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On 22 July 2019, ISRO launched Chandrayaan 2 spacecraft into an earth orbit as part of the second lunar mission. Geosynchronous Satellite Launch Vehicle Mark-III (GSLV Mk -III) is a three-stage launch vehicle, which carried the spacecraft, comprised of the Orbiter, the Lander Vikram and the Rover Pragyan. The Pragyan Rover was placed strategically among the imperative areas of GSLV Mk-III and carried some key responsibilities/functionalities. 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).
In November 2018, ISRO published a compendium for preparing project proposals by universities, which mentioned about programs and research areas in space such as launch vehicle, satellite communication, earth observations, space sciences, and meteorology. The Vikram Sarabhai Space Centre (VSSC) introduced structural health monitoring technology through classification of strain patterns using artificial neural network (ANN). The technology increases safety and reduces maintenance costs of high-performance composite structures used in aircraft and re-entry vehicles. ANN helps in detection of damages such as fibre failure, matrix cracking, de-laminations, skin-stiffener de-bonds in composite structures. It is used by ISRO to classify sensor malfunctioning and structural failures based on strain patterns of healthy and unhealthy structures. ISRO demanded an analytical study for the adopted methodology in its compendium.
In November 2018, the Capacity Building Programme Office of ISRO introduced Respond Basket which comprised of approximately 150 research areas, out of which 13 are on AI and machine learning. The program is focused on key developments with AI and machine learning such as design and development of ANFIS controller for optimised control in Cryogenic High-Altitude Test facility of ISRO Propulsion Complex, algorithms for earth observation data processing, urban spatial growth modelling, mapping elevation using autonomous UAVs in swarm, object tracking and recognition, use of expert systems, genetic algorithms, NLP, robotics and fuzzy logics in mission operations, remote sensing based forestry and ecology, change prediction and modelling of dynamic natural ecosystem using image processing and remotely sensed data, target identification from MOTR radar data, online spatial data analysis and processing, software framework development, estimation and correction of spacecraft jitter to improve geometric performance of satellite images, and operations research/optimal control.
In June 2018, Antrix Corporation (the commercial arm of ISRO) and SatSure Analytics (a satellite data analytics company which uses big data and machine learning technologies) signed an MoU to increase the penetration of geospatial technology-based services and to develop analytics products in various sectors such as agriculture, banking and financial services, social infrastructure, energy, and telecommunications. The partnership aims to develop synergies through technical advisory services, joint project execution, and national capacity building in the geospatial data analytical industry in India. It will also form a framework for effective collaboration with national space program and new home-grown startups with big data, machine learning, satellite imagery, and remote sensing capabilities.
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. The monitoring system allows experts to 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.
ISRO launched an open data archive geo-portal called ‘Bhuvan,’ which provides visualization services and earth observation data to users in public domain, and offers remote sensing applications. The portal uses a crowdsourcing approach for collecting point-of-interest data and acts as a platform to host government data such as of the forest departments. It allows users to explore 2D and 3D representation of the surface of the earth, pest surveillance, disaster services, high-resolution imagery of cities, etc. States like Punjab, Karnataka, Himachal Pradesh and Andhra Pradesh use Bhuvan for specific applications in forestry, tourism, municipal, GIS, geo-tagging, etc.
The geo-portal is now enriched with 20 ministry portals and 30 state portals and assists government programs such as Integrated Watershed Development Program (Srishti-Drishti), National Mission for Clean Ganga, AMRUT, etc. The portal also enables latest tools and techniques such as data discovery, metadata display, database creation and maintenance, smart city programs, and surveillance and monitoring systems.
The challenge of building Space object tracking solution to build successful sustenance of satellites through 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. Detection of low RCS targets with high speed and complex motions has been receiving a growing attention and significant research efforts in the modern radar field especially in space debris tracking. Real-time implementation long term coherent integration by compensating phase fluctuation among different sampling pulses for high performance embedded computing platforms which involves searching the velocity fold factor, estimating acceleration, jerk values and performing CLEAN algorithm for integrating multiple targets has to be developed.
a. ISRO first developed Target identification using machine learning algorithms from MOTR radar data.
b. Followed by development of “Real time JPDA & MHT based data association in dense multi target tracking environment”.
c. Last critical element was to study, monitor and develop algorithm using historic data for “Space debris RCS estimation and dynamics characterisation from MOTR Space debris”
d. ISRO studied and designed algorithm based on received signal from the target gives us the information of the target like its dynamics spin, its size and RCS. These characteristics of the debris need to be catalogued, to compute its drag coefficient, and its life time assessment
Timeline: 12 to 24 months
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).
a. ISRO leveraged Artificial Neural networks (ANN) which is 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 leaning 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.
b. ISRO followed below approach and module-based solutions to design and develop state of art IIRS solution for future:
Timeline: 24 to 36 months
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 state of technologies to design and develop following:
Timeline: 10 to 12 months