How ISRO uses Machine learning?

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What is AI?

Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans.AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Huge historical data is feed to the machine learning model and by that machine learning does the prediction.

How ISRO uses Artificial Intelligence and Machine Learning?

➤Chandrayaan 2: AI-powered ‘Pragyan’ Rover

• On 22 July 2019, ISRO launched Chandrayaan 2 spacecraft into an earth orbit as part of the second lunar mission.

• In Chandrayaan 2, the Pragyan rover was AI-powered which can communicate only with the Lander, includes a piece of motion technology developed by IIT-Kanpur researchers that will help the rover manoeuvre on the surface of the moon and aid in landing. The algorithm will help the rover trace water and other minerals on the lunar surface, and also send pictures for research and examination.
• The rover is a six-wheeled robotic vehicle and is capable of conduct in-situ payload experiments. It is powered by AI tools and frameworks, uses solar energy for its functioning, and can communicate only with the Lander. The Pragyan Rover payloads consist of Alpha Particle X-ray Spectrometer (APXS) and Laser Induced Breakdown Spectroscope (LIBS).

Chandrayaan-2 Pragyan shows how AI is helping space exploration.

➤Multi Object Tracking Radar (SDSC-SHAR)

• The challenge of building a Space object tracking solution to build successful sustenance of satellites through the difficult terrain of open space with millions of unknown objects that could impact every ISRO sponsored mission.

• The objective is to build Multi Object Tracking Radar. ISRO first developed Target identification using machine learning algorithms from MOTR radar data.
• Radar data consists of Range, Azimuth, Elevation and Signal to Noise Ratio (SNR). From Range and SNR correlation target size can be classified. From SNR variation alone in a single-track duration, target nature can be established. Using Machine Learning algorithms, a model should be trained on radar tracked data (Range, Azimuth, Elevation and SNR). The trained model should identify a target nature (controlled or uncontrolled) and size. Using standard libraries in Python Machine Learning Algorithms have become realizable models.

➤Image Processing and Pattern Recognition (IIRS)

In 1980s and 1990s ISRO’s the challenge was to build efficient and cost neutral image processing and pattern recognition solution for upcoming missions for next decade. Hence, Unmanned Image Processing and Pattern Recognition (IIRS).

• ISRO leveraged Artificial Neural networks (ANN) which is the generic name for a large class of machine learning algorithms, most of them are trained with an algorithm called back propagation. ISRO’s team used various path to explore various deep learning algorithms in various applications of earth observation data like; self-learning based classification, prediction, multi-sensor temporal data in crop/forest species identification, remote sensing time series data analysis.

➤AI-enabled monitoring system for forest conservation

• The National Remote Sensing Centre (NRSC), which ISRO has designed and developed, is a monitoring system to observe forest cover change and combat deforestation by leveraging optical remote sensing, geographic information system, AI, and automation technologies.

• ISRO creates a machine learning model that checks the imagery and detect small-scale deforestation and improve the frequency of reporting.
• It also enables scientists to process satellite imagery faster and reduces the time frame for new reports from one year to one month. NSRC aims at preventing negative changes in the green cover and protection of wildlife.
• The NRSC technology makes it possible for monitoring forest cover changes over small areas of one hectare by improving the resolution from 50 meters to 30 meters through optical remote sensing which provides insights into the smallest of deforestation activity.

➤Autonomously Navigating Robot for Space Mission (IISU)

• ISRO’s challenge was to build and send unmanned robots to help fetch critical space information in multiple missions throughout the year.

• A half Vyomnoid with Sensing and perception of surroundings with 3D vision and Dexterous manipulative abilities to carry out defined crew functions in an unmanned mission or assist crew in manned missions.
• Design & Realization of FULL Vyomnoid with features that include full autonomy with 3D vision, dynamically controlled movement in zero ‘g’, Artificial Intelligence / Machine Learning enabled real time decision making with vision optimization and path planning algorithms.
• ISRO leveraged Artificial Intelligence enabled Path Navigation algorithms to solve this.

➤MORE:

• SRO has advised scientists and researchers to focus on building a generalized parameter extraction software based on artificial neural network (ANN) learning methods that utilize multidimensional approximation of ANN to map characteristics of microwave filters. A communication satellite contains a large number of microwave filters that are required to undergo extensive tuning after fabrication.
• ISRO is currently planning to develop high-end propulsion technology to ensure cost-effective re-usable, recoverable, re-startable and reliable space launches with AI-based sensors equipped in propellants.

Sources:

• ISRO Research paper: (open)
• indiaai.gov.in (open)
• Mint.com (open)
• un-spider.org. (open)
• sac.gov.in (open)

So this was an Overview of How ISRO is solving problems in every way possible because of AI and how our lives are evolving and getting better day by day with the help of AI, I hope you found this blog helpful….Your feedback is valuable and it will help me improve… clap if you like it.