Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. This paper evaluates a number of deep-learning algorithms preforming the task of visually detecting trash in realistic underwater environments, with the eventual goal of exploration, mapping, and extraction of such debris by using AUVs. A large and publicly-available dataset of actual debris in open-water locations is annotated for training a number of convolutional neural network architectures for object detection. The trained networks are then evaluated on a set of images from other portions of that dataset, providing insight into approaches for developing the detection capabilities of an AUV for underwater trash removal. In addition, the evaluation is performed on three different platforms of varying processing power, which serves to assess these algorithms' fitness for real-time applications.
Design and Development of a Robotic Vehicle for Shallow-Water Marine Inspections
Centre for Automation and Robotic Engineering Science, University of Auckland（奥克兰大学）；School of Science and Engineering, Ritsumeikan University（立命馆大学）；University of Canterbury（坎特伯雷大学）
Autonomous Oil Spill Response Through Liquid Neural Trajectory Modeling and Coordinated Marine Robotics
Swarm \& AI Lab (SAIL), Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, 199 Aba Khoushy Ave, Mount Carmel, Haifa 3498838, Israel
MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics
The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University（浙江大学流体动力与机电系统国家重点实验室）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Department of Mechanical and Aerospace Engineering, the Hong Kong University of Science and Technology（香港科技大学机械与航空航天工程系）
Results from the Robocademy ITN: Autonomy, Disturbance Rejection and Perception for Advanced Marine Robotics
Heriot-Watt University（赫瑞-瓦特大学）；DFKI（德国人工智能研究中心）；National Oceanography Centre（英国国家海洋学中心）；National Technical University of Athens（雅典国立技术大学）；Tallin University of Technology（塔林理工大学）；Atlas Elektronik GmbH（阿特拉斯电子有限公司）；University of Girona（赫罗纳大学）；SeeByte（SeeByte公司）
海洋和水下资源是许多国家经济的重要组成部分。这需要大量的财政资源用于其建设和维护。机器人技术有望填补这一空白，通过自动化和/或将人类从恶劣的环境中移除，以便轻松执行维护任务。Robocademy Marie Sklodowska-Curie初始培训网络由欧盟FP 7研究计划资助，旨在训练13名研究员成为海洋和水下机器人领域的世界领先研究人员。研究员将指导性研究发展到三个关键领域：自主性，干扰拒绝和感知。本文概述了研究员在这三个行动方针方面的研究。71份科学出版物是该项目的主要成果，目前还有许多其他出版物正在筹备中。大多数研究员在欧洲找到了工作，这表明对这类专家的需求很大。我们相信，该项目的成果已经对海洋机器人行业产生了影响，因为关键技术已经被采用。
Framework for Robust Motion Planning of Tethered Multi-Robot Systems in Marine Environments
School of Engineering & Physical Sciences, Heriot-Watt University（工程与物理科学学院，赫瑞斯泰特大学）；School of Mathematics & Computer Sciences, Heriot-Watt University（数学与计算机科学学院，赫瑞斯泰特大学）
A Robotic Approach towards Quantifying Epipelagic Bound Plastic Using Deep Visual Models
California State University, Monterey Bay（加州州立大学蒙特雷湾分校）；The Jackson Laboratory（杰克逊实验室）；Scripps Institution of Oceanography（斯克里普斯海洋研究所）；The Ocean Cleanup Foundation（海洋清理基金会）；Hawaii Pacific University（夏威夷太平洋大学）
ICM-CSIC（西班牙加泰罗尼亚海洋科学研究所）；Khalifa University（哈利法大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；KTH Royal Institute of Technology（皇家理工学院）；Scuola Superiore Sant’Anna（圣安娜高等学院）
An Aerial Transport System in Marine GNSS-Denied Environment
School of Aerospace Engineering（航空航天学院）；Beijing Institute of Technology（北京理工大学）；Centre for Autonomous Robotic Systems（自主机器人系统中心）；Khalifa University（哈利法大学）
Faculty of Computer and Information Science, University of Ljubljana（卢布尔雅那大学计算机与信息科学学院）；Faculty of Maritime Studies and Transport, University of Ljubljana（卢布尔雅那大学海事研究与运输学院）；OZON Research Group, TIMTEC（TIMTEC公司OZON研究组）；MAT Systemics, University of California, Santa Barbara（加州大学圣塔芭芭拉分校MAT系统学）
AquaticVision: Benchmarking Visual SLAM in Underwater Environment with Events and Frames
School of Data Science, The Chinese University of Hong Kong, Shenzhen, P. R. China（数据科学学院，香港中文大学（深圳））；School of Life Sciences, The Chinese University of Hong Kong, Hongkong（生命科学学院，香港中文大学）
Towards Real-Time Advancement of Underwater Visual Quality with GAN
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University（北京大学工程科学与先进技术北京创新中心）；Beihang University（北京航空航天大学）
Towards Automated Sample Collection and Return in Extreme Underwater Environments
University of Michigan（密歇根大学）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；University of Sydney（悉尼大学）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）
Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery
University of Southampton（索姆塞特大学）；IIS, The University of Tokyo（东京大学信息研究所）；Voyis Imaging Inc.（voyis影像公司）；University of Southampton, UK IIS, The University of Tokyo, Japan（索姆塞特大学 英国 IIS，东京大学，日本）
