Vikram lander and Pragyaan Rover
Chandrayaan-3 has remarkably enhanced its planning, navigation, data analysis, and overall operational efficiency through the utilization of AI-driven technology. These advancements have not only enriched the lunar mission but have also set elevated benchmarks for lunar exploration.
Once the ALS (Automatic Landing Sequence) is initiated, ISRO relinquishes direct control over the Vikram Lander Module. Instead, preset algorithms take command, refined on the fly by AI and ML algorithms, utilizing inputs from the module’s sensors and cameras. It also used all the data previously collected from “Chandrayaan 2”.
Chandrayaan-3 employs a specially designed AI system to oversee the complete 15-minute landing process, encompassing every facet from the lander’s computational functions to its guidance, control, and navigation systems.
As the lander descends from an altitude of 30 km above the moon’s surface to a height of 7.42 km during the initial 11 minutes 30 seconds of the approx. 20-minute landing process, the integrated sensors spring to action, executing intricate calculations.
The descent, which began at 5.45 pm on August 23, Wednesday, was managed by AI software that used instruments like velocimeters and altimeters to calculate the speed and position of the lander above the surface of the moon. The AI-enabled software used the cameras on the orbiter and the lander to detect potential landing sites.
Maneuvers
At the core of Chandrayaan-3’s success lies an astute navigation, guidance, and control system. This intricate nexus of algorithms orchestrates the spacecraft’s movement, steering its trajectory with precision to ensure a secure touchdown. AI’s meticulous planning comprehends every scenario — from altitude adjustments to thruster firings, and surface scans for potential obstacles, all choreographed by AI’s cognitive prowess.
Truly, the crux of Chandrayaan 3 is embodied in its sensors. When dealing with a remotely operated machine, its efficacy hinges on the capacity to ascertain its position, velocity, and heading. A gamut of sensors serves this purpose — among them, velocimeters and altimeters stand out, furnishing essential data about the lander’s speed and altitude.
ISRO harnesses an AI system to oversee the navigation, guidance, and control of the lander, ensuring precise alignment and a gentle landing.
Pragyan, the lunar rover, perpetuates the legacy. AI’s guiding hand directs Pragyan’s navigation as it embarks on a day-long sojourn, conducting experiments and gathering samples. Cameras and antennas, propelled by AI, secure Pragyan’s success in its lunar escapade.
In the boundless expanse of space, Chandrayaan-3’s triumphant touchdown stands as a testament to AI’s prowess and human ingenuity. The symphony of precision and adaptability orchestrated by AI and sensors transforms the moon’s surface into a canvas for India’s technological artistry.
AI’s Multi-Faceted Impact on Chandrayaan-3: From Design to Data Deciphering
AI and Space Exploration
AI is emerging as a pivotal component across numerous industries, including space exploration. Its potential lies in its capacity to sift through colossal volumes of data, discern patterns, and prognosticate. Chandrayaan-3’s case exemplifies the application of artificial intelligence (AI) across various facets of the mission, spanning from spacecraft design to data analysis and decision-making.
The AI-driven design and development of the lander, rover, and the entire spacecraft itself optimize weight, performance, and safety. AI algorithms fine-tune the spacecraft’s design.
AI serves as a valuable tool for ensuring a secure lunar surface landing of the lander. AI algorithms prognosticate topography, identify potential hazards, and deftly maneuver the lander’s descent.
The rover’s exploration of the lunar surface benefits from AI’s prowess. AI algorithms pinpoint and map captivating features, in addition to charting the rover’s trajectory.
AI assumes a pivotal role in the post-mission phase, analyzing the collected spacecraft data. Its algorithms extract insights unattainable through conventional methodologies.