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International Conference on Advanced Computer Control, ICACC

作者： Aftaab Ahamed Karthik Jayaraj Shibin Fazil Nikitha S Sudheer A P Robotics Interest Group Mechatronics/Robotics Laboratory National Institute of Technology Calicut Mechanical Engineering Department Mechatronics/Robotics Laboratory National Institute of Technology Calicut

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

Holonomic drives are gaining popularity in many fields due to their versatility in various industrial applications. The ability of the holonomic drive robots to move in all directions makes it distinct from other drive mechanisms. Open-loop control of omnidirectional bots can be achieved with relative ease whereas automating it is not as easy. When trying to create an automated system with lower cost, off the shelf components and hardware, other problems may arise as a result of the poor performance of the components. Here a three wheeled omnidirectional robot that can trace a predefined path without human intervention has successfully been developed.

关键词： Location awareness Visualization Costs Service robots Debugging Drives Hardware Mobile robots Robots Wireless fidelity

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Photocatalytic bio-inspired PDA-RGO/g-C3N4 nanocomposite incorporated PES membrane for removal of environmental antibiotic pollutants

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Journal of Chemical Technology and Biotechnology 2024年第6期99卷 1401-1413页

作者： Govardhanan, B. GokulaKrishnan, S.A. Arthanareeswaran, G. Ashok, M. Chen, Yong-Song Taweepreda, W. Membrane Research Laboratory Department of Chemical Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India New Generation Materials lab Department of Physics National Institute of Technology Tiruchirappalli Tiruchirappalli India Department of Mechanical Engineering and Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng University Chiayi County Taiwan Polymer Science Program Division of Physical Science Faculty of Science Prince of Songkla University Hat-Yai Thailand

BACKGROUND: Membrane technology makes it more suitable for the treatment of environmental polluted organic wastewater. Membrane fouling is one of the major concerns in commercial membranes. To overcome this issue, photocatalytic nanomaterials are embedded into the membrane to enhance its surface nature and reusability. In this investigation, polydopamine functionalized graphene oxide (PDA-RGO), graphite carbon nitride (g-C3N4) and PDA-RGO/g-C3N4 nanomaterials were incorporated within polyethersulfone membrane fabricated via a phase inversion technique. These as-prepared membranes’ functional groups and surface morphologies were investigated by Fourier transform infrared (FTIR) spectroscopy, atomic force microscopy (AFM) and field emission electron microscopy (FESEM). These as-prepared membranes were investigated for photocatalytic activity under visible-light-driven photodegradation by environmental pollutants such as Rhodamine B (RhB) and Norfloxacin (NOR). RESULTS: Compared with pristine PES membrane, the PDA-RGO/g-C3N4-PES nanocomposite membrane showed higher photocatalytic activity for Rhodamine B 94.6% and Norfloxacin 96.8%, also reached greater water flux 189 L m−2 h−1. Therefore, the bio-inspired nanocomposite-modified PDA-RGO/g-C3N4 photocatalytic membrane could have huge potential in processing environmentally polluted water. CONCLUSIONS: PDA-RGO/g-C3N4-PES nanocomposite membrane improved the membrane hydrophilicity, water flow, anti-fouling performance, rejection studies and photocatalytic degradation than the pristine membrane. The synergistic result of PDA-RGO and g-C3N4 exhibits better photodegradation efficiency of the membrane towards environmental pollutants. © 2024 Society of Chemical Industry (SCI). © 2024 Society of Chemical Industry (SCI).

关键词： Nanocomposites

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An object recognition method based on deep learning

An object recognition method based on deep learning

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2021 International Conference of Optical Imaging and Measurement, ICOIM 2021

作者： Duan, Yingni Xi'an University School of Mechanical and Material Engineering Department of Robotics Engineering Xi'an China

ISBN: (纸本)9780738112121

Deep learning's self-learning capabilities are used to identify objects. Based on the traditional CNN convolutional neural network, this paper constructs a new network model, in which the 9 linear stacked inception modules are embedded. The use of inception modules reuses more features, improves the width of feature tensors, optimizes the training speed and improves the detection accuracy. Firstly, food picture data source is constructed and preprocessed. Then, some images are used to train the model, and the optimization algorithm SGDM is used to update the parameters, and the influence of learning rate on the training effect of the model is compared. Finally, the model is tested by testing the food pictures in the test data set, and the simulation based on MATLAB shows that the model improves the efficiency and accuracy of object recognition. It reflects the potential and advantages of deep learning in feature extraction. © 2021 IEEE.

关键词： Convolutional neural networks

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INVESTIGATING SUSTAINABLE PRODUCTION PLANNING OF AN INDUSTRIAL PROGRESSIVE DIE STAMPING PROCESS USING DISCRETE EVENT SIMULATION

INVESTIGATING SUSTAINABLE PRODUCTION PLANNING OF AN INDUSTRI...

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ASME 2024 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2024

作者： Borbolla, Adrián Aboujarra, Mohamed Nezar Ramanujan, Devarajan Advanced Manufacturing Engineering Grundfos A/s Bjerringbro Denmark AIRA Laboratory Smart Factory and Robotics Université Hassan 1er Settat Morocco Department of Mechanical and Production Engineering Aarhus University Aarhus Denmark Center for Digitalisation Big Data and Data Analytics Aarhus University Aarhus Denmark

ISBN: (纸本)9780791888391

This paper investigates the use of discrete event simulation (DES) as a potential decision-support tool for environmentally sustainable production planning. We propose a comprehensive methodology that integrates DES design, dynamic CO2 intensity calculations, and in-depth scenario analysis. This approach facilitates simulation-driven production planning with an emphasis on evaluating and reducing the environmental impacts of manufacturing. Our methodology is applied to the evaluation of an industrial progressive die stamping process (PDSP). The PDSP is a part of a production line in a large Danish manufacturing company. The performance of the PDSP was simulated and evaluated for two working days: 09 Jan 2024 and 10 Jan 2024. Key performance indicators like utilization rate and lead time are assessed alongside environmental sustainability metrics such as process CO2 emissions and CO2 emissions per product. Building upon this baseline, we then simulate and compare two alternative production scenarios: i) increased production capacity and ii) variations in planning orders. These scenarios are then contrasted with the baseline model. Our results demonstrate the potential of DES in identifying new production strategies. The first scenario demonstrates a 7% increase in production capacity, corresponding to a 2.6% increase in process CO2 emissions. The second scenario demonstrates a 5% reduction in process CO2 emissions while retaining the same production capacity as the baseline model. These findings illustrate how DES can uncover production plans that prioritize sustainable manufacturing. Copyright © 2024 by ASME.

关键词： Stamping

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An End-to-End Proprioception Framework for Soft Continuum Robot

An End-to-End Proprioception Framework for Soft Continuum Ro...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Cao, Hanwen Chen, Wei Lu, Yiang Huang, Junda Zhou, Jianshu Liu, Yunhui The Chinese University of Hong Kong The T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Soft robots are gaining more and more attention owing to their inherent compliance and excellent flexibility, enabling new potentials for robots to address real-world chal-lenges in the manner of their natural counterparts. The soft con-tinuum robot is one typical example that reflects the successful inspiration achievement from biomimetics to robotics. However, due to the nonlinearity and ultra-high degrees of freedom of soft continuum robots, traditional kinematic modeling methods obtain unsatisfying precision, especially when robots interact with their surroundings. To precisely estimate the kinematic model of soft continuum robots, we propose a novel end-to-end proprioception method. Our method inputs time series data from the soft continuum robot actuators and outputs the end-tip position in three dimensions. The experimental results show that the proposed method can improve the kinematic accuracy compared to the constant curvature model-based method either with contact or in noncontact conditions. © 2022 IEEE.

关键词： Robots

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Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation

arXiv

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arXiv 2024年

作者： Chiu, Yi-Hung Lee, Ung Hee Song, Changseob Hu, Manaen Kang, Inseung The Department of Mechanical Engineering Carnegie Mellon University PittsburghPA15213 United States The Department of Mechanical Engineering the Department of Robotics University of Michigan Ann ArborMI48109 United States

Virtual models of human gait, or digital twins, offer a promising solution for studying mobility without the need for labor-intensive data collection. However, challenges such as the sim-to-real gap and limited adaptability to diverse walking conditions persist. To address these, we developed and validated a framework to create a skeletal humanoid agent capable of adapting to varying walking speeds while maintaining biomechanically realistic motions. The framework combines a synthetic data generator, which produces biomechanically plausible gait kinematics from open-source biomechanics data, and a training system that uses adversarial imitation learning to train the agent’s walking policy. We conducted comprehensive analyses comparing the agent’s kinematics, synthetic data, and the original biomechanics dataset. The agent achieved a root mean square error of 5.24±0.09 degrees at varying speeds compared to ground-truth kinematics data, demonstrating its adaptability. This work represents a significant step toward developing a digital twin of human locomotion, with potential applications in biomechanics research, exoskeleton design, and rehabilitation. © 2024, CC BY-NC-ND.

关键词： Biped locomotion

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Kinematic Modeling of a 7-DOF Tendon-Like-Driven Robot Based on Optimization and Deep Learning

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Journal of Field robotics 2025年第0期

作者： Chen, SaiXuan Mu, SaiHu Jiang, GuanWu Omar, Abdelaziz Zhu, Zina Niu, Fuzhou School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai China School of Mechanical Engineering Tongji University Shanghai China School of Information Engineering Southwest University of Science and Technology Mianyang China Mechatronics and Robotics Department Egyptian Russian University Badr city Cairo Egypt School of Mechanical Engineering Suzhou University of Science and Technology Suzhou China

This paper proposes a novel 7-DOF tendon-like-driven redundant robot (TDR7) based on a weighted inverse kinematics (IK) optimization algorithm and a deep learning fine-tuning model. The robot features a modular design that enables highly flexible movements of the shoulder, elbow, and wrist joints. Its kinematic model is established using the Denavit-Hartenberg (D-H) parameter method. To address the complexity of solving IK for 7-DOF redundant robots, a weighted gradient projection method specialized for TDR7 (SWGPM-TDR7) is introduced. This algorithm integrates joint constraints, singularity avoidance, and minimum energy consumption into a multi-objective optimization framework, significantly improving joint motion continuity and trajectory planning efficiency while maintaining solution accuracy. To further accommodate complex trajectory planning requirements, a deep learning fine-tuning model (RWKV-TDR7) that combines recurrent networks with self-attention mechanisms is introduced. Through fine-tuning, RWKV-TDR7 achieves efficient trajectory fitting for TDR7, supports long-sequence outputs, and reduces computational complexity. Simulation and experimental validations demonstrate that the robot exhibits excellent performance in forward kinematics, inverse kinematics, and trajectory tracking in terms of accuracy, stability, and continuity. This work provides an effective solution for the design of high-performance robotic systems in medical and industrial applications. © 2025 Wiley Periodicals LLC.

关键词： Inverse kinematics

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A Machine Learning-Based Predictive Framework for Head Impact Injuries

SSRN

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SSRN 2024年

作者： Heng, Jing Yi Chai, Rifai Mouloodi, Saeed Tse, Kwong Ming Department of Mechanical and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Telecommunications Electrical Robotics and Biomedical Engineering Swinburne University of Technology Melbourne Australia Department of Mechanical Engineering University of Melbourne Melbourne Australia

Modern technologies have rapidly developed in recent years, particular in the field of artificial intelligence (AI). Nascent technique has been adapted by researchers in their respective research fields to leverage the research outcome. Several research fields have achieved significance breakthroughs by embracing AI to address challenges. Machine learning (ML), a prominent subset of AI, is a data-driven method that enables the discovery and establishment of intricate relationship within datasets. Training ML models require large amounts of high-quality data to effectively capture and comprehend the underlying data relationships. Once adequately trained, these AI models can be deployed in real-life applications without the need for extensive computational power or resources. This research paper introduces the utilization of artificial neural networks (ANNs) to predict intracranial pressures using kinematic linear accelerations as inputs only. The proposed model incorporates three kinematic inputs (ax, ay, az) and time as inputs to the ANN. The developed ANN model exhibits the ability to predict intracranial pressure values for five distinct brain regions, representing the front, rear, left, right and rear sections of the brain. Both feedforward and time series ANNs were trained using 80 samples with 177 time-steps data. The ANN model successfully predicted intracranial pressure from head kinematics, with a correlation coefficient (R = 0.98) and root mean square error (RMSE = 0.008MPa). These findings highlight the efficacy of ANNs in accurately predicting intracranial pressure based on linear kinematic measurements. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Design and Analysis of a Vertical Electric Ducted Fan for Unmanned Aerial Vehicles 7

Design and Analysis of a Vertical Electric Ducted Fan for Un...

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2023 7th International Conference on mechanical, Aeronautical and Automotive engineering, ICMAA 2023

作者： Shiau, Y.R. Fang, J. Huang, Y.C. Department of Industrial Engineering and Systems Management Feng Chia University Taichung407 Taiwan Department of Aerospace and Systems Engineering Feng Chia University Taichung407 Taiwan Ph.D. Program of Mechanical and Aeronautical Engineering Feng Chia University Taichung407 Taiwan

Several types of multirotor unmanned aerial vehicles (UAVs) are widely used in various industrial activities. UAVs with electric ducted fans (EDFs) have been developed to mitigate the risks associated with rotary blades, which are installed on the exterior structure of conventional UAVs and exposed to the ambient environment. Particularly, vertical EDFs have become a focus in the field of UAV design because they require less space for the flow channel compared with horizontal EDFs. In this study, a vertical EDF UAV model was developed. Structural and flow field analysis was performed in Ansys software to determine the optimal design configuration. The results confirmed that the designed model facilitated smooth air flow with favorable inlet and outlet velocity. The vehicle structure required minor adjustment to reduce the frequency of occurrence of vortices. Overall, this study made two research contributions. First, an EDF UAV model was developed to mitigate the risk of rotary blades being exposed to the ambient environment. Second, a vertical EDF system was proposed to reduce the flow channel space and maintain the outlet velocity, thereby improving the takeoff, landing, and thrust performance of UAVs. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Channel flow

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Terrain Classification Based on Wheel-terrain Interaction Measurements using Aside-Wheel Camera

Terrain Classification Based on Wheel-terrain Interaction Me...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xue, Feng Hu, Longteng Yao, Chen Wei, Jin Li, Yunzhou Wang, Peichen Zhu, Zheng Jia, Zhenzhong Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China Harbin150090 China

ISBN: (纸本)9798350325706

The scientific applications over a decade have demonstrated the robust practicality of wheeled rovers, which are capable of undertaking exploration tasks in various unstructured terrains. However, sinkage and slippage are extremely common on sandy terrains. Fortunately, these risks can be mitigated in a timely manner through the perception of terrain types. Thus, terrain classification technology has become a key solution to addressing these issues. In this paper, we propose a new terrain classification pipeline for a rover over sandy terrains. It exploits the aside-wheel sensor suite to measure sinkage, contact angle, slip ratio, and normal force. Due to the strong correlation between contact characteristics and terrain types form our experiments, we can infer terrain types based on wheel-terrain interaction parameters. We train various machine learning models and obtained classification results, which is evaluated using the record dataset form a singlewheel testbed. Through Experimental comparison, most models achieve high accuracy (over 90%), we analyze the reasons behind performance discrepancies among different models and recommend LGBM as the preferred approach. In the end, we transfer the models to use data collected from rover field tests and achieve a higher level of accuracy, which proves the effectiveness of our approach. © 2023 IEEE.

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