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Robust Control of Metric Discrete Event systems Against Bounded Disturbances

IFAC-PapersOnLine 2024年第1期58卷 108-113页

作者： Yiding Ji Xiang Yin Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Automation Shanghai Jiao Tong University Shanghai China

This work investigates robust supervisory control problems of discrete event systems modeled as finite state automata equipped with metric functions to measure the distance between states. The system may deviate from its nominal behaviors and fail to achieve the specification under disturbances whose effects are considered to be bounded. Accordingly, the supervisor should be designed to ensure that the controlled system degrades gracefully against such adversary. We formally formulate two problems: robustness bound verification and optimal robust supervisor synthesis. For the special case of verification under constant disturbances, a control Lyapunov function approach is introduced. Then we develop a two player game framework for the general verification and synthesis problems. Specifically, a dynamic programming method is proposed on the game structure, which uniformly tackles both problems.

关键词： Metric discrete event systems supervisory control robustness two player games

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Vivim: a Video Vision Mamba for Medical Video Segmentation

arXiv

引用

arXiv 2024年

作者： Yang, Yijun Xing, Zhaohu Yu, Lequan Huang, Chunwang Fu, Huazhu Zhu, Lei Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China The Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Hong Kong Guangdong Provincial People’s Hospital Guangzhou China The Institute of High Performance Computing A*STAR Singapore Robotics and Autonomous Systems Thrust Hong Kong University of Science and Technology Guangzhou China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

Medical video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are mediocre in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited impressive achievements in efficient long sequence modeling, which develops deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for medical video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. The code is available at: https://***/scottyjyang/Vivim. The dataset will be released once accepted. © 2024, CC BY.

关键词： Deep neural networks

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Enhance Planning with Physics-informed Safety Controller for End-to-end autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

引用

IEEE International Conference on robotics and Biomimetics

作者： Hang Zhou Haichao Liu Hongliang Lu Jun Ma Yiding Ji Robotics and Autonomous Systems Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering School of Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Intelligent Transportation Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark.

关键词： Training Trajectory planning Artificial neural networks Benchmark testing System recovery Safety Planning autonomous vehicles Robots Predictive control

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Real-Time Teleoperation in Motion Mapping for Medical Robot Based on Robot Manipulator and Haptic Device

Real-Time Teleoperation in Motion Mapping for Medical Robot ...

引用

IEEE International Conference on robotics and Biomimetics

作者： Pantita Peuchpen Haichao Liu Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Access to specialized medical care, particularly in rural or underserved areas, remains a significant challenge, with limited availability of healthcare professionals and diagnostic equipment. Teleoperated robotic systems offer a solution by enabling remote procedures, such as ultrasound examinations, where doctors can operate robotic arms from distant locations. In response to these healthcare challenges, we propose a real-time teleoperation system for controlling a UR5 robot arm using a Geomagic Touch haptic device. This system maps the operator's hand movements to the robot arm, ensuring precise control and providing real-time haptic feedback, which is critical for delicate medical tasks. A force-torque sensor at the robot's end-effector measures the force applied during operation, relaying this information back to the haptic device to simulate tactile sensations. Synchronization between the devices at a frequency of 500 Hz ensures smooth and responsive control. Additionally, a Kalman filter is applied to reduce noise in the force data, enhancing system accuracy. Experimental results demonstrate the system's effectiveness with minimal delay and robust force feedback. This work represents a step forward in bridging the gap between remote healthcare providers and patients, offering a scalable solution for teleoperated medical procedures. All the codes and implementation details for this work can be found in the following repository: https://***/primpunn/***.

关键词： Ultrasonic imaging Accuracy Force feedback Medical services Robot sensing systems Real-time systems End effectors Collision avoidance Robots Medical diagnostic imaging

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State Estimation for Wheeled-Legged Robot with Slip Detection

State Estimation for Wheeled-Legged Robot with Slip Detectio...

引用

IEEE International Conference on robotics and Biomimetics

作者： Jinglan Xu Zhicheng Song Chunxin Zheng Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The wheeled-legged robot offers enhanced efficiency in exploring tough terrain compared to legged robots. However, difficulties arise when this hybrid locomotion robot operates on slippery terrain, potentially leading to compromised state estimation and control due to the lack of mechanisms to address such conditions. In response to this issue, we propose a framework incorporating an estimated slip ratio into a Kalman Filter (KF) for state estimation to obtain the robot's velocity on slippery terrain. The framework relies solely on data from the Inertial Measurement Units (IMU) and motor sensors mounted on the robot. Through a series of simulations and real-world experiments, the proposed framework is validated on a single-wheeled-legged robot running on slippery terrain, and the results demonstrate its effectiveness in accurately estimating the robot's velocity under slippery conditions.

关键词： Legged locomotion Measurement units Biomimetics Biological system modeling Inertial navigation Robot sensing systems Motors Sensors Kalman filters State estimation

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CH-LSTTM: A Taxonomy of Traffic Hazards

CH-LSTTM: A Taxonomy of Traffic Hazards

引用

IEEE International Conference on Human-Machine systems

作者： Song Yan Chunxi Huang Dengbo He Thrust of Robotics and Autonomous Systems Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Robotics and Autonomous Systems Division of Emerging Interdisciplinary Areas (EMIA) under Interdisciplinary Programs Office (IPO) The Hong Kong University of Science and Technology Hong Kong SAR China Thrust of Intelligent Transportation The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

During driving, drivers must interact with various objects in the highly dynamic driving environment, in which they need to perceive the traffic hazards and handle safety-critical scenarios. Previous studies have proved the importance of drivers’ hazard perception skills and designed corresponding driver training programs. However, due to the lack of a suitable taxonomy for hazards, most driver training programs on hazard perception are simply a collection of various hazardous scenarios but do not guide drivers’ hazard perception skills in a systematic way. The existing taxonomies of hazards were mainly based on the characteristics of hazards but did not take the hazard perception procedures into consideration, which can hardly be applied to improve drivers’ hazard perception skills. Therefore, in this study, we proposed a taxonomy of traffic hazards based on an analysis of hazard perception procedures. Overall, a four-element taxonomy of traffic hazards (i.e., CH-LSTTM) was proposed, in which the Cues (single-element-based, multiple-element-based, and no-element-based), Hazards (visible vs. invisible), Link Strength (LS, which accounts for the uncertainty between the cues and the hazards during the hazard development process), and Time to Materialization (TTM, which indicates the imminence level of the hazard) were considered. We demonstrated the capability of the proposed taxonomy in depicting and categorizing realworld traffic hazards through a naturalistic driving case study. The proposed taxonomy can support the optimization of driver training programs to improve their hazard perception skills.

关键词： Training Systematics Uncertainty Human-machine systems Taxonomy Hazards Optimization

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Multi-Objective Optimization of Electromagneticthermal Coupling Performance for High-torquedensity PMSM based on Multi-dimensional Correction Techniques

Multi-Objective Optimization of Electromagneticthermal Coupl...

引用

International Conference on Electrical Machines, ICEM

作者： Zequan Li Jin Wang Lu Sun Haoyu Kang Liyang Liu Libing Zhou State Key Laboratory of Advanced Electromagnetic Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China State Key Laboratory of Advanced Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Robotics and Autonomous Systems Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

To address the precision limitation of traditional thermal models and the computational inefficiency of electromagnetic-thermal coupling finite element models, this work proposes a multidimensional correction-based electromagnetic-thermal coupling design optimization method for electric machines. Firstly, this method corrects the sensitive parameters in the analytical lumped parameters thermal network model (LPNM) based on prior information from historical experimental data to ensure the accuracy for thermal performance analysis. Subsequently, to enhance computational efficiency, a large number of low-fidelity (LF) samples are obtained using the corrected thermal model in a serial electromagnetic-thermal field calculation manner, complemented by a small number of high-fidelity (HF) samples obtained from the electromagnetic-thermal coupling simulation model. Then, by employing a transfer learning technique, the data information from the low- to high-fidelity sample sets is comprehensively utilized to train a multi-fidelity (MF) surrogate model for the final optimization. The proposed method applied to a high-torque-density aerospace propulsion permanent magnet synchronous motor (PMSM) with short-term strong overload capability, it not only significantly improves the accuracy of LPNM, but also reduces the training sample acquisition time by over 50% under the same model accuracy. Finally, a prototype is tested to validate the effectiveness of the proposed method.

关键词： Couplings Accuracy Computational modeling Transfer learning Data models Thermal analysis Computational efficiency Iterative methods Optimization Design optimization

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Heuristic Online Path Planning for Mobile Air Purification and Disinfection Robot In Regulating Indoor Air Quality

Heuristic Online Path Planning for Mobile Air Purification a...

引用

2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Zhong, Qingnan Chen, Kai Duan, Jianghua Ma, Jun Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Robotics and Human Machine Interface Laboratory Futian China The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China

ISBN: (纸本)9798350325706

In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting surfaces and purifying the air. This paper focuses on a mobile robot equipped with air purification and UV-C LED disinfection to combat airborne pathogens and environmental pollution. Through the onboard sensors, the proposed algorithm uses gaussian process model to predict the high potential air pollution area in real-time without the requirement of a distributed array of sensors, and it avoids environmental modifications. Then we propose the hybrid artificial potential field (HAPF) by integrating the predicted result with the artificial potential field method, to generate a heuristic path that can navigate the robot to the affected areas with obstacle avoidance. Experimental testing in simulated and real-world environments demonstrates the effectiveness of the proposed approach in rapidly planning purification paths, marking significant progress in automated air purification, and disinfection technology. © 2023 IEEE.

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Event-Triggered Observer-Based Fixed-Time Consensus Control for Uncertain Nonlinear Multiagent systems with Unknown States

arXiv

引用

arXiv 2024年

作者： Zhou, Kewei Wang, Ziming Chen, Zhihao Wang, Xin Southwest University Westa College Chongqing China Robotics and Autonomous Systems Thrust Guangzhou China Wuhan University Electronic Information School Wuhan China Southwest University College of Electronics and Information Engineering Chongqing China

This paper introduces a novel approach for achieving fixed-time tracking consensus control in multiagent systems (MASs). Departing from the reliance on traditional controllers, our innovative controller integrates modified tuning and Lyapunov functions to guarantee stability and convergence. Furthermore, we have implemented an event-triggered strategy aimed at reducing the frequency of updates, alongside an output-feedback observer to manage unmeasured states effectively. To address the challenges posed by unknown functions and algebraic-loop problems, we opted for radial basis function neural networks (RBF NNs), chosen for their superior performance. Our methodology successfully mitigates Zeno's behavior and ensures stability within a narrowly defined set. The efficacy of our proposed solution is validated through two illustrative simulation examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Radial basis function networks

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Locomotion Analysis of a Modular Underactuated Snake-like Robot

Locomotion Analysis of a Modular Underactuated Snake-like Ro...

引用

IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Zhenxuan Ma Jun Zhang Yiming Cao Zaiyang Liu Yang Tian Longchuan Li Shugen Ma Zhongkui Wang Graduate School of Science and Engineering Ritsumeikan University Kusatsu Shiga Japan Department of Robotics Ritsumeikan University Kusatsu Shiga Japan College of Information Science and Technology Beijing University of Chemical Technology Beijing China Robotics & Autonomous Systems Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The exploration of snake locomotion has significantly informed the field of robotics, especially in the development of robots capable of navigating complex terrains. While most robotic designs traditionally rely on the assumption of uniform force distribution for movement, the nuanced agility of snakes suggests a more selective application of force across specific joints. This paper introduces a novel underactuated snake-like robot, drawing inspiration from the adaptive locomotion strategies observed in desert snakes. We propose a modular design, incorporating joints with varying functionalities, to enhance the robot’s adaptability to diverse environmental challenges. Through simulation and experimentation on flat and inclined surfaces, we validate the effectiveness of this modular design in improving locomotive efficiency.

关键词： Navigation Force Real-time systems Robots

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