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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

IEEE Transactions on robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Preparation of Graphene Aerogel for Application in Solar Energy Harvesting

Preparation of Graphene Aerogel for Application in Solar Ene...

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2022 Advances in Science and engineering Technology International Conferences, ASET 2022

作者： Alhammadi, Alya Alnaqbi, Amna Almarashda, Meera Alasad, Shamma Alashkar, Adnan Inayat, Abrar Abdelkareem, Mohammad Alami, Abdul Hai University of Sharjah Sustainable and Renewable Energy Engineering Department Sharjah United Arab Emirates American University of Sharjah Mechanical Engineering Department Sharjah United Arab Emirates American University of Sharjah Materials Science and Engineering PhD Program Sharjah United Arab Emirates

ISBN: (纸本)9781665418010

In this paper, Graphene Aerogel (GA) is prepared and investigated for its potential employment for solar energy harvesting applications. GA can be applied in solar cells applications as an absorption material and as a catalyst for fuel cells. GA is characterized by its extraordinary electrical conductivity and mechanical strength, in addition to a large surface and a low density. The electrical, mechanical, thermal, and absorption characteristics of GA can be tuned depending on the manufacturing process. Hence, GA is utilized in a multitude of applications such as: supercapacitors, air purification, oil and dye absorption from water, and water waste treatment. GA is also an excellent candidate for superior electrodes. In addition, GA are environmentally friendly materials since they are 100% recyclable and do not produce any ozone-depleting compounds. In this paper, GA is synthesized, characterized, and assessed for solar energy harvesting applications with the aid of absorption and Cyclic voltammetry (CV) tests. Results show that GA has immense potential in solar application due to its low band gap and outstanding optical and electrochemical properties. © 2022 IEEE.

关键词： Cyclic voltammetry

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Electric Vehicle Charging Navigation Using Waterwheel Plant Algorithm for Coordinating Smart Grids and Intelligent Transportation Systems 2

Electric Vehicle Charging Navigation Using Waterwheel Plant ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Siva Ramkumar, M. Sivaramkrishnan, M. Uday, Kaparthi Kanan, Mohammad Mohan Raj, C. Giri, Jayant SNS College of Technology Dept of ECE India Karpagam College of Engnieering Dept of EEE India CVR College of Engineering EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

The efficient charging of electric vehicles (EVs) remains essential to create sustainable mobility while EVs transform into crucial elements of upcoming transportation systems. The present EV charging navigation systems deal with three main issues including grid congestion and inadequate charging station (CS) selection and delays from traffic *** paper proposes a novel Waterwheel Plant Algorithm (WWPA) for optimizing EV charging navigation within the Smart Grid (SG) and Intelligent Transportation Systems (ITS). The suggested method strives to enhance SG-EV coordination through demand balancing in addition to charge price reduction and decreased grid congestion levels. WWPA algorithm serves as the proposed method to optimize EV charging schedules. The MATLAB platform serves as the implementation environment for the proposed method which demonstrates superior performance than Multi-agent Reinforcement Learning (MARL), Deep Reinforcement Learning (DRL), Coati-Northern Goshawk Optimization (Coati-NGO) and Grey Sail Fish Optimization (GSFO). Results demonstrate that the WWPA algorithm reduces average charging expenses by 18.14% throughout diverse charging operations as it produces superior performance than existing algorithms operating in the SG ITS. © 2025 IEEE.

关键词： Reinforcement learning

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Uncertainty Quantification and Propagation for Multiscale Materials Systems with Agglomeration and Structural Anomalies

SSRN

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SSRN 2024年

作者： Comlek, Yigitcan Mojumder, Satyajit van Beek, Anton Prabhune, Prajakta Ciampaglia, Alberto Apley, Daniel W. Brinson, L. Catherine Liu, Wing Kam Chen, Wei Department of Mechanical Engineering Northwestern University Evanston United States Theoretical and Applied Mechanics Program Northwestern University Evanston United States School of Mechanical and Materials Engineering University College Dublin Dublin Ireland Thomas Lord Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy Department of Industrial Engineering and Management Sciences Northwestern University Evanston United States

Advancements in manufacturing technologies have enabled material system design optimization at multiple length scales. However, the microstructural anomalies and uncertainties present at each scale have not been considered comprehensively enough for systems to be robust to manufacturing variations. Consequently, considering anomalies at various length scales of the material’s structure and understanding their effects on a part’s response are crucial for designing engineered parts that are robust to various sources of uncertainty. In this work, an efficient bottom-up hierarchical uncertainty quantification and propagation framework bridging multiple scales to establish a design allowable range for the material system at the part-scale is presented. We demonstrate our framework on an additively manufactured polymer nanocomposite material system that is subject to anomalies at different length scales that impact the material properties. Specifically, agglomeration defects formed due to attractive forces between nanoparticles at the microscale and structural variabilities caused by the voids resulting from different processing conditions at the mesoscale are considered. The introduced hierarchical sampling framework combines (i) an innovative microstructure image characterization and reconstruction, (ii) a mechanistic reduced-order model that enables fast property predictions for high-dimensional microstructural design spaces, and (iii) efficient uncertainty quantification and propagation through Copula-based sampling across multiple length scales to reduce the sampling budget by 95%. Through validation of our method, we demonstrate the importance of considering the effects of process-induced microstructure features on the performance of a part. © 2024, The Authors. All rights reserved.

关键词： Microstructure

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Impacts of Aging and Relative Humidity on Properties of Biomass Burning Smoke Particles

ACS ES&T air

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ACS ES&T air 2025年第1期2卷 109-118页

作者： Sofie K Schwink Liora E Mael Thomas H Dunnington Maximilian J Schmid Jonathan M Silberstein Andrew Heck Nicholas Gotlib Michael P Hannigan Marina E Vance Environmental Engineering Program University of Colorado Boulder 1111 Engineering Drive Boulder Colorado 80309-0428 United States. Department of Mechanical Engineering University of Colorado Boulder 1111 Engineering Drive Boulder Colorado 80309-0427 United States. Department of Aerospace Engineering University of Colorado Boulder 429 UCB 3775 Discovery Drive Boulder Colorado 80303 United States.

Quantifying changes in the properties of smoke aerosols under varying conditions is important for understanding the health and environmental impacts of exposure to smoke. Smoke composition, aerosol liquid water content, effective density (ρ), and other properties can change significantly as smoke travels through areas under different ambient conditions and over time. During this study, we measured changes in smoke composition and physical properties due to oxidative aging and exposure to humidity. We found that smoke aging led to SOA formation and increases in ratios of organic carbon to elemental carbon. Aerosol liquid water content increased with increasing relative humidity (RH), and aged smoke took up more water than fresh smoke at all humidity levels, likely due to a combination of changes in aerosol surface polarity at low and medium RH and increases in surface area with aging at high RH. Growth factors ranged from 1.06 ± 0.08 for fresh smoke at low RH to 1.32 ± 0.08 for aged smoke at high RH. Oxidative aging and exposure to humidity led to increases in ρ. For 100 nm particles, ρ ranged from ∼1.2 for fresh smoke at low RH to ∼1.6 for aged smoke at high RH. Results from these experiments suggest that exposure to humidity leads to smoke restructuring and compaction and/or changes in surface chemistry.

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Performance-Based Earthmoving Team Organization Algorithm Enabling Task Completion Under Changing Conditions

Performance-Based Earthmoving Team Organization Algorithm En...

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IEEE/SICE International Symposium on System Integration

作者： Wakana Endo Shota Chikushi Yuichiro Sueoka Yuki Kato Kaito Kimura Yusuke Ikemoto Ren Komatsu Keiji Nagatani Atsushi Yamashita Hajime Asama Department of Precision Engineering Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo Japan Department of Robotics Kindai University Hiroshima Japan Department of Mechanical Engineering Osaka University Suita Osaka Japan Department of Mechanical Engineering Meijo University Nagoya Aichi Japan Department of Human and Engineered Environmental Studies Graduate School of Frontier Sciences The University of Tokyo Kashiwa Chiba Japan

In this study, we propose a methodology for automating earthmoving tasks using autonomous excavators and dump trucks, with an aim to enhance adaptability in response to environmental changes. Our methodology involves a general architecture that fosters self-organized swarm behavior among the robots. Furthermore, we present an illustrative algorithm that evaluates the performance of a team of robots and adjusts the team composition based on this performance evaluation. This algorithm was implemented in a simulation test in a dynamic environment. The results demonstrate that our methodology enables the coordination of excavators and dump trucks in environmental changes.

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Toward Learning Factory for Industry 4.0: Virtual Reality (VR) for Learning Human–Robot Collaboration 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Colombo, Giorgio Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Industry 4.0 will bring not only transformation to the manufacturing technologies but also to the profile of the workforce. Education system should be revised to prepare the future graduates embracing the knowledge of the ongoing revolution. Initiatives on the modification of curriculum and tools to deliver the concepts of Industry 4.0 must be taken. This paper examines limitations of current engineering tools and proposes a virtual reality (VR)-based learning platform to support the teaching and learning activity for Industry 4.0, focusing on the design of Human–robot Collaboration (HRC). The development process began with the identification of the general Intended Learning Outcomes (ILOs) which were extracted from the review of the HRC design issues, followed by the evaluation of the current teaching tool. From these results, the new requirements of the tools to achieve ILOs are redefined. The implementation of the system is demonstrated to show the feasibility of the proposed learning platform. This workflow can serve as initial development of another learning platform for the other innovative concepts that form the building blocks of Industry 4.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Industry 4.0

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Effect of Layer Thickness and Orientation of 3D Printed Parts on the mechanical Properties

Effect of Layer Thickness and Orientation of 3D Printed Part...

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2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022

作者： Harwood, Tyler Liu, Gloria Reyes, Aaliyah Slohoda, Jonathan Lynch, Jennifer Governor's School of New Jersey Program in Engineering and Technology 2022 New BrunswickNJ United States Rutgers University Mechanical and Aerospace Engineering Department New BrunswickNJ United States

ISBN: (纸本)9781665473453

With the many advancements in 3D printing technology in the last few decades, there is an increased need for a greater understanding of fundamental print variables. Additive manufacturing (AM) is becoming more viable for large commercial processes and more accessible to 3D printing hobbyists. The field of additive manufacturing would benefit from stronger prints to allow for a wider range of applications and innovations. Stereolithography additive manufacturing (SLA) is used to investigate the effect of layer thickness and print orientation of 3D printed parts on the mechanical properties. Using clear resin, parts were 3D printed with three layer thicknesses (25μ m50μm and 100μm) and three print orientations (0°, 45°, 90°). Tensile tests were performed in accordance with ASTM D638 and results showed a positive correlation between the Young's modulus and print orientation for parts with 100 micron layers, with the 90° specimens being the stiffest of the three print orientations in the 100 micron layer thickness group. Among all samples, the 25μm0° print proved to be the stiffest parts-A sacrifice for its brittle nature with low strain. Even so, the almost linear trends present in the 100μm samples were absent from the thinner layer prints, raising the belief that at a certain thickness, the print orientation provides no significant trends. These results raise the understanding of SLA printing parameters and the effect on printed parts, enabling more efficiency and raising the ceiling on the upper limits of 3D printing. © 2022 IEEE.

关键词： 3D printers

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ANALYSIS OF NITROGEN FLOW IN FOOD WASTE: A CASE STUDY OF THE UNIVERSITY RESTAURANT-UNB 32nd

ANALYSIS OF NITROGEN FLOW IN FOOD WASTE: A CASE STUDY OF THE...

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32nd European Biomass Conference and Exhibition, EUBCE 2024

作者： Ramos, Arthur Augusto Frasson De Brites, Eneida Campos Felipe Soares, Raquel Moraes Silveira, Edgar A. Contreras, Francisco University of Brasilia Department of Civil and Environmental Engineering DF Brasilia70910-900 Brazil University of Brasilia Graduate Program on Environmental Technology and Water Resources Department of Civil and Environmental Engineering DF Brasilia70910-900 Brazil Federal Institute of Education Science and Technology of Brasília Estrutural Campus DF Brasília71250-000 Brazil University of Brasília Mechanical Sciences Graduate Program Laboratory of Energy and Environment DF Brasilia70910-900 Brazil

With the projected global population reaching 9.7 billion by 2050, the production and consumption of food continue to escalate. Nitrogen is pivotal in this landscape due to its significance in living organisms and the environmental repercussions associated with nutrient-rich waste. Inappropriate disposal of such residues contributes to nitrogen release into the environment, impacting aquatic ecosystems and precipitating the generation of greenhouse gases. This study tackles the global challenge of effectively managing nitrogen in food waste by utilizing Material Flow Analysis (MFA) as a tool to comprehend this cycle. The analysis uncovered that vegetables, legumes, and fruits constitute the primary sources of waste generation, while meats, despite their lower mass, account for a substantial proportion of total nitrogen depletion. In the surveyed month of October 2023, 174,834 meals were served, resulting in an average food consumption N-footprint of 0.003 kg of nitrogen discarded per individual meal within the restaurant’s organic waste. These findings indicate that 65% of nitrogen is consumed in meal form, while 35% is discarded as organic solid waste. However, 53% of the nitrogen in the residues originates from food preparation processes, with food preparation responsible for over half of this figure. A deeper process analysis reveals that vegetables have low nitrogen concentrations, although they significantly contribute to waste at all stages. In contrast, meats and eggs, with higher nitrogen concentrations, emerge as noteworthy contributors to the overall nitrogen content in waste. Vegetables and meats contribute about 50% and 45% of the total nitrogen, respectively. These outcomes substantially enhance our comprehension of waste generation dynamics and nutrient utilization at the university restaurant, assisting with waste management, design of sustainable menus, reduction of food waste, and optimized resource utilization, contributing to sustainability and re

关键词： Effluent treatment

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Human-Robot Interaction Control for Multi-Mode Exosuit with Reinforcement Learning

Human-Robot Interaction Control for Multi-Mode Exosuit with ...

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

作者： Kaizhen Huang Jiajun Xu Tianyi Zhang Mengcheng Zhao Aihong Ji Guoli Song Youfu Li College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Soft exoskeleton robots have promising potential in walking assistance with comfortable wearing experience. In this study, an exosuit equipped with a twisted string actuator (TSA) is developed to provide powerful driving force and diverse operating modes for hemiplegic patients in daily life. It is challenging to establish the human-robot coupling dynamic model due to the soft structure of the exosuit and tight coupling, precise control and effective assistance are difficult to guaranteed in current exosuits. Considering the impedance characteristics of human-robot interaction, an adaptive impedance control method based on reinforcement learning (RL) is proposed, where human motion intention is utilized to optimize impedance parameters and adjust the robot's operating mode. A nonlinear disturbance observer is proposed to compensate for the effects of model estimation errors, joint friction, and external disturbances. Experimental verification demonstrates the effectiveness and superiority of the robotic system.

关键词： Legged locomotion Couplings Adaptation models Visualization Heuristic algorithms Human-robot interaction Reinforcement learning Sensor systems Impedance Intelligent sensors

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