Real-Time Neuromorphic Navigation: Guiding Physical Robots with Event-Based Sensing and Task-Specific Reconfigurable Autonomy Stack

Abstract

Neuromorphic vision, inspired by biological neural systems, has recently gained significant attention for its potential in enhancing robotic autonomy. This paper presents a systematic exploration of a proposed Neuromorphic Navigation framework that uses event-based neuromorphic vision to enable efficient, real-time navigation in robotic systems. We discuss the core concepts of neuromorphic vision and navigation, highlighting their impact on improving robotic perception and decision-making. The proposed reconfigurable Neuromorphic Navigation framework adapts to the specific needs of both ground robots (Turtlebot) and aerial robots (Bebop2 quadrotor), addressing the task-specific design requirements (algorithms) for optimal performance across the autonomous navigation stack -- Perception, Planning, and Control. We demonstrate the versatility and the effectiveness of the framework through two case studies: a Turtlebot performing local replanning for real-time navigation and a Bebop2 quadrotor navigating through moving gates. Our work provides a scalable approach to task-specific, real-time robot autonomy leveraging neuromorphic systems, paving the way for energy-efficient autonomous navigation.