antarcticocean.ai

#Antarctic Ocean AI | Artificial Intelligence for Antarctic Ocean

#Australian Antarctic Program | University of Tasmania | CSIRO | Institute for Marine and Antarctic Studies | Bureau of Meteorology, Australian Antarctic Division | Geoscience Australia | Integrated Marine Observing System | Million year ice core project | Tasmanian Government

#picknik.ai | Remote Robot Control | Boulder, Colorado, USA

#Australian Antarctic Program Partnership (AAPP) | Simulating amount of energy from sun that strikes Southern Ocean | Understanding of how much clouds absorb and scatter sunlight | Measuring cloud properties | Shortwave (sunlight) radiation reflection depends on height, layering and optical properties of clouds | Machine learning used to teach models how to predict amount of sunlight entering Southern Ocean | Built models based on training data

#SEA.AI | Detecting floating objects early | Using thermal and optical cameras to catch also objects escaping conventional ­systems such as Radar or AIS: Unsignalled crafts or other floating obstacles, e.g., containers, tree trunks, buoys, inflatables, kayaks, persons over board | System computes input from lowlight and thermal cameras, using Machine Vision technology, deep learning capabilities and proprietary database of millions of annotated marine objects | High-resolution lowlight and thermal cameras | Real-time learning of water surface patterns | Searching for anomalies | Distinguishing water from non-water | Comparing anomalies with neural network | Recognize objects by matching combination of filters | Augmented reality video stream combined with map view | Intelligent alarming based on threat level | Detecting persons in water | On-board cameras with integrated image processing | Providing digital understanding of vessel surroundings on water | SEA.AI App on smartphone or tablet

#Sea Machines | Artificial Intelligence Recognition and Identification System | Detects, tracks, classifies and geolocates objects, vessel traffic and other potential obstacles | Boston, USA

#Biral | Sensors for Antarctic Climate Change Research | Bristol Industrial & Research Associates Ltd | Unit 8 Harbour Road Trading Estate, Portishead, Bristol BS20 7BL UK

#ICEYE | Synthetic aperture radar (SAR) | Maritime monitoring

#Advanced Navigation | AI-based marine navigation systems | AI-Based underwater navigation solutions and robotics technology | Hydrography | Underwater acoustic positioning solutions | Autonomous Underwater Vehicle (AUV) | Inertial navigation systems (INS)

#Ocean Infinity | Robotic ships | Smaller uncrewed vessels | Underwater robotics

#Blue Atlas Robotics | Manufactures subsea inspection robots and provides marine survey solutions

#NVIDIA | AIoT Platform | NVIDIA Jetson Orin Nano | 40 TOPS of AI performance | Power options between 7W and 15W | Platform runs image segmentation model (SegFormer) | Model trained on eight NVIDIA A100 Tensor Core GPUs using NVIDIA TAO AutoML | AIoT Platform crunches data at edge transmitting only results

#Avikus | Autonomous navigation solutions for vessels

#Robotics Engineering | Intelligent Sensing for Object Recognition, Manipulation and Control | Design, Development and Simulation Tools for Robotics Development | Developing Intelligent Robots - Machine Learning on Edge, Cloud and Hybrid Architectures | Advanced Motion Control Solutions for Robotics Systems | Intelligent Vision and Sensing Solutions for Autonomous Mapping and Navigation | Motion Control for Healthcare Robotics Applications: Functional Requirements, Critical Capabilities

#Howell Marine Consulting (HMC) | Blue economy planning and strategy | Offshore energy | Natural capital | Ocean climate | Fisheries management | Equitable transitions | Clients: Defra, Crown Estate, Natural England, Marine Management Organisation, UNEP, UNDP, World Bank, NERC, Welsh Government, Scottish Government, UNESCO IOC, Offshore Wind Industry Council | Delivering marine science into operational decision making

#Natural Environment Research Council | Organic polar and non-polar compounds analyses

#Ommatidia Lidar | Ocean observation | 3D Light Sensor | In-orbit characterization of large deployable reflectors (LDRs) | Channels: 128 parallel | Imaging vibrometry functionality | Target accuracy: 10µm | Measurement range: 0.5-20 m | Measurement accuracy (MPE): 20 + 6 μ/m | Angular range 30 x 360 | Vibrometry sampling frequenvy: 40 kHz | Vibrometry max in-band velocity: 15.5 mm/s | Power consumption: 45W | Battery operation time: 240 min | Interface: Ethernet | Format: CSV / VKT / STL / PLY / TXT | Dimension: 150x228x382 mm | Weight: 7,5 kg | Pointer: ~633 nm | Temperature range: 0/40 ºC | Environmental protection class: IP54 | Eye safety: Class 1M | Raw point clouds: over 1 million points | Calibration: metrology-grade with compensation of thermal and atmospheric effects | ESA

#OndoSense | Radar distance sensor | Sensor software: integrated into control system or used for independent quality monitoring | Object detection | Distance measurement | Position control | Agriculture: reliable height control of the field sprayer | Mining industry | Transport & Logistics | Shipping & Offshore | Mechanical and plant engineering | Metal and steel industry | Energy sector | Harsh industrial environments | Dust & smoke: no influence | Rain & snow: no influence | Radar frequency: 122GHz | Opening angle: ±3° | Measuring range: 0.3 – 40 m | Measuring rate: up to 100Hz | Output rate: up to 10 ms / 100 Hz | Measurement accuracy; up to ±1mm | Measurement precision: ±1mm | Communication protocol: RS485; Profinet, other interfaces via gateway | Switching output: 3x push-pull (PNP/NPN) | Analogue output: Current interface (4 – 20 mA) | Protection class: IP67

#Heliogen | AI-controlled concentrating solar thermal technology | AI, cameras, advanced computer vision software precisely aligni array of small mirrors reflecting and concentrating sunlight on receiver tower | Receiver generates heat which is transferred to thermal energy storage | Providing steam heat up to 300 °C | Cameras installed at top of tower measure color intensity of sky as reflected in mirrors | By comparing intensities as seen from multiple cameras, system calculates mirror orientation and direction of beam, for real-time hyper-accurate tracking | AI technology for continuous micro-adjustments | System automatically adapts to atmospheric conditions | WiFi connects heliostats | Direct Steam Generating Receivers (DSGR) absorb concentrated sunlight and transmit energy to pressurized water within metal tubes | Manufacturing facility in Long Beach, California

#British Antarctic Survey Artificial Intelligence (AI) Lab | Ice forecasting | Ice dynamics | Polar operations | Tracking icebergs from space | Benthic biology on seafloor | Integrating different types of data using AI | Optimising data collection processes in remote and hostile environments | Autonomous marine vehicles

#Fincantieri | Polar research vessel manufacturing

#Aker Arctic Technology | Designing and Engineering reliable and efficient ships operating in ice-covered waters | Polar research vessels | Complete development process of a new ship design | Computational Fluid Dynamics (CFD) analysis (numerical analysis and algorithms to analyze and solve problems involving fluid flows) | Finite Element Method (FEM) analysis for ship structures | Ship propulsion systems | Winterization specifications and solutions

#LookOut | AI vision system | Synthesized data from charts, AIS, computer vision, and cloud fusing it into one 3D augmented reality view | Connects to existing boat display | Mountable camera system to the top of any boat | Lookout App for laptop, phone or tablet | Infrared vision | Night vision sensor | Spotting small vessels, floating debris, buoys, people in water | Blind spot detection | Backup camera | Temperature breaks, bird cluster locations, underwater structures for anglers | Camera streaming over WiFi to phones and tablets on the boat | Over-the-air (OTA) updates | Marine-grade water-proof enclosure | Integrated with satellite compass | National Marine Electronics Association (NMEA) communication standard interface | Multifunction Display (MFD) | Multi-core CPU driving augmented reality compute stack | ClearCloud service | NVIDIA RTX GPU for real-time computer vision | DockWa app

#SiLC | Machine Vision solutions with FMCW LiDAR vision | FMCW at the 1550nm wavelength | Eyeonic Vision Sensor platform | Detecting vehicles and various obstacles from long distances | Honda Xcelerator Ventures | Honda Marine

#HEBI Robotics | Robot development platform | Smart robotic actuation hardware and building blocks | Streamlininh the process of developing robots | Space-rated hardware deployed for missions in space | NASA: SBIR

#National Technical University of Athens | MariNeXt deep-learning framework detecting and identifying marine pollution | Sentinel-2 imagery | Detecting marine debris and oil spills on sea surface | Automated data collection and analysis across large spatial and temporal scales | Deep learning framework | Data augmentation techniques | Multi-scale convolutional attention network | Marine Debris and Oil Spill (MADOS) dataset | cuDNN-accelerated PyTorch framework | NVIDIA RTX A5000 GPUs | NVIDIA Academic Hardware Grant Program | AI framework produced promising predictive maps | Shortcomings: unbalanced dataset, marine water and oil spills are abundant, foam and natural organic material are less represented

#Yamaha Marine | 450 hp hydrogen-powered V-8 outboard | Three 6-foot-long cylindrical-shaped hydrogen fuel tanks | H2 machine operates by using hydrogen in its combustion chambers | H2 tanks are positioned low and centrally to enhance stability | H2 tanks size demands rethinking of future boat designs, hulls specifically tailored for hydrogen storage | Hydrogen storage system adds considerable weight to vessel | Volumetric energy density of hydrogen is lower, requiring larger tanks | Partners: Roush Performance, Regulator Marine

#Securing Antarctica Environmental Future (SAEF) | Funded by Australian Research Council | Developed autonomous year-round monitoring platform to measure and analyze moss health | Artificial Intelligence of Things Platform (AIoT Platform) | NVIDIA Jetson Orin Nano | Sensors collect and analyze moss canopy and air temperature, relative humidity, soil moisture and heat flux, solar radiation, and imagery | AIoT Platform transmits only results

#Intergovernmental Negotiating Committee (INC-5) | Developing international legally binding instrument on plastic pollution | Raising awareness about the serious impacts of plastic pollution on both humans and nature | Global bans and phase-outs of the most harmful and problematic plastic products and chemicals | Global product design requirements to ensure all plastic produced is safe to reuse and recycle as part of global non-toxic circular economy

#Tampere University | Pneumatic touchpad | Soft touchpad sensing force, area and location of contact without electricity | Device utilises pneumatic channels | Can be used in environments such as MRI machines | Soft robots | Rehabilitation aids | Touchpad does not need electricity | It uses pneumatic channels embedded in the device for detection | Made entirely of soft silicone | 32 channels that adapt to touch | Precise enough to recognise handwritten letters | Recognizes multiple simultaneous touches | Ideal for use in devices such as MRI machines | If cancer tumours are found during MRI scan, pneumatic robot can take biopsy while patient is being scanned | Pneumatic device can be used in strong radiation or conditions where even small spark of electricity would cause serious hazard

#BrainChip | Akida Pico | Ultra-low power acceleration co-processor | Enabling development of uber-compact, intelligent devices | Akida2 event-based computing platform | Ultra-low-power (less than a milliwatt) neural processing unit (NPU) | AI accelerator for battery powered, compact intelligent devices (hearing aids, noise-cancelling earbuds, medical equipment) | Event-based co-processor | Intended for voice wake detection, keyword spotting, speech noise reduction, audio enhancement, presence detection, personal voice assistant, automatic doorbell, wearable AI and appliance voice interfaces | Supports power islands for minimal standby power

#Allen Institute for Artifical Intelligence | AI for the Environment | Robot planning precise action points to perform tasks accurately and reliably | Vision Language Model (VLM) controlling robot behavior | Introducing automatic synthetic data generation pipeline | Instruction-tuning VLM to robotic domains and needs | Predicting image keypoint affordances given language instructions | RGB image rendered from procedurally generated 3D scene | Computing spatial relations from camera perspective | Generating affordances by sampling points within object masks and object-surface intersections | Instruction-point pairs fine-tune language model | RoboPoint predicts 2D action points from image and instruction, which are projected into 3D using depth map | Robot navigates to these 3D targets with motion planner | Combining object and space reference data with VQA and object detection data | Leveraging spatial reasoning, object detection, and affordance prediction from diverse sources | Enabling to generalize combinatorially.| Synthetic dataset used to teach RoboPoint relational object reference and free space reference | Red and ground boxes as visual prompts to indicate reference objects | Cyan dots as visualized ground truth | NVIDIA | | Universidad Catolica San Pablo | University of Washington