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A Joint Learning of Force Feedback of Robotic Manipulation and Textual Cues for Granular Materials Classification

IEEE robotics and Automation Letters 2025年第7期10卷 7166-7173页

作者： Zeqing Zhang Guanqi Chen Wentao Chen Ruixing Jia Guanhua Chen Liangjun Zhang Jia Pan Peng Zhou Department of Computer Science The University of Hong Kong Hong Kong Centre for Garment Production Limited Hong Kong Hong Kong Southern University of Science and Technology Shenzhen China Robotics and Autonomous Driving Lab Baidu Research Mountain View CA USA School of Advanced Engineering The Great Bay University Dongguan China

Granular materials (GMs) are formed by a collection of particles. Even if their visual representation is straightforward, it can be seriously affected in the visually constrained environment. Based on frequency features observed in force signals, this paper proposes a non-visual classifier, GmClass, using the force feedback in the robot-granules interaction. Specifically, we transform the force sequences into the frequency domain and integrate them with high-dimensional textual information into a two-branch architecture for multimodal supervised contrastive learning (MSCL). This method achieves an 84.10% classification accuracy, surpassing traditional supervised learning by 10% and outperforming supervised contrastive learning by more than 40%, demonstrating the positive impact of adding text modality on classification, and when applied to a larger dataset, it attains an even higher 85.28% accuracy, further validating its effectiveness. Also, we demonstrate the performance of our approach in handling unseen particles and the generalization capability for varying data collection parameters.

关键词： Force Feature extraction Robots Probes Frequency-domain analysis Force feedback Contrastive learning Transforms Solids Frequency conversion

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A reasoning diagram based method for fault diagnosis of railway point system

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High-Speed Railway 2023年第2期1卷 110-119页

作者： Feng Wang Yuan Cao Clive Roberts Tao Wen Lei Tan Shuai Su Tao Tang State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijing100044China Birmingham Centre for Railway Research and Education University of BirminghamBirmingham B152TTUK School of Electronic and Information Engineering Beijing Jiaotong UniversitBeijing 100044China Fronters Science Center or Smart High-speed Raiway Systm Beijing Jiotong Univers Beijing 00044China

Railway Point system(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train *** the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high *** address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training ***,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary ***,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic *** on the results of decisions,the diagnostic rules are established to identify the failure ***,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

关键词： Railway point system Fault diagnosis Reasoning diagram Segmentation Detection method

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Chlorine-Substituent Regulation in Dopant-Free Small-Molecule Hole-Transport Materials Improves the Effi ciency and Stability of Inverted Perovskite Solar Cells

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Transactions of Tianjin University 2024年第4期30卷 314-323页

作者： Xinyi Liu Xiaoye Zhang Zhanfeng Li Jinbo Chen Yanting Tian Baoyou Liu Changfeng Si Gang Yue Hua Dong Zhaoxin Wu Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan University of TechnologyTaiyuan 030024China School of Electrical Engineering University of South ChinaHengyang 421001China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China Ningxia Hui Autonomous Region Screen Display Organic Materials Engineering Technology Research Center Ningxia Sinostar Display Material Co.Ltd.Yinchuan 750003China Organic Semiconductor Centre EaStCHEM School of ChemistryUniversity of St AndrewsFife KY169STUK Key Laboratory of Photonics Technology for Information School of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China

Although doped hole-transport materials(HTMs)off er an effi ciency benefi t for perovskite solar cells(PSCs),they inevi-tably diminish the ***,we describe the use of various chlorinated small molecules,specifi cally fl uorenone-triphenylamine(FO-TPA)-x-Cl[x=para,meta,and ortho(p,m,and o)],with diff erent chlorine-substituent positions,as dopant-free HTMs for *** chlorinated molecules feature a symmetrical donor-acceptor-donor structure and ideal intramolecular charge transfer properties,allowing for self-doping and the establishment of built-in potentials for improving charge *** effi cient hole-transfer interfaces are constructed between perovskites and these HTMs by strategi-cally modifying the chlorine ***,the chlorinated HTM-derived inverted PSCs exhibited superior effi ciencies and air ***,the dopant-free HTM FO-TPA-o-Cl not only attains a power conversion effi ciency of 20.82% but also demonstrates exceptional stability,retaining 93.8%of its initial effi ciency even after a 30-day aging test conducted under ambient air conditions in PSCs without *** fi ndings underscore the critical role of chlorine-substituent regulation in HTMs in ensuring the formation and maintenance of effi cient and stable PSCs.

关键词： Hole-transport materials Inverted perovskite solar cells Chlorinated small molecules Donor–acceptor–donor structure

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Towards Non-Robocentric Dynamic Landing of Quadrotor UAVs

arXiv

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arXiv 2024年

作者： Lo, Li-Yu Li, Boyang Wen, Chih-Yung Chang, Ching-Wei AIRo-LAB Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Research Centre for Unmanned Autonomous System The Hong Kong Polytechnic University Kowloon Hong Kong School of Engineering The University of Newcastle CallaghanNSW2308 Australia Hong Kong Center for Construction Robotics The Hong Kong University of Science and Technology New Territories Hong Kong

In this work, we propose a dynamic landing solution without the need for onboard exteroceptive sensors and an expensive computation unit, where all localization and control modules are carried out on the ground in a non-inertial frame. Our system starts with a relative state estimator of the aerial robot from the perspective of the landing platform, where the state tracking of the UAV is done through a set of onboard LED markers and an on-ground camera;the state is expressed geometrically on manifold, and is returned by Iterated Extended Kalman filter (IEKF) algorithm. Subsequently, a motion planning module is developed to guide the landing process, formulating it as a minimum jerk trajectory by applying the differential flatness property. Considering visibility and dynamic constraints, the problem is solved using quadratic programming, and the final motion primitive is expressed through piecewise polynomials. Through a series of experiments, the applicability of this approach is validated by successfully landing 18 cm × 18 cm quadrotor on a 43 cm × 43 cm platform, exhibiting performance comparable to conventional methods. Finally, we provide comprehensive hardware and software details to the research community for future reference. © 2024, CC BY.

关键词： Motion planning

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Real-Driving Co2, Nox and Fuel Consumption Prediction Using Machine Learning Approaches

SSRN

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SSRN 2022年

作者： Shahariar, G.M. Hasan Bodisco, Timothy A. Surawski, Nicholas Komol, Md Mostafizur Rahman Sajjad, Mojibul Chu-Van, Thuy Ristovski, Zoran Brown, Richard J. Biofuel Engine Research Facility QUT BrisbaneQLD4000 Australia International Laboratory for Air Quality and Health QUT BrisbaneQLD4000 Australia Centre for Green Technology University of Technology Sydney 81 Broadway UltimoNSW2007 Australia Centre for Robotics QUT BrisbaneQLD4059 Australia CSIRO Data61 Robotics and Autonomous System PullenvaleQLD4069 Australia

On-road emissions significantly contribute to urban air pollution. This study developed machine learning-based emission and fuel consumption prediction models, which can be used for optimisation and regulatory purposes. A light-duty diesel vehicle equipped with a portable emissions measurement system (PEMS) was driven on an urban test route by 30 participant drivers of disparate backgrounds to obtain a wide range of data in terms of driving behaviour and traffic conditions. CO 2 , NO x and fuel consumption prediction models were developed using linear regression (LR), support vector machine (SVM) and Gaussian process regression (GPR). The results showed that developed models could predict CO 2 , NO x and fuel consumption with an absolute relative error (ARE) of less than 9%. This method will contribute to developing emission-optimal eco-driving patterns focusing on driving behaviour and developing more effective driver training protocols which will reduce environmental impact and amount of burned fuel. © 2022, The Authors. All rights reserved.

关键词： Fuels

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Design of magnetic coupling-based anti-biofouling mechanism for underwater optical sensors

arXiv

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arXiv 2022年

作者： Ramirez, Jane Pauline Stefanini, Cesare De Masi, Giulia Romano, Donato The BioRobotics Institute Sant’Anna School of Advanced Studies Viale Rinaldo Piaggio 34 Pisa Pontedera56025 Italy Department of Excellence in Robotics and AI Sant’Anna School of Advanced Studies Pisa56127 Italy Healthcare Engineering Innovation Center Khalifa University Abu Dhabi United Arab Emirates Autonomous and Robotics Research Centre Technology Innovation Institute Abu Dhabi United Arab Emirates College of Engineering Khalifa University Abu Dhabi United Arab Emirates

Water monitoring is crucial for environmental monitoring, transportation, energy and telecommunication. One of the main problems in aquatic environmental monitoring is biofouling. The simplest method among the current antifouling strategies is the use of wiper technologies like brushes and wipers which apply mechanical pressure. In designing built-in strategies however, manufacturers usually build the sensor around the biofouling system. The current state-of-the-art is a fully integrated central wiper in the sensor that enables cleaning of all probes mounted on the sonde. Improvements in antifouling strategies lag rapid advancements in sensor technologies such as in miniaturization, specialization, and costs. Hence, improving built-in designs by decreasing size and complexity will decrease maintenance and overall costs. This design is targeted for the EU project Robocoenosis since bio-hybrid systems in this project incorporate living organisms. This technology targets selective proliferation of the organisms which only prevents biofilms on components where they are unwanted. Beyond this, the use of autonomous activation based on image processing may likely be advantageous for minimizing the need for human inspection and maintenance. In addition to Robocoenosis, we also aim at incorporating this design in another project entitled Heterogeneous Swarm of Underwater autonomous Vehicles where a swarm of heterogeneous underwater robotic fish is being developed. © 2022, CC BY.

关键词： Image processing

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Novel Design of a Soft Lightweight Pneumatic Continuum Robot Arm with Decoupled Variable Stiffness and Positioning

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Soft robotics 2018年第1期5卷 54-70页

作者： Giannaccini, Maria Elena Xiang, Chaoqun Atyabi, Adham Theodoridis, Theo Nefti-Meziani, Samia Davis, Steve Centre for Autonomous Systems and Advanced Robotics School of Computing Science and Engineering University of Salford Stoke Gifford BristolBS16 1QY United Kingdom School of Mechanical Engineering and Automation Institute of Mechatronics Engineering Northeastern University Shenyang China Department of Pediatrics University of Washington SeattleWA United States Seattle Children's Research Institute SeattleWA United States

Soft robot arms possess unique capabilities when it comes to adaptability, flexibility, and dexterity. In addition, soft systems that are pneumatically actuated can claim high power-to-weight ratio. One of the main drawbacks of pneumatically actuated soft arms is that their stiffness cannot be varied independently from their end-effector position in space. The novel robot arm physical design presented in this article successfully decouples its end-effector positioning from its stiffness. An experimental characterization of this ability is coupled with a mathematical analysis. The arm combines the light weight, high payload to weight ratio and robustness of pneumatic actuation with the adaptability and versatility of variable stiffness. Light weight is a vital component of the inherent safety approach to physical human-robot interaction. To characterize the arm, a neural network analysis of the curvature of the arm for different input pressures is performed. The curvature-pressure relationship is also characterized experimentally. © Copyright 2018, Mary Ann Liebert, Inc. 2018.

关键词： Stiffness

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Toward End-to-End Control for UAV autonomous Landing via Deep Reinforcement Learning

Toward End-to-End Control for UAV Autonomous Landing via Dee...

引用

International Conference on Unmanned Aircraft systems (ICUAS)

作者： Riccardo Polvara Massimiliano Patacchiola Sanjay Sharma Jian Wan Andrew Manning Robert Sutton Angelo Cangelosi Autonomous Marine System Research Group School of Engineering Plymouth University United Kingdom Centre for Robotics and Neural Systems School of Computing Electronics and Mathematics Plymouth University United Kingdom

The autonomous landing of an unmanned aerial vehicle (UAV) is still an open problem. Previous work focused on the use of hand-crafted geometric features and sensor-data fusion for identifying a fiducial marker and guide the UAV toward it. In this article we propose a method based on deep reinforcement learning that only requires low-resolution images coming from a down looking camera in order to drive the vehicle. The proposed approach is based on a hierarchy of Deep Q-Networks (DQNs) that are used as high-end control policy for the navigation in different phases. We implemented various technical solutions, such as the combination of vanilla and double DQNs trained using a form of prioritized buffer replay that separates experiences in multiple containers. The optimal control policy is learned without any human supervision, providing the agent with a sparse reward feedback indicating the success or failure of the landing. The results show that the quadrotor can autonomously land on a large variety of simulated environments and with relevant noise, proving that the underline DQNs are able to generalise effectively on unseen scenarios. Furthermore, it was proved that in some conditions the network outperformed human pilots.

关键词： Cameras Sensors Drones Kernel Task analysis Global Positioning system

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