Six AI Contributions Used In The Lunar Mission
India’s Chandrayaan-3 Cracking the Moon’s Code
The moon landing made the moon the hottest real estate in the galaxy, and we have a parking spot reserved on the moon for India!
Chandrayaan-3 is unquestionably a ‘lunar’ achievement for India. India’s space agency, the Indian Space Research Organization (ISRO), out-ranges an exigent job to spin an endless number of plates and to work perpetually to have the Midas touch when it comes to technological advancements in space exploration. With the Chandrayaan-3 mission, ISRO has encompassed the power of Artificial Intelligence (AI) to aggrandize its capabilities for moon landing. This article probes into how AI played a crucial role in enhancing the accuracy and efficiency of various processes, particularly in autonomous landing and image analysis, highlighting some noteworthy examples.
Autonomous Landing with AI
A crucial stumbling block in lunar missions is ensuring and achieving a safe and precise landing. AI algorithms were utilized to implement autonomous landing during Chandrayaan-3 to confirm this. The lander that handled and processed real-time data from numerous sensors, cameras, and other instruments and made pivotal and significant decisions during the descent phase was fitted with advanced AI algorithms and AI-based navigation systems, making necessary adjustments to ensure a successful touchdown. By leveraging AI, the spacecraft could adjust and adapt to unforeseen situations or unexpected circumstances and make instantaneous decisions, thereby enhancing the accuracy of the touchdown operation. Hence, Chandrayaan-3’s success greatly benefits from integrating Artificial Intelligence into its autonomous landing system.
1. Adaptive Thruster Control: Chandrayaan-3’s AI-operated landing system utilizes adaptable and modifiable thruster management, allowing instantaneous adaptations and rapid alterations based on the up-to-the-minute sensor data. This feature was pivotal for countering unforeseeable circumstances, such as sudden deviations of lunar wind or slight landform irregularities. AI-based navigation systems perpetually streamlined the thrust vector to affirm a safe and accurate descent.
2. Risk Mitigation: AI was crucial in Chandrayaan-3’s risk mitigation system. Via the inspection of images obtained from onboard cameras in real-time, AI algorithms spotted likely risks such as substantial boulders or lunar depressions/ cavities. The system then autonomously adjusted the descent trajectory for peril circumvention, ensuring a safe touchdown.
3. Sensor Fusion for Precision Landing: The lander utilized a combination of LiDAR (Light Detection and Ranging), radar, and visual sensors to amass vital knowledge about the lunar topography. AI algorithms substantially contributed to managing this data in real-time and processing or determining the most suitable touchdown point, considering parameters such as landscape, topography, gradient, pitch, potential obstructions, and probable impediments. This sensor confluence technology vastly ameliorated the exactitude of the landing operation.
AI in Image Analysis from Chandrayaan-2
India’s forerunner lunar mission, Chandrayaan-2 confronted challenges during its descent phase. Nevertheless, the task successfully captured excess high-definition images of the lunar terrain. AI played a substantial role in inspecting these images to gather valuable insights and facilitate future missions. Chandrayaan-3 reaped the benefits of AI in analyzing images procured by its forerunner, Chandrayaan-2. The information acquired by Chandrayaan-2’s orbiter and rover underwent detailed scrutiny, utilizing AI algorithms to derive critical information. Here are some examples of AI applications in image analysis:
4. Mineralogical or Elemental Mapping: AI algorithms and AI-based image analysis techniques were utilized to scrutinize the high-definition images procured by Chandrayaan-2’s orbiter. These algorithms used spectral analysis procedures and analyzed the spectral signatures of minerals captured in the pictures to identify and map the distribution of different elements and minerals on the moon’s surface. AI algorithms could easily classify and map their distribution. This data was crucial in ascertaining potential touchdown sites for Chandrayaan-3 based on the lunar soil or regolith composition. This information is significant in the apprehension of the lunar design and possible resources, aiding the utilization of resources and upcoming space exploration.
5. Terrain Mapping and Impact Assessment: AI was instrumental in processing the topographical information gathered via Chandrayaan-2’s rover. Detailed 3D maps of the lunar surface were created using innovative computer vision approaches. The data obtained was pivotal in Chandrayaan-3’s automated descent and mission planning. AI made the inspection of images of impact sites on the moon’s terrain possible. By differentiating between pre- and post-impact pictures and analyzing the resultant ejecta patterns, AI algorithms evaluate the severity of damage caused by meteorite impacts. This analysis can contribute to our insight into lunar geological evolution by understanding the lunar topography dynamics and the frequency of events of meteorite impacts.
6. Impact Crater Identification: Chandrayaan- Making waves, one crater at a time! AI algorithms played a crucial role in scrutinizing the images from Chandrayaan-2’s orbiter to classify and identify impact craters on the lunar terrain. The selection process for Chandrayaan 3’s landing site was facilitated by this obtained information, guaranteeing a scientifically valuable and secure landing. AI algorithms were utilized to detect and analyze craters on the moon’s surface. By leveraging AI algorithms, it was possible to identify and classify craters according to their sizes, types, and characteristics. Examining these ejecta patterns has proven invaluable in understanding the moon’s geological history and determining potential touchdown sites for future space missions.
Unlocking Secrets of the Moon with AI
India’s Chandrayaan-3: Pioneering the Next Lunar Odyssey Serves as a testament to India’s unwavering commitment and remarkable technological advancements in space exploration. The successful integration of AI in automated descent and image analysis played a pivotal role in the mission’s success. The infusion of AI technology in India’s Chandrayaan-3 mission exemplifies the nation’s dedication to extending the frontiers of space exploration. By utilizing automated decision-making tools, India continues to broaden the horizons of space exploration, setting a benchmark for future interplanetary and lunar endeavors. By leveraging AI for image analysis and autonomous landing, ISRO has enhanced its accuracy in descent or landing and obtained valuable knowledge about the lunar terrain. These strides constitute the framework for the upcoming lunar missions, empowering India to contribute to the global scientific community’s potential for future space exploration, making the Moon Our Next-door Neighbor!
