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We developed an insect-inspired visual navigation system for tiny robots. This method uses compressed images and odometry for efficient route following, enabling autonomous navigation on resource-constrained drones.

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Area of Science:

  • Robotics
  • Computer Vision
  • Bio-inspired Engineering

Background:

  • Visual navigation is crucial for autonomous robots but computationally intensive, limiting its use on small, resource-constrained platforms like drones.
  • Existing methods struggle with the computational and memory demands of visual processing and data storage.

Purpose of the Study:

  • To propose an insect-inspired visual navigation strategy for extremely resource-restricted robots.
  • To enable autonomous route following and return journeys on lightweight drones.

Main Methods:

  • A route-following approach storing compressed panoramic images and odometry data.
  • Utilizing a combination of odometry and visual homing for inbound navigation, preventing odometric drift.
  • Minimizing stored image count by maximizing spacing based on odometry accuracy.

Main Results:

  • Implementation on a 56-gram drone successfully navigated routes up to 100 meters.
  • Trajectory representation achieved under 20 bytes per meter, demonstrating high efficiency.
  • The system proved suitable for small, resource-constrained robotic platforms.

Conclusions:

  • The proposed insect-inspired visual navigation method significantly advances autonomous capabilities for tiny robots.
  • This approach facilitates wider applications of autonomous navigation in small drones and other resource-limited robotic systems.