  Vision-based object tracking is an essential precursor to performing
autonomous aerial navigation in order to avoid obstacles. Biologically inspired
neuromorphic event cameras are emerging as a powerful alternative to
frame-based cameras, due to their ability to asynchronously detect varying
intensities (even in poor lighting conditions), high dynamic range, and
robustness to motion blur. Spiking neural networks (SNNs) have gained traction
for processing events asynchronously in an energy-efficient manner. On the
other hand, physics-based artificial intelligence (AI) has gained prominence
recently, as they enable embedding system knowledge via physical modeling
inside traditional analog neural networks (ANNs). In this letter, we present an
event-based physics-guided neuromorphic planner (EV-Planner) to perform
obstacle avoidance using neuromorphic event cameras and physics-based AI. We
consider the task of autonomous drone navigation where the mission is to detect
moving gates and fly through them while avoiding a collision. We use event
cameras to perform object detection using a shallow spiking neural network in
an unsupervised fashion. Utilizing the physical equations of the brushless DC
motors present in the drone rotors, we train a lightweight energy-aware
physics-guided neural network (PgNN) with depth inputs. This predicts the
optimal flight time responsible for generating near-minimum energy paths. We
spawn the drone in the Gazebo simulator and implement a sensor-fused
vision-to-planning neuro-symbolic framework using Robot Operating System (ROS).
Simulation results for safe collision-free flight trajectories are presented
with performance analysis, ablation study and potential future research
directions
