Tiny fragments of space debris pose threats to operating satellites and spacecraft. Researchers are building a platform to track them and predict their movements. Image: NASA
An article published on the ASME (American Society of Mechanical Engineers) website discusses how machine learning can be utilized to track space debris. The author quotes Dr. Moriba Jah, an associate professor of aerospace engineering and engineering mechanics at the University of Texas at Austin, who stresses the significance of tracking space debris. Dr. Jah warns that space debris is becoming an increasingly pressing issue that will compromise our ability to use space in the future.
Dr. Jah further explains that conventional approaches to tracking space debris are insufficient, and machine learning has the potential to significantly enhance our ability to monitor and forecast the movements of objects in space. The author also mentions the work of Dr. Mark Matney, an orbital debris scientist at NASA, who is leading a project to leverage machine learning to track debris in geostationary orbit. Dr. Matney emphasizes the importance of machine learning, stating that “machine learning is going to be essential for helping us stay ahead of the debris problem and protect our valuable space assets.”
NASA data shows debris orbiting Earth. About 95 percent of the objects in the image are space debris. Image: NASA
The article concludes by highlighting the potential of machine learning to address the escalating problem of space debris. Experts in the field are recognizing the importance and necessity of machine learning for the future of space exploration and utilization.
Via The Impactlab