Machine Learning Helps Robot Swarms Coordinate
07-14-20
Soon-Jo Chung, Bren Professor of Aerospace, Yisong Yue, Professor of Computing and Mathematical Sciences, postdoctoral scholar Wolfgang Hönig, and graduate students Benjamin Rivière and Guanya Shi, have designed a new data-driven method to control the movement of multiple robots through cluttered, unmapped spaces, so they do not run into one another. "Our work shows some promising results to overcome the safety, robustness, and scalability issues of conventional black-box artificial intelligence (AI) approaches for swarm motion planning with GLAS and close-proximity control for multiple drones using Neural-Swarm," says Chung. [Caltech story]
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GALCIT
CMS
Yisong Yue
CNS
Soon-Jo Chung
postdocs
Benjamin Rivière
Guanya Shi
Wolfgang Hönig
Daniel Neamati Receives 2020 Henry Ford II Scholar Award
06-08-20
Daniel Neamati is a recipient of the 2020 Henry Ford II Scholar Award. Daniel’s interests sit at the cross-section of mechanical engineering, aerospace engineering, and planetary science. Daniel's research includes modern computational techniques in microfluidic analyses, and he has contributed to JPL projects such as the Europa Lander and Mars 2020. In the near future, Daniel plans to conduct a SURF at Stanford, and a senior thesis with Soon-Jo Chung, Bren Professor of Aerospace; Jet Propulsion Laboratory Research Scientist, in the Aerospace Robotics and Control Laboratory. Thereafter, Daniel plans to pursue a Ph.D. in control systems in aerospace engineering. The Henry Ford II Scholar Award is funded under an endowment provided by the Ford Motor Company Fund. The award is made annually to engineering students with the best academic record at the end of the third year of undergraduate study.
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Henry Ford II Scholar Award
Soon-Jo Chung
Daniel Neamati
"Neural Lander" Uses AI to Land Drones Smoothly
05-23-19
Professors Chung, Anandkumar, and Yue have teamed up to develop a system that uses a deep neural network to help autonomous drones "learn" how to land more safely and quickly, while gobbling up less power. The system they have created, dubbed the "Neural Lander," is a learning-based controller that tracks the position and speed of the drone, and modifies its landing trajectory and rotor speed accordingly to achieve the smoothest possible landing. The new system could prove crucial to projects currently under development at CAST, including an autonomous medical transport that could land in difficult-to-reach locations (such as a gridlocked traffic). "The importance of being able to land swiftly and smoothly when transporting an injured individual cannot be overstated," says Professor Gharib who is the director of CAST; and one of the lead researchers of the air ambulance project. [Caltech story]
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Morteza Gharib
Yisong Yue
Soon-Jo Chung
Animashree Anandkumar
Engineers Taught a Drone to Herd Birds Away From Airports
08-08-18
Soon-Jo Chung, Associate Professor of Aerospace and Bren Scholar; Jet Propulsion Laboratory Research Scientist, and colleagues have developed a new control algorithm that enables a single drone to herd an entire flock of birds away from the airspace of an airport. The effectiveness of the algorithm is only limited by the number and size of the incoming birds, Professor Chung says, adding that the team plans to explore ways to scale the project up for multiple drones dealing with multiple flocks. [Caltech story]
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Soon-Jo Chung
