time, computational resources and power, without jeopardizing critical system properties such as safety and goal convergence. This is done by combining recent advances in nonlinear estimation with event based control using artificial potential fields. The results are particularly useful for real time systems such as high speed vehicles or teleoperated robots, where the cost of taking measurements is even higher, in terms of stops or transmission times. We conclude the paper with a set of simulations to illustrate the effectiveness of the approach and compare it with a baseline approach using periodic measurements.

关键词： Robot sensing systems Collision avoidance Uncertainty Time measurement Bismuth Navigation

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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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On the Evolution of Fingertip Grasping Manifolds

On the Evolution of Fingertip Grasping Manifolds

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IEEE International Conference on Robotics and Automation

作者： K. Hang J. A. Haustein M. Li A. Billard C. Smith D. Kragic the Computer Vision and Active Perception Lab CAS CSC at KTH Royal Institute of Technology Stockholm Sweden the Learning Algorithms and Systems Laboratory (LASA) at école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781467380270

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

关键词： Evolution Fingertip Grasping

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Tracking techniques for visual servoing tasks

Tracking techniques for visual servoing tasks

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IEEE International Conference on Robotics and Automation (ICRA)

作者： 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 Sys Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

Many of today's visual servoing systems rely on the use of markers on the object to provide features for control. There is thus a need for a visual system that provides control features regardless of the appearance of the object. Region based tracking is a natural approach since it does not require any special type of features. In this paper we present two different approaches to region based tracking: 1) a multi-resolution gradient based approach (using optical flow); and 2) a discrete feature based search approach. We present experiments conducted with both techniques for different types of image motions. Finally, the performance, drawbacks and limitations of used techniques are discussed.

关键词： Visual servoing Robots Tracking computer vision Numerical analysis Control systems Grippers Image motion analysis Motion estimation Visual system

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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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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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AN UNCERTAINTY DECODING APPROACH TO NOISE- AND REVERBERATION-ROBUST SPEECH RECOGNITION

AN UNCERTAINTY DECODING APPROACH TO NOISE- AND REVERBERATION...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Roland Maas Akshaya Thippur Armin Sehr Walter Kellermann Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Erlangen Germany Computer Vision and Active Perception Lab School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden Beuth University of Applied Sciences Berlin Department VII Berlin Germany

ISBN: (纸本)9781479903573

The generic REMOS (Reverberation Modeling for robust Speech recognition) concept is extended in this contribution to cope with additional noise components. REMOS originally embeds an explicit reverberation model into a hidden Markov model (HMM) leading to a relaxed conditional independence assumption for the observed feature vectors. During recognition, a nonlinear optimization problem is to be solved in order to adapt the HMMs' output probability density functions to the current reverberation conditions. The extension for additional noise components necessitates a modified numerical solver for the nonlinear optimization problem. We propose an approximation scheme based on continuous piecewise linear regression. Connected-digit recognition experiments demonstrate the potential of REMOS in reverberant and noisy environments. They furthermore reveal that the benefit of an explicit reverberation model, overcoming the conditional independence assumption, increases with increasing signal-to-noise-ratios.

关键词： automatic speech recognition reverberation robustness noise robustness uncertainty decoding piecewise linear regression Speech recognition Hidden Markov models Noise robustness Linear Regression Reverberation DECODING Additive noise Conditional Independence Uncertainty

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