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Controlling a bio-inspired miniature blimp using a depth sensing neural-network camera.

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This study demonstrates autonomous flight for miniature blimps in GPS-denied areas using a low-cost camera and inertial measurement unit. The system achieved precise 3D position hold and waypoint navigation, comparable to expensive systems.

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Area of Science:

  • Robotics and Autonomous Systems
  • Aerospace Engineering
  • Computer Vision

Background:

  • Miniature blimps are valuable unmanned aerial systems due to their endurance and safety.
  • Traditional navigation systems (GPS, motion capture) are costly and impractical for many environments.
  • There is a need for affordable, portable solutions for autonomous blimp navigation.

Purpose of the Study:

  • To develop and evaluate a low-cost autonomous flight system for miniature blimps in GPS-denied environments.
  • To enable basic flight autonomy using affordable sensors for position and orientation estimation.
  • To demonstrate comparable performance to conventional, high-cost positioning systems.

Main Methods:

  • Utilized the UNSW-C miniature blimp, a bio-inspired design with a helium-filled envelope and control fins.
  • Integrated a low-cost embedded neural network stereoscopic camera (OAK-D-PoE) for blimp detection and positioning.
  • Employed an onboard inertia measurement unit for orientation estimation.

Main Results:

  • Achieved stable 3D position hold with positional variance less than 0.1 meters.
  • Demonstrated basic waypoint navigation capabilities.
  • Performance was comparable to a high-end multi-camera positioning system (VICON).

Conclusions:

  • Low-cost positioning systems offer a viable alternative for achieving autonomous flight in GPS-denied conditions.
  • This approach extends the operational capabilities of unmanned aerial systems.
  • The system shows promise for applications requiring affordable and portable autonomous navigation.