Drone navigation policy uses vision for obstacle evasion

Researchers have developed a novel reinforcement learning policy for drones to autonomously navigate and evade obstacles in unknown outdoor environments. The system utilizes stereo-vision depth and visual-inertial odometry to generate velocity commands for commercial drones. Trained in simulation with a two-stage process and fine-tuned with domain randomization, the policy demonstrated successful zero-shot transfer to new drone platforms and environments without prior exposure. AI

IMPACT Enables autonomous drone operation in GPS-denied and unknown environments, potentially for delivery or surveillance.

RANK_REASON This is a research paper detailing a novel approach to drone navigation using reinforcement learning and computer vision. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Shiladitya Dutta

• paper
• other

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