Caltech engineers revealed a new data-driven method to control the movement of multiple drones through cluttered, unfamiliar spaces, so they do not collide with each other. Obstacles that needed to be overcome in new environments include making split-second decisions about their trajectories despite having incomplete data about their future path and how not to run into one another.
So, the team came up with a multi-robot motion-planning algorithm called “Global-to-Local Safe Autonomy Synthesis” (GLAS) that 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.
- ๐๐ถ๐ด๐ต๐๐๐ฒ๐ถ๐ด๐ต๐ ๐ฎ๐ป๐ฑ ๐ฃ๐ผ๐ฟ๐๐ฎ๐ฏ๐น๐ฒ - The drone is lightweight and compact, weighing...
- ๐ก๐ผ ๐ฅ๐ฒ๐ด๐ถ๐๐๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ - Under 249 g, FAA Registration and Remote ID are not required if...
- ๐๐ ๐๐ฒ๐ป๐ฑ๐ฒ๐ฑ ๐๐น๐ถ๐ด๐ต๐ ๐ง๐ถ๐บ๐ฒ - Enjoy longer flights with DJI Mini 2 SE, which offers a 31-min max 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,” said Soon-Jo Chung, Bren Professor of Aerospace.
