  In autonomous aerial navigation, real-time and energy-efficient obstacle
avoidance remains a significant challenge, especially in dynamic and complex
indoor environments. This work presents a novel integration of neuromorphic
event cameras with physics-driven planning algorithms implemented on a Parrot
Bebop2 quadrotor. Neuromorphic event cameras, characterized by their high
dynamic range and low latency, offer significant advantages over traditional
frame-based systems, particularly in poor lighting conditions or during
high-speed maneuvers. We use a DVS camera with a shallow Spiking Neural Network
(SNN) for event-based object detection of a moving ring in real-time in an
indoor lab. Further, we enhance drone control with physics-guided empirical
knowledge inside a neural network training mechanism, to predict
energy-efficient flight paths to fly through the moving ring. This integration
results in a real-time, low-latency navigation system capable of dynamically
responding to environmental changes while minimizing energy consumption. We
detail our hardware setup, control loop, and modifications necessary for
real-world applications, including the challenges of sensor integration without
burdening the flight capabilities. Experimental results demonstrate the
effectiveness of our approach in achieving robust, collision-free, and
energy-efficient flight paths, showcasing the potential of neuromorphic vision
and physics-driven planning in enhancing autonomous navigation systems.
