"A human brain has many capabilities: talking, writing, etcetera," Tsukamoto said. "Deep learning creates a brain specialized for one of these capabilities with a domain-specific knowledge. In this case, Neural-Rendezvous learns all the information it needs to encounter an ISO, while also considering the safety-critical, high-cost nature of space exploration."
Tsukamoto said Neural-Rendezvous is based on contraction theory for data-driven nonlinear control systems, which he developed for his Ph.D. at Caltech, while this project was a collaboration with NASA's Jet Propulsion Laboratory, where he spent his time as a post-doctoral research affiliate.
"Our key contribution is not just in designing the specialized brain, but in proving mathematically that it works. For example, with a human brain we learn from experience how to navigate safely while driving. But what are the mathematics behind it? How do we know and how can we make sure we won't hit anyone?"
In space, Neural-Rendezvous autonomously predicts a spacecraft's best action, based on data, but with a formal probabilistic bound on its distance to the target ISO.
Tsukamoto said there are two main challenges: the interstellar object is a high-energy, high-speed target, and its trajectory is always poorly constrained due to the unpredictable nature of its visit.
"We're trying to encounter an astronomical object that streaks through our solar system just once and we don't want to miss the opportunity. Even though we can approximate the dynamics of ISOs ahead of time, they still come with large state uncertainty because we cannot predict the timing of their visit. That's a challenge."
The speed and uncertainty of ISO encounters are also why the spacecraft must be able to think on its own.
"Unlike traditional approaches in which you design almost everything before you launch a spacecraft, to encounter an ISO, a spacecraft has to have something like a human brain, specifically designed for this mission, to fully respond to data onboard in real time."
Tsukamoto also demonstrated Neural-Rendezvous using multi-spacecraft simulators called M-STAR and tiny drones called Crazyflies.
While he was at JPL, two Illinois aerospace undergraduate students, Arna Bhardwaj and Shishir Bhatta, contacted him to work on a research project using Neural-Rendezvous.
"Because of the speed and uncertainty, it's challenging to obtain a clear view of an ISO during a flyby with 100 percent accuracy, even with Neural-Rendezvous. Arna and Shishir wanted to show that Neural-Rendezvous could benefit from a multi-spacecraft concept."
To theoretically justify the empirical observations from the M-STAR and Crazyfly demonstrations, their research looked at how to mathematically maximize the information gathered from the ISO encounter using a swarm of spacecraft.
"Now we have an additional layer of decision-making during the ISO encounter," Tsukamoto said. "How do you optimally position multiple spacecraft to maximize the information you can get out of it? Their solution was to distribute the spacecraft to visually cover the highly probable region of the ISO's position, which is driven by Neural-Rendezvous."
Tsukamoto said he was impressed with the level of dedication and academic potential demonstrated by Bhardwaj and Bhatta.
"The topics explored in Neural-Rendezvous can be advanced even for Ph.D. students. Arna and Shishir were very productive and worked hard, and I was surprised to see them publish a paper, given this field initially was entirely new to them. They did a great job.
"And while the Neural-Rendezvous is more of a theoretical concept, their work is our first attempt to make it much more useful, more practical."
Research Report:Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
Research Report:Information-Optimal Multi-Spacecraft Positioning for Interstellar Object Exploration
Related Links
Grainger College of Engineering
Stellar Chemistry, The Universe And All Within It
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