Machine Learning Helps Robot Swarms Coordinate

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Engineers at Caltech 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.

Multi-robot motion coordination is a fundamental robotics problem with wide-ranging applications that range from urban search and rescue to the control of fleets of self-driving cars to formation-flying in cluttered environments. Two key challenges make multi-robot coordination difficult: first, robots moving in new environments must make split-second decisions about their trajectories despite having incomplete data about their future path; second, the presence of larger numbers of robots in an environment makes their interactions increasingly complex (and more prone to collisions).

To overcome these challenges, Soon-Jo Chung , Bren Professor of Aerospace, and Yisong Yue , professor of computing and mathematical sciences, along with Caltech graduate student Benjamin Rivière (MS '18), postdoctoral scholar Wolfgang Hönig, and graduate student Guanya Shi, developed a multi-robot motion-planning algorithm called "Global-to-Local Safe Autonomy Synthesis," or GLAS, which imitates a complete-information planner with only local information, and "Neural-Swarm," a swarm-tracking controller augmented to learn complex aerodynamic interactions in close-proximity flight.

"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.

When GLAS and Neural-Swarm are used, a robot does not require a complete and comprehensive picture of the environment that it is moving through, or of the path its fellow robots intend to take. Instead, robots learn how to navigate through a space on the fly, and incorporate new information as they go into a "learned model" for movement. Since each robot in a swarm only requires information about its local surroundings, decentralized computation can be done; in essence, each robot "thinks" for itself, which makes it easier to scale up the size of the swarm.

"These projects demonstrate the potential of integrating modern machine-learning methods into multi-agent planning and control, and also reveal exciting new directions for machine-learning research," says Yue.

To test their new systems, Chung's and Yue's teams implemented GLAS and Neural-Swarm on quadcopter swarms of up to 16 drones and flew them in the open-air drone arena at Caltech's Center for Autonomous Systems and Technologies (CAST). The teams found that GLAS could outperform the current state-of-the-art multi-robot motion-planning algorithm by 20 percent in a wide range of cases. Meanwhile, Neural-Swarm significantly outperformed a commercial controller that cannot consider aerodynamic interactions; tracking errors, a key metric in how the drones orient themselves and track desired positions in three-dimensional space, were up to four times smaller when the new controller was used.

Their research appears in two recently published studies. "GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning" was published in IEEE Robotics and Automation Letters on May 11 by Chung, Yue, Rivière, and Hönig. "Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions" was published in Proceedings of IEEE International Conference on Robotics and Automation on June 1 by Chung, Yue, Shi, and Hönig.

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