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

作者： Seyed Ali Shahzadeh Fazeli Sohrab Allahyari Arman Gholibeikian S. Ali A. Moosavian Dept. of Mechanical Engineering Advanced Robotics & Automated Systems (ARAS) Laboratory Center of Excellence in Robotics & Control K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Mecanum wheeled mobile robots have increasingly caught researchers' attention due to their wide range of applications and higher maneuverability. However, their complex structure makes their trajectory tracking and control problem more challenging. This paper presents a cascaded controller for trajectory tracking of a four-wheel Mecanum mobile robot. The cascaded controller consists of a linear time-varying model predictive controller (LTV-MPC) and an inner PID controller. The proposed controller does not require the system's dynamics model. To this end, the linear time-varying error model used to design the MPC is obtained from the robot's kinematics model. The angular velocities obtained from the predictive controller are then mapped into the required commands for the DC motors using a PID controller. The PID controller is fine-tuned considering the motor's dead zone using the Genetic Algorithm. Simulations and experimental results demonstrate the superiority of the cascaded controller over the regular LTV-MPC. The proposed algorithm can also be used for other real-time implementations of robotic tasks.

关键词： Trajectory tracking System dynamics Robot kinematics Predictive models DC motors Angular velocity Real-time systems Mobile robots Voltage control Genetic algorithms

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The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments

arXiv

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arXiv 2024年

作者： Sharma, Shivansh Huang, Mathew Nair, Sanat Wen, Alan Petlowany, Christina Moore, Juston Wanna, Selma Pryor, Mitch Nuclear and Applied Robotics Group Department of Mechanical Engineering Advanced Research in Cyber Systems Los Alamos National Laboratory United States

Industry 4.0 introduced AI as a transformative solution for modernizing manufacturing processes. Its successor, Industry 5.0, envisions humans as collaborators and experts guiding these AI-driven manufacturing solutions. Developing these techniques necessitates algorithms capable of safe, real-time identification of human positions in a scene, particularly their hands, during collaborative assembly. Although substantial efforts have curated datasets for hand segmentation, most focus on residential or commercial domains. Existing datasets targeting industrial settings predominantly rely on synthetic data, which we demonstrate does not effectively transfer to real-world operations. Moreover, these datasets lack uncertainty estimations critical for safe collaboration. Addressing these gaps, we present HAGS: Hand and Glove Segmentation Dataset. This dataset provides challenging examples to build applications toward hand and glove segmentation in industrial human-robot collaboration scenarios as well as assess out-of-distribution images, constructed via green screen augmentations, to determine ML-classifier robustness. We study state-of-the-art, real-time segmentation models to evaluate existing methods. Our dataset and baselines are publicly available. © 2024, CC BY.

关键词： Semantic Segmentation

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A Contactless Magnetic Frequency-Boosting Mechanism for Wind Energy Harvesting

A Contactless Magnetic Frequency-Boosting Mechanism for Wind...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Yunfei Li Manjuan Huang Tianyi Tang Heng Zhao Lining Sun Huicong Liu State Key Laboratory of Robotics and System Harbin Institute of Technology (HIT) CHINA School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University CHINA

ISBN: (数字)9798350380200

ISBN: (纸本)9798350380217

This paper presents a non-contact magnetic frequency-boosting wind energy harvester suitable for low wind speed environments, designed to power various IoT sensors. The device comprises a driving component, a driven component, and a stator component. These components work together to convert low-speed rotational motion caused by wind into high-speed rotational motion, thereby achieving high-power electromagnetic energy output. Performance tests have shown that the output voltage increases with wind speed, reaching a peak voltage of 27V and a peak power of 1.6W at a wind speed of 10 m/s. The device has successfully powered LED bulbs and Bluetooth sensors, demonstrating its practicality for real-time environmental monitoring applications.

关键词： Temperature sensors Wind energy Wind speed Magnetic sensors Voltage Stators Sensor systems Environmental monitoring Electromagnetics Power generation

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Shape Estimation of Continuum Arms Based on Inverse Kinematics Data-Driven Modeling

Shape Estimation of Continuum Arms Based on Inverse Kinemati...

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

作者： Saeedeh Shekari S. Ali A. Moosavian Dept of Mechanical Engineering Advanced Robotics and Automated Systems (ARAS) Lab. Center of Excellence in Robotics and Control K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

In recent decades, soft robotic arms inspired by biological organisms with flexible structures have enabled significant advancements in robotics, particularly by allowing robots to navigate in complex and unknown environments. Tendon-driven continuum robotic arms, as a type of such systems, are characterized by their infinite degrees of freedom and the inherent complexity of their nonlinear systems, while the analysis of their configurations is highly challenging. In this study, a data-driven inverse kinematics model is developed to estimate the shape of a two-segment continuum robot called RoboArm in ARAS Lab. The robot's final configuration was recorded using two cameras, and the shape estimation was performed by tracking the position of each segment's end along a given dynamic path. The experimental results were compared with software simulations which demonstrate high accuracy in predicting the arm's configuration. By comparing the estimated shapes from the simulations with the robot's actual shape during movement, we validated the effectiveness of this approach for accurately modeling the robot's configuration in real-time.

关键词： Data-driven modeling Accuracy Shape Tracking Estimation Kinematics Soft robotics Real-time systems Software Continuum robots

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Feedback linearization and equivalent-disturbance compensation control strategy for piezoelectric stage

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Nanotechnology and Precision engineering 2024年第2期7卷 49-59页

作者： Tao Huang Yingbin Wang Zhihong Luo Huajun Cao Guibao Tao Mingxiang Ling State Key Laboratory of Mechanical Transmissions for Advanced Equipment Chongqing UniversityChongqing 400044China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China Hongdu Aviation Industry Group NanchangJiangxi 330000China Robotics and Microsystems Center Soochow UniversitySuzhou 215123China

Piezoelectric stages use piezoelectric actuators and flexure hinges as driving and amplifying mechanisms,*** systems have high positioning accuracy and high-frequency responses,and they are widely used in various precision/ultra-precision positioning ***,the main challenge with these devices is the inherent hysteresis nonlinearity of piezoelectric actuators,which seriously affects the tracking accuracy of a piezoelectric *** by this challenge,in this work,we developed a Hammerstein model to describe the hysteresis nonlinearity of a piezoelectric *** particular,in our proposed scheme,a feedback-linearization algorithm is used to eliminate the static hysteresis *** addition,a composite controller based on equivalent-disturbance compensation was designed to counteract model uncertainties and external *** analysis of the stability of a closed-loop system based on this feedback-linearization algorithm and composite controller was performed,and this was followed by extensive comparative experiments using a piezoelectric stage developed in the *** experimental results confirmed that the feedback-linearization algorithm and the composite controller offer improved linearization and trajectory-tracking performance.

关键词： Piezoelectric stage Hysteresis nonlinearity Feedback linearization Equivalent-disturbance compensation

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Solving Challenging Tasks with Manipulation Policy with Limited Demonstrations

Solving Challenging Tasks with Manipulation Policy with Limi...

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IEEE/ASME (AIM) International Conference on advanced Intelligent Mechatronics

作者： Haofeng Liu Jiayi Tan Yanchun Cheng Yiwen Chen Haiyue Zhu Marcelo H Ang Department of Mechanical Engineering National University of Singapore Advanced Robotics Centre College of Design and Engineering National University of Singapore Agency for Science Technology and Research (A*STAR)

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Deep Reinforcement Learning (DRL), combined with demonstration data, has progressed in developing manipulation policies. However, the practical collection of ample high-quality demonstrations is timeconsuming, and demonstrations generated by humans may not perfectly align with the operational demands of robots. Our study suggests that RL agents in manipulation tasks are acutely sensitive to demonstration quality in both online and offline environments. Utilizing low-quality or limited demonstrations to enhance RL policy development remains a significant challenge. To address these concerns, we propose TD3+Smooth BC, an enhancement of TD3+BC [1] and TD3fG [2]. This algorithm ensures a smooth transition from learning through expert demonstrations to learning from experience, incorporating prior knowledge while mitigating negative demonstration impacts. In the offline setting, TD3+Smooth BC adjusts the balance between imitation and Q-value maximization dynamically, effectively utilizing human demonstrations for efficient policy learning. The algorithm demonstrates marked improvements in the Adroit manipulator and MuJoCo tasks, even with limited demonstrations of varying quality.

关键词： Mechatronics Heuristic algorithms Robot sensing systems Deep reinforcement learning Task analysis Manipulator dynamics

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Cam-Driven Frequency Up-Conversion Mechanism For Kinetic Energy Harvesting

Cam-Driven Frequency Up-Conversion Mechanism For Kinetic Ene...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Heng Zhao Tianyi Tang Yunfei Li Mingqi Mei Huicong Liu School of Mechanical and Electrical Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou CHINA State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin CHINA

ISBN: (数字)9798350380200

ISBN: (纸本)9798350380217

This paper proposes an ocean wave energy harvester (WEH) with a cam-driven frequency up-conversion mechanism. It utilizes a planar pendulum structure to achieve motion response to ultra-low-frequency wave excitation and then converts the mechanical energy of the pendulum into high-frequency and high-power electrical energy output through the cam-driven frequency upconversion mechanism. WEH’s superior design enables high-power output even under excitation below 1 Hz. At the excitation frequency of 0.75 Hz and resistance load of $20 \Omega$, the maximum power output can reach 461.4 mW. Due to its high output power, the WEH can be integrated into the ocean observation buoy to power the sensors, thereby enabling long-term, unattended operation of the buoy in remote offshore areas.

关键词： Temperature sensors Resistance Ocean waves Frequency conversion Sensor systems Sensors Power systems Wave energy conversion Power generation Ocean temperature

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A bi-objective Smart Capacitated Vehicle Routing Problem with Threshold Waste Level for the Home Health Care

A bi-objective Smart Capacitated Vehicle Routing Problem wit...

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IEEE International Conference on engineering, Technology and Innovation

作者： Kubra Sar Pezhman Ghadimi Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Dublin Ireland

This article presents a bi-objective mixed-integer linear programming (MILP) model for solving the smart capacitated vehicle routing problem with threshold waste level (SCVRP-TWL) with a specific application in home health care services waste management. The model aims to optimize the route of the vehicles for the purpose of minimizing both the total travel time and the total carbon emission. The data used in the model are randomly generated, based on the real coordinates of Dublin city. The results of the model are visualized using the Google API direction tools. The research makes three key contributions to the field of vehicle routing and waste management. The integration of real-time traffic data and waste threshold level in the model, which allows for more accurate and efficient routing decisions. The application of the model in the unique domain of home health care (HHC) services waste management, which addresses a specific and important problem in this field. The use of the non-dominated sorting genetic algorithm II (NSGA-II) as the solution algorithm which allows for a better balancing of the trade-offs between the two objectives.

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A Wave Energy Harvester With Vertical Pendulum And Mag-Boost Mechanism

A Wave Energy Harvester With Vertical Pendulum And Mag-Boost...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Tianyi Tang Yunfei Li Heng Zhao Lining Sun Huicong Liu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin CHINA School of Mechanical and Electrical Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou CHINA

ISBN: (数字)9798350380200

ISBN: (纸本)9798350380217

This paper presents an ultra-low-frequency and high-power wave energy harvester (WEH). It utilizes a vertical pendulum structure to achieve motion response to ultra-low-frequency wave excitation and then converts the mechanical energy of the pendulum into high-frequency and high-power electrical energy output through the MagBoost Mechanism. The design of WEH ensures stable output even under excitation conditions below 1 Hz. At the excitation frequency of 1.4 Hz and resistance load of $20 \Omega$, the maximum power output can reach 1.16 W with a power density of $0.54 \mathrm{~mW} / \mathrm{cm}^{3}$. The high energy density allows stable power supplementation for ocean observation buoy within a limited volume, thereby enabling long-term, unattended operation of the buoy in remote offshore areas.

关键词： Resistance Power system measurements Density measurement Oceans Frequency conversion Sensor systems Sensors mechanical energy Wave energy conversion Power generation

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A Review of Artificial Intelligence Application on Enhancing Resilience of Closed-loop Supply Chain

A Review of Artificial Intelligence Application on Enhancing...

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IEEE International Conference on engineering, Technology and Innovation

作者： Yang Hu Pezhman Ghadimi Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Dublin Ireland

The purpose of this review is to investigate the landscape of scientific literature in analysing the Artificial Intelligence (AI) technology capabilities for enhancing supply chain resilience both from the forward and reverse side in times of increased risk and uncertainty. This article adopts a systematic literature analysis to extract and analyse the related papers that implemented AI in typical SC functions that can help develop supply chain resilience. It is found that AI technology can be considered as enabling technology for mitigating disruption risk in the closed-loop supply chain due to higher efficiency and accuracy in supplier selection, facility allocation, inventory planning, capacity planning, and demand forecasting, leading to resilience and robustness of a closed-loop supply chain. This review also suggested that a more comprehensive, in-depth, and wide range study from both forward and reverse, especially the reverse side from the reactive, dynamic aspects should be conducted in the future to better reduce supply chain risk more efficiently, effectively, and completely.

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