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 | Tasmanian Government

#picknik.ai | Remote Robot Control

#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

#Biral | Sensors for Antarctic Climate Change Research

#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

#Jetson Orin Modules | NVIDIA | Advanced robotics and edge AI applications

#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

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

#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