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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Tomáš Vintr Zhi Yan Kerem Eyisoy Filip Kubiš Jan Blaha Jiří Ulrich Chittaranjan S. Swaminathan Sergi Molina Tomasz P. Kucner Martin Magnusson Gregorz Cielniak Jan Faigl Tom Duckett Achim J. Lilienthal Tomáš Krajník Artificial Intelligence Center Czech Technical University Distributed Artificial Intelligence and Knowledge Laboratory (CIAD) University of Technology of Belfort-Montbeliard (UTBM) France Faculty of Engineering Marmara University Turkey AASS Mobile Robotics and Olfaction Lab Orebro University Sweden Lincoln Centre for Autonomous Systems (L-CAS) University of Lincoln UK

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Models of human behaviour, such as pedestrian flows, are beneficial for safe and efficient operation of mobile robots. We present a new methodology for benchmarking of pedestrian flow models based on the afforded safety of robot navigation in human-populated environments. While previous evaluations of pedestrian flow models focused on their predictive capabilities, we assess their ability to support safe path planning and scheduling. Using real-world datasets gathered continuously over several weeks, we benchmark state-of-the-art pedestrian flow models, including both time-averaged and time-sensitive models. In the evaluation, we use the learned models to plan robot trajectories and then observe the number of times when the robot gets too close to humans, using a predefined social distance threshold. The experiments show that while traditional evaluation criteria based on model fidelity differ only marginally, the introduced criteria vary significantly depending on the model used, providing a natural interpretation of the expected safety of the system. For the time-averaged flow models, the number of encounters increases linearly with the percentage operating time of the robot, as might be reasonably expected. By contrast, for the time-sensitive models, the number of encounters grows sublinearly with the percentage operating time, by planning to avoid congested areas and times.

关键词： Navigation Predictive models Benchmark testing Trajectory Planning Safety Robots

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Optimal Feature Selection for EMG-Based Finger Force Estimation Using LightGBM Model

Optimal Feature Selection for EMG-Based Finger Force Estimat...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yuhang Ye Chao Liu Nabil Zemiti Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Robotics LIRMM CNRS-University of Montpellier Montpellier France Bristol Robotics Laboratory University of the West of England Bristol UK

Electromyogram (EMG) signal has been long used in human-robot interface in literature, especially in the area of rehabilitation. Recent rapid development in artificial intelligence (AI) has provided powerful machine learning tools to better explore the rich information embedded in EMG signals. For our specific application task in this work, i.e. estimate human finger force based on EMG signal, a LightGBM (Gradient Boosting Machine) model has been used. The main contribution of this study is the development of an objective and automatic optimal feature selection algorithm that can minimize the number of features used in the LightGBM model in order to simplify implementation complexity, reduce computation burden and maintain comparable estimation performance to the one with full features. The performance of the LightGBM model with selected optimal features is compared with 4 other popular machine learning models based on a dataset including 45 subjects in order to show the effectiveness of the developed feature selection method.

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Stereo Visual SLAM Based on Unscented Dual Quaternion Filtering

Stereo Visual SLAM Based on Unscented Dual Quaternion Filter...

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International Conference on Information Fusion

作者： Simon Bultmann Kailai Li Uwe D. Hanebeck Autonomous Intelligent Systems Group Computer Science Institute VI University of Bonn ISAS KIT Germany Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

We present DQV-SLAM (Dual Quaternion Visual SLAM). This novel feature-based stereo visual SLAM framework uses a stochastic filter based on the unscented transform and a progressive Bayes update, avoiding linearization of the nonlinear spatial transformation group. 6-DoF poses are represented by dual quaternions where rotational and translational components are stochastically modeled by Bingham and Gaussian distributions. Maps represented by point clouds of ORB-features are incrementally built and landmarks are updated with an unscented transform-based method. In order to get reliable measurements during the update, an optical flow-based approach is proposed to remove false feature associations. Drift is corrected by pose graph optimization once loop closure is detected. The KITTI and EuRoC datasets for stereo setup are used for evaluation. The performance of the proposed system is comparable to state-of-the-art optimization-based SLAM systems and better than existing filtering-based approaches.

关键词： Simultaneous localization and mapping Quaternions Feature extraction Visualization Three-dimensional displays Optimization Predictive models

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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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Position Tracking for Continuum Robots with Joint Limit Constraints

Position Tracking for Continuum Robots with Joint Limit Cons...

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International Conference on Unmanned systems and Artificial Intelligence

作者： Dengliang Lin Xin Dong Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Nottingham University Nottingham UK Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

This paper presents a novel tip-following approach for real-time position tracking and avoiding obstacle of continuum robots with joint limit constraints. This type of hyper-redundant robot, which is more flexible and compliant to explore in complex and constrained environments, such as in pipeline or narrow entrance. However, tradictional method based on inverse kinematic model is difficult to control the end-effector of continuum robot to avoid abstcales in the constrained circumstance. Therefore a new tip-following method based on forward kinematic model is proposed to control the robot and make it inspect the environment. For the method, we use nonliner progamming approach to solve the optimization problem, which is used to find minimum of constrained nonlinear objective function to ensure that the end-effector of the robot follow the desired position with minimal error. In this paper, we employ a 24 DoF continuum robot with continuous backbone and compliant joint to the simulation, and it is testified that the proposed approch can ensure joint limit avoidance, track the end postion in real time and minimize the tracking errors.

关键词： hyper-redundant robots obstacle avoidance tip-following control kinematic model kinematics Robot Redundant robot Obstacle avoidance Continuum Location tracking Collision avoidance Joints end effectors

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Dual Arm Cooperation Based on Visual Servo Control

Dual Arm Cooperation Based on Visual Servo Control

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Junbao Gan Chenguang Yang Long Cheng Ying Feng Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Bristol Robotics Laboratory University of the West of England Bristol UK Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

In this paper, a position-based visual servo method is proposed to perform the high-accuracy precision grasping and collaborative tasks for dual arm robot. Firstly, an adaptive color identification procedure is presented to cope with the problem that the color of the object will change when the object is in different positions for object identification. Secondly, we employ the position-based visual servo method to build a control loop including the sensor data streams obtained from the visual system, the force / torque sensors and the joint encoders. Based on these data, the joint velocity value and the on-line motion trajectory of manipulator could be obtained, which led to a good performance on grasping a fixed object and tracking online. Finally, we implement three experiments to verify the effectiveness of the position-based visual servo method in this paper. The experimental results demonstrate the proposed method can effectively identify target objects with a good positioning accuracy and high capture success rate for collaborative tasks.

关键词： Conferences Automation Control systems Intelligent systems

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Binary Channel Fuzzy Self-Adjusted Neural Network for Solving Time-Changing QP Problems

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IEEE Transactions on Neural Networks and Learning systems 2025年 PP卷 PP页

作者： Luo, Yamei Ren, Qingyi Zheng, Zihao Chen, Siyuan Ma, Xin Liu, Yu Li, Xiaoli Yu, Junzhi Zhang, Zhijun The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southern University of Science and Technology Institute for Robotics Shenzhen518055 China Peking University College of Engineering Beijing100871 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Laboratory Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong 723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

A novel binary channel fuzzy self-adjusted neural network (BCF-SANN) is proposed and researched for solving time-changing quadratic programming (QP) problems in this article. Unlike the fixed parameters of the typical zeroing neural network, the main parameters of the proposed BCF-SANN are time-changing, and its errors are adaptively quickly convergent. The biggest advantage of the novel neural network is that it combines a fuzzy self-adjusted controller, which takes the errors and derivatives of errors as fuzzy inputs and neural networks, further improving the convergence and robustness of the neural networks. To design the novel neural network, a time-changing QP problem is first established;then, using Lagrange’s law, the time-changing QP problem is transformed into a time-changing matrix equation;and finally, based on the time-changing parameter neural dynamics method, a novel BCF-SANN is proposed. The detailed design process is given in this article, and the convergence and robustness of the proposed BCF-SANN are proved by theoretical analysis. Through comparative experiments, it is demonstrated that the proposed BCF-SANN has a faster convergence rate and stronger robustness than the traditional zeroing neural network and 1-D fuzzy recurrent neural network (RNN). © 2012 IEEE.

关键词： Recurrent neural networks

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Negotiating with a robot: Analysis of Regulatory Focus Behavior

Negotiating with a robot: Analysis of Regulatory Focus Behav...

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IEEE International Conference on robotics and Automation

作者： Arturo Cruz-Maya Adriana Tapus Autonomous Systems and Robotics Laboratory Université Paris-Saclay Palaiseau cedex France

Companion robots are more and more taking the role of caregivers for elderly people. Elderly people sometimes take the advice given by their family members or caregivers as a criticism. In this context, persuasive communication skills could be helpful. A social psychology theory called Regulatory Focus states that people have one of two inclinations when taking decisions: Promotion or Prevention Focus. Also, based on these inclinations, people can be influenced by the way the message is sent, including the speed of the speech and the amplitude of body gestures. In this paper, we analyze the influence of Regulatory Focus on a negotiation scenario, using 3 conditions: (1) a robot with a promotion behavior, (2) a robot with a prevention behavior, and (3) a robot with a neutral behavior. Our results support the results found in the psychology literature related to Regulatory Focus, suggesting that Promotion participants were more influenced by the robot showing a Promotion based behavior. Moreover, Prevention participants were more relaxed on the condition with the robot showing a Prevention based behavior, and accepted the biggest concession between the initial and final offer.

关键词： Robots Senior citizens Psychology Games Speech recognition Tracking Human-robot interaction

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Robust Self-Scheduled Fault-Tolerant Control of a Quadrotor UAV

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IFAC-PapersOnLine 2017年第1期50卷 5761-5767页

作者： Nguyen D.-T. Saussié D. Saydy L. Mobile Robotics and Autonomous Systems Laboratory Polytechnique Montreal Canada

This paper deals with active fault-tolerant control (FTC) of a quadrotor in the presence of actuator faults. In previous studies, authors generally assume that the fault has been detected, isolated or identified and then proceed with a reconfigurable controller. This paper presents a complete active fault-tolerant control system with both fault detection and diagnosis (FDD) and FTC. The proposed FTC design is based on gain-scheduling control in the framework of structured H∞ synthesis. The scheduled gains are parameterized as polynomial functions of the loss of control effectiveness of the quadrotor's actuators, estimated by a two-stage Kalman filter. Then, the MATLAB-based function systune tune the polynomial coefficients to meet the robustness and performance requirement. In comparison with other FTC systems which use switching mechanisms, this smooth self-scheduled controller allows one to avoid undesirable transient phenomenons during the controller reconfiguration process. Numerical simulations based on an underactuated 6DoF quadrotor nonlinear model show the good effectiveness of the proposed FTC in accommodating different levels of actuator faults. © 2017

关键词： Actuator faults Fault Detection Fault-Tolerant Control Gain scheduling Quadrotor Structured H-Infinity Control Two-Stage Kalman filter

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Design of a Control Platform for Mobile Robot with SSVEP-BCI System

Design of a Control Platform for Mobile Robot with SSVEP-BCI...

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IEEE Conference on Cybernetics and Intelligent systems

作者： Yang Liu Xinyang Li Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Hammersmith Campus Imperial College London National Heart and Lung Institute London Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728134581

ISBN: (纸本)9781728134598

This paper introduces a control platform based on brain-computer interface(BCI) for the mobile robot. The proposed platform consists of three parts: Path Planning, SSVEP-BCI and Mobile Robot Control. The path planning part aims to seek an optimal path out from the starting point to the target point, which is denoted as A* algorithm in this work. The found path is shown to the user who determines the actual movement direction using SSVEP-BCI. The signal is sent to the mobile robot to control the movement of the robot. In the BCI part, the collected EEG signals are analyzed with the CCA method to obtain the homologous control signals. The mobile robot is required to complete the movement from the initial point to the destination without collision. The platform is evaluated with an off-line experiment with 4 subjects and the off-line analysis shows the feasibility of the proposed platform.

关键词： Mobile robots Visualization Electroencephalography Path planning Heuristic algorithms Frequency modulation

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