Autonomous Flying With Neuromorphic Sensing

Area of Science:

• Neuroscience
• Robotics
• Computer Science
• Sensor Physics
• Mathematics

Background:

• Autonomous flight for large aircraft is nearing reality but requires significant interdisciplinary research.
• Current challenges necessitate advancements in sensing, data processing, and flight control for operational autonomy.

Purpose of the Study:

• To advocate for a bio-inspired, neuromorphic approach to autonomous flight challenges.
• To outline the current state of sensing, data processing, and flight control within a neuromorphic paradigm.
• To identify research directions for achieving naturalistic autonomous flight capabilities.

Main Methods:

• Examining biological learning mechanisms in neuroscience for energy and computational efficiency.
• Analyzing flight behaviors of insects, bats, and birds for insights into navigation, attention, and active sensing.
• Proposing fundamental changes in hardware and software for implementing neuromorphic paradigms.

Main Results:

• Insect flight dynamics offer models for navigation and control with reduced brain complexity.
• Bat and bird flight behaviors inform complex attention models and active sensing for advanced missions.
• Neuromorphic computing requires adaptive, relativistic information acquisition for efficient learning.

Conclusions:

• A bio-inspired, neuromorphic approach is crucial for advancing autonomous flight.
• Recommendations for sensor hardware and processing algorithms are provided for energy-efficient flight control.
• Achieving autonomous flight comparable to natural systems requires concerted interdisciplinary efforts.