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IEEE International Conference on robotics and Automation (ICRA)

作者： Tom P. Huck Yuvaraj Selvaraj Constantin Cronrath Christoph Ledermann Martin Fabian Bengt Lennartson Torsten Kröger Intelligent Process Automation and Robotics Lab Institute of Anthropomatics and Robotics (IAR-IPR) Karlsruhe Institute of Technology India Department of Electrical Engineering Division of Systems and Control Chalmers University of Technology Gothenburg Sweden

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

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Model Predictive Degree of Automation Regulation for Mobile Robots Using Robot Vitals and Robot Health

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IFAC-PapersOnLine 2023年第2期56卷 8345-8350页

作者： Christian Alexander Braun Aniketh Ramesh Simon Rothfuß Manolis Chiou Rustam Stolkin Sören Hohmann Institute of Control Systems Karlsruhe Institute of Technology 76131 Karlsruhe Germany Extreme Robotics Lab School of Metallurgy and Materials Elm Rd Birmingham B15 2SE

Environmental adversities can severely impact the performance of human-robot teams, potentially even leading to task failure. If the operator and the robot automation are not equally affected, adjusting the degree of automation to shift control authority between them is a means of maintaining the performance of the human-robot team. The robot vitals and robot health framework is a recent approach to quantifying runtime performance degradation in robots. This framework can serve as a methodological foundation for the adjustment of the degree of automation based on the human-robot system's state. In this paper, we contribute two model predictive adaptive automation systems that can adjust either the level or the degree of automation of a robot. These systems optimize robot health to ensure optimal performance of the human-robot team when exposed to adversities. Feasibility studies in simulation showcase the ability of our systems to manage the level and degree of automation, thus allowing for an optimal task execution by the human-robot team.

关键词： Adaptive automation Shared control Cooperation Degree of automation Levels of autonomy Levels of automation Mixed initiative control

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I'll tell you what I want: Categorization of Pareto Fronts for Automated Rule-based Decision-Making

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IFAC-PapersOnLine 2022年第16期55卷 376-381页

作者： Matthias K. Hoffmann Thomas Schmitt Kathrin Flaßkamp Systems Modeling and Simulation Saarland University Saarbrücken Germany Control Methods & Robotics Lab Technical University of Darmstadt Darmstadt Germany

The application of Pareto optimization in control engineering requires decision-making as a downstream step since one solution has to be selected from the set of computed Pareto optimal points. Economic Model Predictive control (MPC) requires repeated optimization and, in multi-objective optimization problems, selection of Pareto optimal points at every time step. Thus, designing an automated selection strategy is favorable. However, it is challenging to come up with a measure – possibly based on a Pareto front analysis – that characterizes preferred Pareto optimal points uniformly across different Pareto fronts. In this work, we first discuss these difficulties for application within MPC and then suggest a solution based on unsupervised machine learning methods. The approach is based on categorizing Pareto fronts as an intermediate step. This allows generating an individual set of rules for every category. Thereby, the human decision-maker's preferences can be modeled more accurately and the selection of a Pareto optimal solution becomes less time-consuming while breaking down the decision-making process into a selection solely based on the Pareto front's shape. Here, the measures act as anchor points for the decision rules. Lastly, a novel knee point measure, i.e. an approximation of the Pareto front's curvature, is presented and used for a knee point-focused categorization. The proposed algorithm is successfully applied to a case study for an energy management system. Moreover, we compare our method to using singular measures for decision-making in order to show its higher flexibility leading to better performance of the controller.

关键词： Multi-objective Optimization Model predictive control Decision-making Energy Management systems Optimal control

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Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region

Enhancing Model-Based Step Adaptation for Push Recovery thro...

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IEEE-RAS International Conference on Humanoid Robots

作者： Tobias Egle Yashuai Yan Dongheui Lee Christian Ott Automation and Control Institute TU Wien Vienna Austria Autonomous Systems Lab TU Wien Vienna Austria Institute of Robotics and Mechatronics (DLR) German Aerospace Center Wessling Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturbances requires bipedal robots to dynamically adjust their stepping strategies, including footstep positions and timing. Unlike most advanced walking controllers that restrict footstep locations to a predefined convex region, substantially limiting recoverable disturbances, our method leverages reinforcement learning to dynamically adjust the permissible footstep region, expanding it to a larger, effectively non-convex area and allowing cross-over stepping, which is crucial for counteracting large lateral pushes. Additionally, our method adapts footstep timing in real time to further extend the range of recoverable disturbances. Based on these adjustments, feasible footstep positions and DCM trajectory are planned by solving a QP. Finally, we employ a DCM controller and an inverse dynamics whole-body control framework to ensure the robot effectively follows the trajectory.

关键词： Legged locomotion Limiting Perturbation methods Humanoid robots Reinforcement learning Robustness Real-time systems Timing Trajectory IEEE Regions

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Equivariant Symmetries for Inertial Navigation systems

arXiv

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arXiv 2023年

作者： Fornasier, Alessandro Ge, Yixiao van Goor, Pieter Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian Centre for Robotic Vision Australian National University Australia

This paper investigates the problem of inertial navigation system (INS) filter design through the lens of symmetry. The extended Kalman filter (EKF) and its variants, have been the staple of INS filtering for 50 years;however, recent advances in inertial navigation systems have exploited matrix Lie group structure to design stochastic filters and state observers that have been shown to display superior performance compared to classical solutions. In this work, we consider the case where a vehicle has an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) receiver. We show that all the modern variants of the EKF for these sensors can be interpreted as the recently proposed Equivariant Filter (EqF) design methodology applied to different choices of symmetry group for the INS problem. This leads us to propose two new symmetries for the INS problem that have not been considered in the prior literature, and provide a discussion of the relative strengths and weaknesses of all the different algorithms. We believe the collection of symmetries that we present here capture all the sensible choices of symmetry for this problem and sensor suite, and that the analysis provided is indicative of the relative real-world performance potential of the different algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Inertial navigation systems

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Unobtrusive Sleep Position Classification Using a Novel Optical Tactile Sensor

Unobtrusive Sleep Position Classification Using a Novel Opti...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alexander Breuss Carmelo Sferrazza Jonas Pleisch Raffaello D’Andrea Robert Riener Sensory-Motor Systems Lab Institute of Robotics and Intelligent Systems ETH Zurich Switzerland Institute for Dynamic Systems and Control ETH Zurich Switzerland Spinal Cord Injury Center University Hospital Balgrist Zurich Switzerland

Unobtrusive sleep position classification is essential for sleep monitoring and closed-loop intervention systems that initiate position changes. In this paper, we present a novel unobtrusive under-mattress optical tactile sensor for sleep position classification. The sensor uses a camera to track particles embedded in a soft silicone layer, inferring the deformation of the silicone and therefore providing information about the pressure and shear distributions applied to its *** characterized the sensitivity of the sensor after placing it under a conventional mattress and applying different weights (258 g, 500 g, 5000 g) on top of the mattress in various predefined locations. Moreover, we collected multiple recordings from a person lying in supine, lateral left, lateral right, and prone positions. As a proof-of-concept, we trained a neural network based on convolutional layers and residual blocks that classified the lying positions based on the images from the tactile *** observed a high sensitivity of the optical tactile sensor: Even after placing the sensor below a conventional mattress, we were able to detect our lowest test weight of 258 g. Using the neural network, we were able to classify the four sleep positions, lateral left, lateral right, prone, and supine with a classification accuracy of 91.2 %.The high sensitivity of the sensor, as well as the good performance in the classification task, demonstrate the feasibility of using such a sensor in a robotic bed *** Relevance— Positional Obstructive Sleep Apnea is highly prevalent across the general population. Today’s gold standard treatment of using CPAP ventilation is often not accepted, leading to unwanted treatment cessations. Alternative treatments, such as positional interventions through robotic beds are highly promising. However, these beds require reliable detection of the lying position. In this paper, we present a novel, scalable and completely unobtrusive sensor that is conce

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Equivariant filter design for inertial navigation systems with input measurement biases

arXiv

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arXiv 2022年

作者： Fornasier, Alessandro Ng, Yonhon Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

Inertial Navigation systems (INS) are a key technology for autonomous vehicles applications. Recent advances in estimation and filter design for the INS problem have exploited geometry and symmetry to overcome limitations of the classical Extended Kalman Filter (EKF) approach that formed the mainstay of INS systems since the mid-twentieth century. The industry standard INS filter, the Multiplicative Extended Kalman Filter (MEKF), uses a geometric construction for attitude estimation coupled with classical Euclidean construction for position, velocity and bias estimation. The recent Invariant Extended Kalman Filter (IEKF) provides a geometric framework for the full navigation states, integrating attitude, position and velocity, but still uses the classical Euclidean construction to model the bias states. In this paper, we use the recently proposed Equivariant Filter (EqF) framework to derive a novel observer for biased inertial-based navigation in a fully geometric framework. The introduction of virtual velocity inputs with associated virtual bias leads to a full equivariant symmetry on the augmented system. The resulting filter performance is evaluated with both simulated and realworld data, and demonstrates increased robustness to a wide range of erroneous initial conditions, and improved accuracy when compared with the industry standard Multiplicative EKF (MEKF) approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Inertial navigation systems

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Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation with Online Calibration

arXiv

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arXiv 2022年

作者： Fornasier, Alessandro Ng, Yonhon Brommer, Christian Böhm, Christoph Mahony, Robert Weiss, Stephan The Control of Networked Systems Group University of Klagenfurt Austria The System Theory and Robotics Lab Australian National University Australia

Stochastic filters for on-line state estimation are a core technology for autonomous systems. The performance of such filters is one of the key limiting factors to a system’s capability. Both asymptotic behavior (e.g., for regular operation) and transient response (e.g., for fast initialization and reset) of such filters are of crucial importance in guaranteeing robust operation of autonomous systems. This paper introduces a new generic formulation for a gyroscope aided attitude estimator using N direction measurements including both body-frame and reference-frame direction type measurements. The approach is based on an integrated state formulation that incorporates navigation, extrinsic calibration for all direction sensors, and gyroscope bias states in a single equivariant geometric structure. This newly proposed symmetry allows modular addition of different direction measurements and their extrinsic calibration while maintaining the ability to include bias states in the same symmetry. The subsequently proposed filter-based estimator using this symmetry noticeably improves the transient response, and the asymptotic bias and extrinsic calibration estimation compared to state-of-the-art approaches. The estimator is verified in statistically representative simulations and is tested in real-world experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

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Test-Stand Development and Performance Analysis of RoboWalk Assistive Device

Test-Stand Development and Performance Analysis of RoboWalk ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： S. Ali A. Moosavian Omid Mahdizadeh Vahid Akbari Mahdi Nabipour Sina Ghanaat Shahin Naeemi Sina Hallajian Arash T. Beydokhti Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab K. N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781665454537

Robotic devices are increasingly studied for the rehabilitation purposes of the lower limb disabilities. Wearable assistive devices such as exoskeletons have been recognized as a practical solution to increase the power and maneuverability of human beings in industries, as well as rehabilitation, elderly care, and sports exercises. RoboWalk is a lower limb assistive device that has been designed in ARAS lab at KNTU. To guarantee its proper performance, a Test-Stand is designed and manufactured with extended toolkit options. To validate the simulation results of various scenarios, is the main goal of developing this Test-Stand. Also, to examine the electronics boards and subsystems, the correct interaction between the sensors and the actuators, and investigate the performance of the main control loop, and so on, the Test-Stand is highly useful. In this paper, these various aspects of design and manufacturing of RoboWalk test stand are presented. Stress analyses in SOLIDWORKS software showed that the design of the proposed device is capable to handle the required tasks. Finally, a positioning task is implemented, which reveals the merits of the designed Test_Stand.

关键词： Performance evaluation Simulation Exoskeletons Software Sensors Manufacturing Assistive devices

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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