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
"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
