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arXiv 2019年

作者： Marić, Filip Limoyo, Oliver Petrović, Luka Ablett, Trevor Petrović, Ivan Kelly, Jonathan University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems Laboratory Canada University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations. Copyright © 2019, The Authors. All rights reserved.

关键词： Continuous time systems

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Fast Manipulability Maximization Using Continuous-Time Trajectory optimization

Fast Manipulability Maximization Using Continuous-Time Traje...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Filip Marić Oliver Limoyo Luka Petrović Trevor Ablett Ivan Petrović Jonathan Kelly Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Canada Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Elecmcal Engineenng and Computing Croatia

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Inverse kinematics for serial kinematic chains via sum of squares optimization

arXiv

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arXiv 2019年

作者： Marić, Filip Giamou, Matthew Khoubyarian, Soroush Petrović, Ivan Kelly, Jonathan University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems Laboratory Canada University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia Vector Institute Postgraduate Affiliate and RBC Fellow

Inverse kinematics is a fundamental problem for articulated robots: fast and accurate algorithms are needed for translating task-related workspace constraints and goals into feasible joint configurations. In general, inverse kinematics for serial kinematic chains is a difficult nonlinear problem, for which closed form solutions cannot be easily obtained. Therefore, computationally efficient numerical methods that can be adapted to a general class of manipulators are of great importance. In this paper, we use convex optimization techniques to solve the inverse kinematics problem with joint limit constraints for highly redundant serial kinematic chains with spherical joints in two and three dimensions. This is accomplished through a novel formulation of inverse kinematics as a nearest point problem, and with a fast sum of squares solver that exploits the sparsity of kinematic constraints for serial manipulators. Our method has the advantages of post-hoc certification of global optimality and a runtime that scales polynomially with the number of degrees of freedom. Additionally, we prove that our convex relaxation leads to a globally optimal solution when certain conditions are met, and demonstrate empirically that these conditions are common and represent many practical instances. Finally, we provide an open source implementation of our algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems

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Robust air data reconstruction: On the use of robust cost functions for flight path reconstruction applications

Robust air data reconstruction: On the use of robust cost fu...

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AIAA Atmospheric Flight Mechanics Conference, 2018

作者： Moszczynski, Gregory Peretroukhin, Valentin Grant, Peter R. Vehicle Simulation Laboratory University of Toronto Institute for Aerospace Studies TorontoON Canada Space and Terrestrial Autonomous Robotics Systems Laboratory University of Toronto Institute for Aerospace Studies TorontoON Canada

ISBN: (数字)9781624105258

ISBN: (纸本)9781624105258

The flight path reconstruction (FPR) problem is formulated as a robust estimation problem to address the practical limitations of modern air data measurement techniques. The FPR problem is first formulated as maximum a posteriori (MAP) estimation problem and the philosophy of robust cost functions (RCFs) is adopted for the development of optimization weight deflation schemes. Using the iteratively reweighted least squares algorithm for solving the formulated robust estimation problem, the popular redescending Geman-McClure (GM), Cauchy, and threshold RCFs are applied to a simulated dataset and compared. Experimental results demonstrate the effectiveness of the GM RCF at mitigating a wide spectrum of deterministic air data measurement errors indicative of those encountered in practice. Further estimation accuracy is demonstrated when trajectory estimates obtained using the GM RCF are used as the initial trajectory estimates for robust estimation using the threshold RCF. Overall, the investigation is successful in establishing the feasibility of the use of RCFs and FPR to mitigate practical air data instrumentation limitations routinely encountered in aircraft system identification and control activities. © 2018 Gregory Moszczynski, Valentin Peretroukhin, and Peter Grant.

关键词： Cost functions

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Manipulability maximization using continuous-time gaussian processes

arXiv

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arXiv 2018年

作者： Marić, Filip Limoyo, Oliver Petrović, Luka Petrović, Ivan Kelly, Jonathan University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems laboratory Canada University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

A significant challenge in motion planning is to avoid being in or near singular configurations (singularities), that is, joint configurations that result in the loss of the ability to move in certain directions in task space. A robotic system’s capacity for motion is reduced even in regions that are in close proximity to (i.e., neighbouring) a singularity. In this work we examine singularity avoidance in a motion planning context, finding trajectories which minimize proximity to singular regions, subject to constraints. We define a manipulability-based likelihood associated with singularity avoidance over a continuous trajectory representation, which we then maximize using a maximum a posteriori (MAP) estimator. Viewing the MAP problem as inference on a factor graph, we use gradient information from interpolated states to maximize the trajectory’s overall manipulability. Both qualitative and quantitative analyses of experimental data show increases in manipulability that result in smooth trajectories with visibly more dexterous arm configurations. Copyright © 2018, The Authors. All rights reserved.

关键词： Trajectories

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LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation

LSTM-Based Zero-Velocity Detection for Robust Inertial Navig...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Brandon Wagstaff Jonathan Kelly Space & Terrestrial Autonomous Robotic Systems (STARS) Laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS) Toronto Canada

We present a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero-velocity detector with a long short-term memory (LSTM) neural network. While existing threshold-based zero-velocity detectors are not robust to varying motion types, our learned model accurately detects stationary periods of the inertial measurement unit (IMU) despite changes in the motion of the user. Upon detection, zero-velocity pseudo-measurements are fused with a dead reckoning motion model in an extended Kalman filter (EKF). We demonstrate that our LSTM-based zero-velocity detector, used within a zero-velocity-aided INS, improves zero-velocity detection during human localization tasks. Consequently, localization accuracy is also improved. Our system is evaluated on more than 7.5 km of indoor pedestrian locomotion data, acquired from five different subjects. We show that 3D positioning error is reduced by over 34% compared to existing fixed-threshold zero-velocity detectors for walking, running, and stair climbing motions. Additionally, we demonstrate how our learned zero-velocity detector operates effectively during crawling and ladder climbing. Our system is calibration-free (no careful threshold-tuning is required) and operates consistently with differing users, IMU placements, and shoe types, while being compatible with any generic zero-velocity-aided INS.

关键词： Detectors Footwear Foot Light rail systems Acceleration Training Dead reckoning

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Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Based Interaction

Self-Calibration of Mobile Manipulator Kinematic and Sensor ...

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IEEE International Conference on robotics and Automation

作者： Oliver Limoyo Trevor Ablett Filip Maric Luke Volpatti Jonathan Kelly Space & Terrestrial Autonomous Robotic Systems (STARS) Laboratory at the university of Toronto Institute for Aerospace Studies (UTIAS) Toronto Canada

We present a novel approach for mobile manip-ulator self-calibration using contact information. Our method, based on point cloud registration, is applied to estimate the extrinsic transform between a fixed vision sensor mounted on a mobile base and an end effector. Beyond sensor calibration, we demonstrate that the method can be extended to include manipulator kinematic model parameters, which involves a non-rigid registration process. Our procedure uses on-board sensing exclusively and does not rely on any external measurement devices, fiducial markers, or calibration rigs. Further, it is fully automatic in the general case. We experimentally validate the proposed method on a custom mobile manipulator platform, and demonstrate centimetre-level post-calibration accuracy in positioning of the end effector using visual guidance only. We also discuss the stability properties of the registration algorithm, in order to determine the conditions under which calibration is possible.

关键词： Robot sensing systems Three-dimensional displays Manipulators Calibration Cameras Kinematics Transforms

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Corrections to “Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction”

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IEEE Access 2020年 8卷 76595-76595页

作者： Lihong Dai Jinguo Liu Zhaojie Ju Yang Gao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China School of Computing University of Portsmouth Portsmouth U.K. Space Technology for Autonomous and Robotic Systems Laboratory (STAR LAB) Surrey Space Centre University of Surrey Guildford U.K.

In the above article [1] , the affiliations of authors Lihong Dai and Yang Gao need to be corrected. Moreover, the biography of Yang Gao needs to be corrected. The specific corrections are as follows.

关键词： Robots Biographies Aerospace electronics Automation Manufacturing Research and development Object recognition

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PROBE-GK: Predictive robust estimation using generalized kernels

arXiv

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arXiv 2017年

作者： Peretroukhin, Valentin Vega-Brown, William Roy, Nicholas Kelly, Jonathan Space & Terrestrial Autonomous Robotic Systems Laboratory Institute for Aerospace Studies University of Toronto Robust Robotics Group Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates. In practice, dynamic environments may degrade sensor performance in predictable ways that cannot be captured with static uncertainty parameters. In this paper, we employ fast nonparametric Bayesian inference techniques to more accurately model sensor uncertainty. By setting a prior on observation uncertainty, we derive a predictive robust estimator, and show how our model can be learned from sample images, both with and without knowledge of the motion used to generate the data. We validate our approach through Monte Carlo simulations, and report significant improvements in localization accuracy relative to a fixed noise model in several settings, including on synthetic data, the KITTI dataset, and our own experimental platform. Copyright © 2017, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Reducing drift in visual odometry by inferring sun direction using a Bayesian Convolutional Neural Network

Reducing drift in visual odometry by inferring sun direction...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Valentin Peretroukhin Lee Clement Jonathan Kelly Space & Terrestrial Autonomous Robotic Systems (STARS) laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS) Canada

We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, where the sun is typically not visible. We leverage recent advances in Bayesian Convolutional Neural Networks to train and implement a sun detection model that infers a three-dimensional sun direction vector from a single RGB image. Crucially, our method also computes a principled uncertainty associated with each prediction, using a Monte Carlo dropout scheme. We incorporate this uncertainty into a sliding window stereo visual odometry pipeline where accurate uncertainty estimates are critical for optimal data fusion. Our Bayesian sun detection model achieves a median error of approximately 12 degrees on the KITTI odometry benchmark training set, and yields improvements of up to 42% in translational ARMSE and 32% in rotational ARMSE compared to standard VO. An open source implementation of our Bayesian CNN sun estimator (Sun-BCNN) using Caffe is available at https://***/utiasSTARS/sun-bcnn-vo.

关键词： Sun Bayes methods Neural networks Visualization Pipelines Training

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