By Girish Linganna

Apr 17: Chandrayaan-3 was launched on July 14, 2023, and it reached the moon's orbit on August 5. Then, on August 23, the lander successfully touched down on the South Pole of the moon. This achievement made India the fourth nation, following Russia, the USA, and China, to accomplish a successful moon landing. Notably, India became the first country ever to land on the moon's South Pole.

Artificial intelligence has been a pivotal technology in ensuring the success of the Chandrayaan-3 mission. It has significantly enhanced planning, navigation, data analysis, and overall operational efficiency. The integration of artificial intelligence has played a crucial role in the accomplishments of Chandrayaan-3. Let's explore how artificial intelligence has contributed to the mission's triumph.

Numerous algorithms were utilized in the design and development of the Chandrayaan-3 mission vehicle, as is customary in space exploration endeavors. Artificial intelligence played a vital role in the creation of these algorithms and the subsequent design and development of key components such as the Vikram lander and the Pragyan rover. The entire Chandrayaan-3 mission was optimized for weight, performance, and safety through the implementation of algorithms driven by artificial intelligence. These AI-powered algorithms were instrumental in achieving an efficient and secure design for the Chandrayaan-3, contributing to its successful completion.

Artificial intelligence played a crucial role in ensuring the safe landing of Chandrayaan-3 at the South Pole of the moon. By leveraging AI, the ISRO team was able to predict the lunar topography, identify potential hazards, and effectively manage the descent of the Vikram lander. AI technology improved the team's control over attitude and thrust, significantly reducing the risk of landing failure. Furthermore, the integration of AI led to an enhanced attitude correction rate and improved impact legs, both of which greatly contributed to the successful landing of the spacecraft. Additionally, artificial intelligence was employed to analyze data and images from Chandrayaan-2's orbiter, providing more precise calculations for determining the landing site.

Chandrayaan-3 is equipped with AI-powered sensors such as velocimeters, altimeters, and accelerometers, among others. These sensors enable the lander to achieve a precise landing by mapping the lunar topography and adjusting the descent trajectory based on real-time conditions on the moon's surface. This capability allows the lander to adapt to the changing variables encountered during the descent. Additionally, the Chandrayaan-3 features the Lander Horizontal Velocity Camera (LHVC), which utilizes artificial intelligence to smoothly transition the spacecraft from a horizontal to a vertical orientation during descent.

Lunar Terrain Mapping: Algorithms and Sensors for Safe Navigation and Landing

When it comes to landing on the Moon, terrain mapping is crucial for ensuring a safe touchdown. This process involves utilizing a combination of sensors and algorithms. Commonly employed algorithms and techniques include:

a. Lidar, which stands for Light Detection and Ranging, is a technology that uses laser beams to measure distances and create detailed 3D maps of the lunar surface. By emitting laser pulses and measuring the time it takes for the light to reflect back, Lidar sensors provide valuable data for terrain mapping during lunar landings.

b. Radar, which stands for Radio Detection and Ranging, is a technology capable of penetrating the lunar surface to gather data about the composition and depth of the terrain.

c. Visual odometry is an algorithm that utilizes images taken by cameras on the lunar lander to estimate its position and movement in relation to the lunar surface. It relies on techniques such as stereo vision and structure-from-motion to achieve accurate estimations.

d. Inertial navigation involves utilizing accelerometers and gyroscopes to measure the lander's acceleration and rotation. This data is then used to estimate the lander's position and orientation in relation to the lunar surface.

e. Simultaneous Localization and Mapping (SLAM) algorithms blend sensor data with motion models to create a map of the terrain while also determining the lander's position within that map. By combining these two processes, SLAM enables the lander to navigate and understand its surroundings in real-time.

f. Terrain classification involves using machine learning algorithms to classify various types of terrain, such as rocks, craters, and flat areas. This classification is based on sensor data, providing valuable information for the lander to select a safe landing site.

g. Hazard detection and avoidance algorithms are employed to identify potential dangers, such as boulders or steep slopes, and make instant decisions to steer the lander away from them, ensuring a secure landing.

h. Trajectory planning algorithms calculate the most optimal descent path and landing site, considering factors like fuel consumption and safety margins. These calculations enable the lander to navigate and land in the most efficient and secure manner possible.

i. Autonomous landing involves the utilization of algorithms that take charge of the lander's descent and landing sequence. These algorithms operate independently and make real-time adjustments to ensure a secure touchdown on the lunar surface.

By working together, these algorithms enable a lunar lander to accurately map and navigate the lunar terrain, effectively avoid obstacles, and achieve a safe landing on the surface of the Moon.

Collaborative Progress : Humans and AI

In addition, artificial intelligence plays a crucial role in analyzing the data collected by Chandrayaan-3. By leveraging AI, scientists and experts can extract valuable insights from the gathered data, which would be challenging using traditional analysis methods alone. The AI-powered instruments on the lander and rover are capable of capturing and analyzing various aspects of the lunar surface, including temperature experiments, measurements of seismic activity, estimation of plasma density, analysis of chemical and mineral composition, and even examination of elemental composition. AI's contribution in data analysis significantly enhances our understanding of the lunar environment and its characteristics.

The AI algorithms utilized in the Chandrayaan-3 mission were created through a collaboration between the Indian Space Research Organisation (ISRO) and the Indian Institute of Technology Madras. These algorithms were trained using a dataset consisting of images and terrain data specifically gathered from the Moon.

The information presented clearly highlights the growing collaboration between human creativity and machine intelligence. It showcases India's remarkable technological progress and its unwavering commitment to space exploration. These are truly fascinating times, as humans and machines join forces to create something extraordinary and unprecedented. Importantly, the goal is not to replace one with the other, but rather to achieve harmonious coexistence that leads to the greatest achievements and advancements.

(The author Girish Linganna of this article is a Defence, Aerospace & Political Analyst based in Bengaluru. He is also Director of ADD Engineering Components, India, Pvt. Ltd, a subsidiary of ADD Engineering GmbH, Germany. You can reach out to him at: girishlinganna@gmail.com)