Energid created proof-of-concept software for the U.S. Navy for controlling unmanned vehicles using human hand gestures. Our approach provides sensory information fusion, structurized knowledge representation, embedded system adaptive learning, and advanced remote supervision. It can instill more intelligence and autonomy while reducing operator workload.

We apply sensor techniques and intelligent control schemes to UAVs for enhancing autonomy. Our system identifies the arm and body movements of directors on a carrier deck using two simultaneous methods: a vision system and clothing-embedded accelerometers. Two independent sensory methods are needed to achieve high reliability. This information is then translated into UAV path-planning commands. The sensor suite for this project can improve perception capability of autonomous systems, and the intelligent control method can add learning, system adaptation, and safety. The level of autonomy offered through these techniques will support complex situations involving many UAVs. A video demonstration of Energid's gesture recognition can be downloadedhere.