Development of flapping wing robot and vision-based obstacle avoidance strategy.

Area of Science:

• Robotics
• Aerospace Engineering
• Computer Vision

Background:

• Flapping wing robots offer advantages like small size and low noise, making them suitable for search and reconnaissance.
• Existing research often prioritizes robot development over autonomous flight capabilities.
• Simultaneous development of flapping wing robots and autonomous flight systems is crucial due to unique aerodynamic challenges.

Purpose of the Study:

• To develop a lightweight flapping wing robot with stable flight characteristics.
• To create a computationally efficient, vision-based obstacle avoidance algorithm for autonomous navigation.
• To address challenges in motion-blurred and feature-less imagery during flight.

Main Methods:

• Developed the CNUX Mini, a 27 cm, 45 g flapping wing robot with an X-type wing and tail for stability.
• Implemented an optical flow-based obstacle detection algorithm incorporating appearance variation cues.
• Utilized state machine logic for executing obstacle avoidance maneuvers during straight flight.

Main Results:

• The CNUX Mini demonstrated stable flight for 1.5 minutes with controlled directional changes.
• The obstacle avoidance algorithm achieved a 90.2% success rate in simulated obstacle scenarios.
• The system proved robust against motion-blurred and feature-less images in ground tests.

Conclusions:

• The integrated system of the CNUX Mini robot and its vision-based obstacle avoidance algorithm is effective for autonomous flight.
• This technology holds significant potential for future search and reconnaissance applications.
• Further research can enhance autonomous capabilities for more complex environments.