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This arXiv preprint proposes an end-to-end spiking neural network pipeline for micro aerial vehicle navigation, running on event-camera input and Intel Loihi hardware. The reported power savings (the perception module at 19.57% of its ANN counterpart, the navigation module at 12.37% on Loihi versus an Orin NX) are relative comparisons from the authors' own experiments, so they need independent replication across tasks and platforms. The work is most useful to readers tracking whether neuromorphic approaches can match conventional deep learning on embodied control under tight energy budgets.
Towards bio-inspired visual navigation: a neuromorphic navigation paradigm for MAVs
arXiv:2610.21978v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2610.21978 [cs.RO]
(or arXiv:2610.21978v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.21978 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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