# Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor

arXiv: https://arxiv.org/abs/2011.00534 · HTML: https://mlsanity.com/paper/2011.00534/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://mlsanity.com/paper/2305.19725/) (2023)
- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition](https://mlsanity.com/paper/2209.08723/) (2022)
- [Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems](https://mlsanity.com/paper/2504.00957/) (2025)
- [Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors](https://mlsanity.com/paper/2109.10199/) (2021)
- [EvTTC: An Event Camera Dataset for Time-to-Collision Estimation](https://mlsanity.com/paper/2412.05053/) (2024)
- [A Neuromorphic Approach to Obstacle Avoidance in Robot Manipulation](https://mlsanity.com/paper/2404.05858/) (2024)
- [Robust Iterative Value Conversion: Deep Reinforcement Learning for Neurochip-driven Edge Robots](https://mlsanity.com/paper/2408.13018/) (2024)
- [SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning](https://mlsanity.com/paper/2502.17496/) (2025)
- [Realizing Fully-Integrated, Low-Power, Event-Based Pupil Tracking with Neuromorphic Hardware](https://mlsanity.com/paper/2511.20175/) (2025)
- [MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots](https://mlsanity.com/paper/2606.07813/) (2026)

## Abstract (arXiv)

Neuromorphic processors like Loihi offer a promising alternative to conventional computing modules for endowing constrained systems like micro air vehicles (MAVs) with robust, efficient and autonomous skills such as take-off and landing, obstacle avoidance, and pursuit. However, a major challenge for using such processors on robotic platforms is the reality gap between simulation and the real world. In this study, we present for the very first time a fully embedded application of the Loihi neuromorphic chip prototype in a flying robot. A spiking neural network (SNN) was evolved to compute the thrust command based on the divergence of the ventral optic flow field to perform autonomous landing. Evolution was performed in a Python-based simulator using the PySNN library. The resulting network architecture consists of only 35 neurons distributed among 3 layers. Quantitative analysis between simulation and Loihi reveals a root-mean-square error of the thrust setpoint as low as 0.005 g, along with a 99.8% matching of the spike sequences in the hidden layer, and 99.7% in the output layer. The proposed approach successfully bridges the reality gap, offering important insights for future neuromorphic applications in robotics. Supplementary material is available at https://mavlab.tudelft.nl/loihi/.