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control Theory and Applications (ICoCTA), International Conference on

作者： Mohammed A. M. Hussein Maki K. Habib Robotics Control and Smart Systems The American University in Cairo Cairo Egypt Department of Mechanical Engineering The American University in Cairo Cairo Egypt

ISBN: (数字)9798331506155

ISBN: (纸本)9798331506162

This research addresses the critical challenge of enhancing object recognition and real-time response capabilities in autonomous vehicles (AVs) under varying simulated conditions, which is crucial for ensuring both navigational safety and operational efficiency. Utilizing the Carla 0.9.14 simulator and Unreal Engine 4.26 on Ubuntu 20.04, we focus on improving the detection and classification of key on-road obstacles—vehicles, pedestrians, and cyclists—using the YOLOv7 object detection algorithm. By integrating advanced sensory technologies, specifically stereo vision cameras and LIDAR, we create a dynamic testing environment that simulates diverse urban and rural scenarios. Our methodology enhances the YOLOv7 algorithm's accuracy and speed through extensive training on a meticulously curated dataset of 4,113 images, reflecting a broad spectrum of environmental conditions, including varying lighting and weather conditions. This rigorous approach yielded a significant increase in mean average precision (mAP) from 64.3% to 76.3%, and enhanced the algorithm's reliability, with notable improvements over previous models. The research delineates a clear advancement in AV technology by demonstrating substantial improvements in object detection metrics, contributing foundational insights for future implementations in real-world settings and supporting the further development of real-time avoidance systems. This study not only progresses the field of AV but also sets a new benchmark for object detection performance, aligning with industry and academic goals to optimize AV systems for complex and unpredictable driving scenarios.

关键词： Laser radar Navigation Heuristic algorithms Object detection Real-time systems Data models Classification algorithms Vehicle dynamics Autonomous vehicles Engines

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Data-Efficient control Barrier Function Refinement

Data-Efficient Control Barrier Function Refinement

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American control Conference (ACC)

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

control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, a CBF 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 two-link arm.

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Novel Disturbance Observer Relevant Parametric System Identification Based on Robust Stability Criterion

Novel Disturbance Observer Relevant Parametric System Identi...

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control Technology and Applications (CCTA),

作者： Hanul Jung Paul Tacx Tom Oomen Sehoon Oh Department of Robotics Engineering DGIST Daegu Korea Department of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands

In this paper, novel parametric identification is addressed by the disturbance observer (DOB) relevant algorithm. To fully utilize the disturbance observer, a proper nominal model, which is a parametric model, is essential. The control performance of the DOB can be checked based on the robust stability criterion, which is related to the modeling error and the desired bandwidth of the DOB. Consequently, it is beneficial to evaluate the parametric model in the system identification procedure. Therefore, this paper proposes a novel disturbance observer-relevant system identification algorithm based on the robust stability criterion of the disturbance observer. The effectiveness of the proposed method is validated by showing the numerical simulations under specific conditions.

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Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

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Science China(Information Sciences) 2021年第5期64卷 151-162页

作者： Xiang ZHANG Shuping HE Vladimir STOJANOVIC Xiaoli LUAN Fei LIU Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil EngineeringUniversity of Kragujevac Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable LyapunovKrasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters U, G and V. Finally, the filter gains and the optimal index α*are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

关键词： dissipative filtering hidden Markov model finite-time boundedness Markov jump systems conic-type nonlinearity

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MateRobot: Material Recognition in Wearable robotics for People with Visual Impairments

MateRobot: Material Recognition in Wearable Robotics for Peo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Junwei Zheng Jiaming Zhang Kailun Yang Kunyu Peng Rainer Stiefelhagen Institute for Robotics and Anthropomatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Engineering Science University of Oxford UK School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered robotics. To fill this gap, in this work, a wearable vision-based robotic system, MATERobot, is established for PVI to recognize materials and object categories beforehand. To address the computational constraints of mobile platforms, we propose a lightweight yet accurate model MATEViT to perform pixel-wise semantic segmentation, simultaneously recognizing both objects and materials. Our methods achieve respective 40.2% and 51.1% of mIoU on COCOStuff-10K and DMS datasets, surpassing the previous method with +5.7% and +7.0% gains. Moreover, on the field test with participants, our wearable system reaches a score of 28 in the NASA-Task Load Index, indicating low cognitive demands and ease of use. Our MATERobot demonstrates the feasibility of recognizing material property through visual cues and offers a promising step towards improving the functionality of wearable robots for PVI. The source code has been made publicly available at MATERobot.

关键词： Visualization Source coding Semantic segmentation Visual impairment Semantics Wearable robots Mobile applications

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Pulsating Fluidic Sensor for Sensing of Location, Pressure and Contact Area

Pulsating Fluidic Sensor for Sensing of Location, Pressure a...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Joanna Jones Marco Pontin Dana D. Damian Department of Automatic Control and Systems Engineering Sheffield Biomedical Robotics Laboratory University of Sheffield United Kingdom Sheffield Robotics University of Sheffield UK Insigneo Institute for in Silico Medicine University of Sheffield UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Designing information-rich and space-efficient sensors is a key challenge for soft robotics, and crucial for the development of safe soft robots. Sensing and understanding the environmental interactions with a minimal footprint is especially important in the medical context, where portability and unhindered patient/user movement is a priority, to move towards personalized and decentralized healthcare solutions. In this work, a pulsating fluidic soft sensor (PFS) capable of determining location, pressure and contact area of press events is shown. The sensor relies on spatio-temporal resistance changes driven by a pulsating conductive fluid. The sensor demonstrates good repeatability and distinction of single and multiple press events, detecting single indents of sizes greater than 1 cm, forces larger than 2 N, and various locations across the sensor, as well as multiple indents spaced 2 cm apart. Furthermore, the sensor is demonstrated in two applications to detect foot placement and grip location. Overall, the sensor represents an improvement towards minimizing electronic hardware, and cost of the sensing solution, without sacrificing the richness of the sensing information in the field of soft fluidic sensors.

关键词： Presses Fluids Soft sensors Medical services Soft robotics Robot sensing systems Hardware

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Model Predictive control with Model Error Compensation by Koopman Approach 48

Model Predictive Control with Model Error Compensation by Ko...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Kanai, Masaki Yamakita, Masaki R&d Group Hitachi Ltd. Controls and Robotics Innovation Center Ibaraki Japan Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This study proposes a method for high-performance model predictive control (MPC) by using a lifted bilinear model error model (MEM) based on the Koopman approach to compensate for errors in the prediction model. In MPC, more accurate prediction models can be expected to provide better control performance for the real plant. However, there often exist modeling errors in the prediction model, which may prevent MPC from achieving good performance. In this study, we identify the errors in the nominal model as an MEM, and formulate an MPC that integrates the nominal model and the MEM into an "error-compensated model"as a prediction model. We model the MEM with a lifted bilinear model that can represent nonlinear characteristics based on Koopman's theory. In addition, an efficient linear MPC algorithm is constructed by linearizing the MEM while maintaining high prediction accuracy. Numerical simulations demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Model predictive control

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A control Strategy for Bidirectional Buck Converter with Combining Continuous and Active Discontinuous Conducting Modes

A Control Strategy for Bidirectional Buck Converter with Com...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Evgeniy Stolyarov Maxim Lashkevich Egor Kulik Yousef Ali Galina Demidova Alecksey Anuchin Department of Electric Drives Moscow Power Engineering Institute Moscow Russia Faculty of Control System and Robotics ITMO University Saint Petersburg Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The hybrid electric powertrains can utilize bidirectional buck converters to charge the battery while driving and discharge it when extra power is required. In order to increase the power density, the wide-bandgap switches should be used. These switches have bidirectional conductivity mode as they are metal-oxide-semiconductor field-effect transistors (MOSFET) and the utilization of the freewheeling diode is undesirable due to high voltage drop across it and much higher losses in conducting mode. This paper proposes the combined control strategy which implements continuous conducting mode at high and active discontinuous conducting mode at low currents. The use of model predictive control allows to minimize the conducting losses by means of reverse conductivity mode of the switches. The paper provides derivation of the combined control strategy. Then, the simulation results show the effectiveness of the proposed method. Finally, the simulation was performed with various errors in the definition of the system parameters.

关键词： Power system measurements MOSFET Buck converters Density measurement Mechanical power transmission Conductivity control systems

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Thermally-activated Biochemically-sustained Reactor for Soft Fluidic Actuation

Thermally-activated Biochemically-sustained Reactor for Soft...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jialun Liu MennaAllah Soliman Dana D. Damian Automatic Control and Systems Engineering Department Sheffield Robotics University of Sheffield Sheffield UK Insigneo Institute for in Silico Medicine University of Sheffield UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Soft robots have shown remarkable distinct capabilities due to their high deformation. Recently increasing attention has been dedicated to developing fully soft robots to exploit their full potential, with a recognition that electronic powering may limit this achievement. Alternative powering sources compatible with soft robots have been identified such as combustion and chemical reactions. A further milestone to such systems would be to increase the controllability and responsiveness of their underlying reactions in order to achieve more complex behaviors for soft robots. In this paper, we present a thermally-activated reactor incorporating a biocompatible hydrogel valve that enables control of the biochemical reaction of sugar and yeast. The biochemical reaction is utilized to generate contained pressure, which in turn powers a fluidic soft actuator. Experiments were conducted to evaluate the response time of the hydrogel valves with three different crosslinker concentrations. Among the tested concentrations, we found that the lowest crosslinker concentration yielded the fastest response time of the valve at an ambient temperature of 50°C. We also evaluated the pressure generation capacity of the reactor, which can reach up to 0.22 bar, and demonstrated the thermoresponsive behavior of the reactor to trigger a biochemical reaction for powering a fluidic soft actuator. This work opens up the possibility to power and control tetherless and fully soft robots.

关键词： Actuators Electric potential Deformation Hydrogels Soft robotics Valves Time factors

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