Neuromorphic visual navigation reduces power use for small drones
Towards bio-inspired visual navigation: a neuromorphic navigation paradigm for MAVs
Robotics
Summary
Small flying robots called MAVs need to navigate complex places using cameras, but regular AI methods use a lot of power. The authors show how using a brain-inspired method with special sensors and spiking neural networks lets these robots see and decide where to go while using much less energy. Their system works well in both simulations and real flights, matching regular AI performance but with far less power consumption. This shows a new way to make tiny flying robots smarter and more energy efficient.
What this means in practice
- •For embedded systems engineers: Build low-power navigation modules for small drones that operate longer in complex environments by using neuromorphic event-driven sensing and processing.
- •For robotics hardware designers: Incorporate spiking neural network processors like Intel Loihi to reduce power consumption of autonomous navigation systems in edge robotics.
Authors
Junjie Jiang, Ankang Zhou, Zheng Fang
Abstract
Biological visual navigation achieves remarkable robustness at extremely low power, whereas frame-based robotic systems relying on artificial neural networks remain limited in both respects, particularly for energy-constrained micro aerial vehicles (MAVs) operating in complex environments. Inspired by the event-driven, sparse, and asynchronous principles of biological visual systems, neuromorphic sensing and computation provide a promising alternative for robust and energy-efficient navigation. In this paper, we propose the first end-to-end event-driven neuromorphic paradigm for MAV visual navigation in complex environments by integrating neuromorphic vision, spiking neural networks, and neuromorphic processors. The proposed method first extracts the environmental representation from event streams using a spiking perceptual representation module. This representation is fed into a spiking navigation module, where guidance learning and population coding enable efficient policy learning with few time steps. The navigation module then outputs a waypoint converted into executable trajectory points. Experiments show that the proposed neuromorphic method achieves navigation performance comparable to ANN-based methods while markedly reducing power consumption. The spiking perceptual module consumes only 19.57% of the power of its ANN counterpart, while the spiking navigation module deployed on Intel Loihi consumes only 12.37% of that required by its ANN counterpart running on NVIDIA Orin NX. Beyond prior neuromorphic systems focused mainly on relatively simple navigation subtasks, we demonstrate a complete neuromorphic visual navigation system spanning simulation and real-world deployment. Our results establish the feasibility of the neuromorphic paradigm for autonomous navigation and provide a bio-inspired pathway toward robust and low-power MAV navigation.