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Electrical, Electronic and Computing engineering (IcETRAN), International Conference on

作者： Vladimir Sibinović Mirko Raković Department of Automatic Control Faculty of Electronic Engineering University of Niš Niš Serbia Chair of Mechatronics Robotics and Automation Faculty of Technical Science Novi Sad Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

This paper deals with the magnet position estimation based on the measurements from multiple Hall sensors. Estimation of the position is obtained from trained artificial neural networks. Data are gathered using a custom sensor board that has eight three-axis Hall sensors on it, and correlation between magnetic field and position was determined to be able to estimate position in X and Y directions. First measurements were made to determine the optimal height of the magnet for later data gathering. The outcome of the research was evaluated by comparing the ground truth with the estimated values of the best-trained network to determine the error of estimation. Precise measurement of the position of the magnet is intended to be used for the development of a tactile sensor that estimates the contact force through the displacement of the magnet in the deformable layer.

关键词： Magnetic field measurement Systematics Magnetic sensors Layout Estimation Tactile sensors Artificial neural networks

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Adversarial Attacks Against Uncertainty Quantification

arXiv

引用

arXiv 2023年

作者： Ledda, Emanuele Angioni, Daniele Piras, Giorgio Fumera, Giorgio Biggio, Battista Roli, Fabio Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Electric and Electronic Engineering University of Cagliari Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy

Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantification has been recently proposed to detect adversarial inputs, under the assumption that such attacks exhibit a higher prediction uncertainty than pristine data, it has been shown that adaptive attacks specifically aimed at reducing also the uncertainty estimate can easily bypass this defense mechanism. In this work, we focus on a different adversarial scenario in which the attacker is still interested in manipulating the uncertainty estimate, but regardless of the correctness of the prediction;in particular, the goal is to undermine the use of machine-learning models when their outputs are consumed by a downstream module or by a human operator. Following such direction, we: (i) design a threat model for attacks targeting uncertainty quantification;(ii) devise different attack strategies on conceptually different UQ techniques spanning for both classification and semantic segmentation problems;(iii) conduct a first complete and extensive analysis to compare the differences between some of the most employed UQ approaches under attack. Our extensive experimental analysis shows that our attacks are more effective in manipulating uncertainty quantification measures than attacks aimed to also induce misclassifications. © 2023, CC BY.

关键词： Forecasting

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A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

A Graph-Based Self-Calibration Technique for Cable-Driven Ro...

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

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

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Improving Road Safety With Enhanced Vehicle Detection for Night-time Traffic in Mirpur

Improving Road Safety With Enhanced Vehicle Detection for Ni...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Usama Younis Faisal Riaz Umar Raza Gokhan Atali Hamid Rabnawaz Control Automotive and Robotics Lab Affiliated Lab of National Centre of Robotics and Automation (NCRA) HEC Islamabad Pakistan Department of Engineering Manchester Metropolitan University Manchester United Kingdom Mechatronics Engineering Sakarya University of Applied Sciences Sakarya Turkey Selftech Pvt Ltd

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This paper presents a study on improving night-time vehicle detection in the Mirpur region through a modified neural network model. Given the challenges of low visibility at night, the research focused on optimizing a deep learning algorithm to enhance detection accuracy. A dataset of 10,000 images covering various vehicle types was collected and annotated to train and test the model. The study found a 7% increase in detection performance compared to the standard model, highlighting the potential for tailored algorithms in specific environmental conditions. The findings suggest significant implications for road safety and autonomous driving systems, particularly in regions with similar night-time driving challenges. The research contributes to the broader field of computer vision by showing the effectiveness of model optimization in real-world conditions.

关键词： Accuracy Computational modeling Vehicle detection Neural networks Road safety Reliability robotics and automation Optimization Autonomous vehicles Standards

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Data-Efficient control Barrier Function Refinement

arXiv

引用

arXiv 2023年

作者： Dai, Bolun Huang, Heming Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, it provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a unicycle and a two-link arm. © 2023, CC BY.

关键词： controllers

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NA-CPG: A robust and stable rhythm generator for robot motion control

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Biomimetic Intelligence & robotics 2022年第4期2卷 30-38页

作者： Ru Tong Changlin Qiu Zhengxing Wu Jian Wang Min Tan Junzhi Yu State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Central pattern generators(CPGs)have been widely applied in robot motion control for the spontaneous output of coherent periodic ***,the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces,and the coupling of oscillators affects the network output accuracy in complex topological ***,CPGs may diverge when parameters change drastically,and the divergence is irreversible,which is catastrophic for the control of robot ***,normalized asymmetric CPGs(NA-CPGs)that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are ***-CPGs can realize parameter decoupling,precise amplitude output,and stable and rapid convergence,as well as asymmetric output ***,it can effectively cope with large parameter changes to avoid network oscillations and *** optimize the parameters of the NA-CPG model,a reinforcement-learning-based online optimization method is further ***,a biomimetic robotic fish is illustrated to realize the whole optimization *** demonstrated that the designed NA-CPGs exhibit stable,secure,and accurate network outputs,and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7%and 41.7%,*** proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.

关键词： Normalized asymmetric CPGs Rhythm generator Motion control Robotic fish

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Adversarial Attacks Against Uncertainty Quantification

Adversarial Attacks Against Uncertainty Quantification

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Emanuele Ledda Daniele Angioni Giorgio Piras Giorgio Fumera Battista Biggio Fabio Roli Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electric and Electronic Engineering University of Cagliari Italy

Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantification has been recently proposed to detect adversarial inputs, under the assumption that such attacks exhibit a higher prediction uncertainty than pristine data, it has been shown that adaptive attacks specifically aimed at reducing also the uncertainty estimate can easily bypass this defense mechanism. In this work, we focus on a different adversarial scenario in which the attacker is still interested in manipulating the uncertainty estimate, but regardless of the correctness of the prediction; in particular, the goal is to undermine the use of machine-learning models when their outputs are consumed by a downstream module or by a human operator. Following such direction, we: (i) design a threat model for attacks targeting uncertainty quantification; (ii) devise different attack strategies on conceptually different UQ techniques spanning for both classification and semantic segmentation problems; (iii) conduct a first complete and extensive analysis to compare the differences between some of the most employed UQ approaches under attack. Our extensive experimental analysis shows that our attacks are more effective in manipulating uncertainty quantification measures than attacks aimed to also induce misclassifications.

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Delayed dynamic-feedback controller design for multi-frequency vibration suppression

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IFAC-PapersOnLine 2024年第27期58卷 196-201页

作者： Adrian Saldanha Adam Peichl Wim Michiels Tomas Vyhlidal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

We present a methodology for designing a dynamic controller with delayed output feedback for achieving non-collocated vibration suppression with a focus on the multi-frequency case. To synthesize the delay-based controller, we first remodel the system of equations as a delay-differential algebraic equation (DDAE) in such a way that existing tools for design of a static output feedback controller can be easily adapted. The problem of achieving non-collocated vibration suppression with sufficient damping is formulated as a constrained optimization problem of minimizing the spectral abscissa in the presence of zero-location constraints, with the constraints exhibiting polynomial dependence on its parameters. We transform the problem into an unconstrained one using elimination, following which we solve the resulting non-convex, non-smooth optimization problem.

关键词： vibration suppression time-delay DDAE

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Differentiable Optimization Based Time-Varying control Barrier Functions for Dynamic Obstacle Avoidance

arXiv

引用

arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based methods to systematically construct CBFs for static obstacle avoidance tasks between geometric shapes. In this work, we extend the application of diffOpt CBFs to perform dynamic obstacle avoidance tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can perform obstacle avoidance for dynamic geometric obstacles. Additionally, we show how to extend the TVCBF constraint to consider measurement noise and actuation limits. To demonstrate the efficacy of our proposed approach, we first compare its performance with a model predictive control based method and a circular CBF based method on a simulated dynamic obstacle avoidance task. Then, we demonstrate the performance of our proposed approach in experimental studies using a 7-degree-of-freedom Franka Research 3 robotic manipulator. © 2023, CC BY.

关键词： Model predictive control

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Learning Agile Quadrotor Flight in Restricted Environments with Safety Guarantees

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-8页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Wu, Xinyu Department of Control Science and Engineering Harbin Institute of Technology Shenzhen and Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the increasing requirement for agile and efficient controllers in safety-critical scenarios, controllers that exhibit both agility and safety are attracting attention, especially in the aerial robotics domain. This paper focuses on the safety issue of Reinforcement Learning (RL)-based control for agile quadrotor flight in restricted environments. To this end, we propose a unified Adaptive Safety Predictive Corrector (ASPC) to certify each output action of the RL-based controller in real-time, ensuring its safety while maintaining agility. Specifically, we develop the ASPC as a finite-horizon optimal control problem, formulated by a variant of Model Predictive control (MPC). Given the safety constraints determined by the restricted environment, the objective of minimizing loss of agility can be optimized by reducing the difference between the actions of RL and ASPC. As the safety constraints are decoupled from the RL-based control policy, the ASPC is plug-and-play and can be incorporated into any potentially unsafe controllers. Furthermore, an online adaptive regulator is presented to adjust the safety bounds of the state constraints with respect to the environment changes, extending the proposed ASPC to different restricted environments. Finally, simulations and real-world experiments are demonstrated in various restricted environments to validate the effectiveness of the proposed ASPC. IEEE

关键词： Reinforcement learning

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