# AI and Vision based Autonomous Navigation of Nano-Drones in Partially-Known Environments

arXiv: https://arxiv.org/abs/2505.04972 · HTML: https://mlsanity.com/paper/2505.04972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 16 of 16)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones](https://mlsanity.com/paper/1805.01831/) (2018)
- [Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs](https://mlsanity.com/paper/2103.10873/) (2021)
- [An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs](https://mlsanity.com/paper/1905.04166/) (2019)
- [Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones](https://mlsanity.com/paper/2301.12175/) (2023)
- [NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter](https://mlsanity.com/paper/2209.06918/) (2022)
- [A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing](https://mlsanity.com/paper/2312.08991/) (2023)
- [Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning](https://mlsanity.com/paper/2203.14749/) (2022)
- [Distilling Tiny and Ultra-fast Deep Neural Networks for Autonomous Navigation on Nano-UAVs](https://mlsanity.com/paper/2407.12675/) (2024)
- [A Parallel Implementation of Computing Mean Average Precision](https://mlsanity.com/paper/2206.09504/) (2022)
- [Resource-aware Deployment of Dynamic DNNs over Multi-tiered Interconnected Systems](https://mlsanity.com/paper/2404.08060/) (2024)
- [Land & Localize: An Infrastructure-free and Scalable Nano-Drones Swarm with UWB-based Localization](https://mlsanity.com/paper/2307.10255/) (2023)
- [Resource-Efficient Sensor Fusion via System-Wide Dynamic Gated Neural Networks](https://mlsanity.com/paper/2410.16723/) (2024)
- [Latency-aware and -predictable Communication with Open Protocol Stacks for Remote Drone Control](https://mlsanity.com/paper/2010.09584/) (2020)
- [Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones](https://mlsanity.com/paper/2307.01559/) (2023)
- [Is That Rain? Understanding Effects on Visual Odometry Performance for Autonomous UAVs and Efficient DNN-based Rain Classification at the Edge](https://mlsanity.com/paper/2407.12663/) (2024)

## Abstract (arXiv)

The miniaturisation of sensors and processors, the advancements in connected edge intelligence, and the exponential interest in Artificial Intelligence are boosting the affirmation of autonomous nano-size drones in the Internet of Robotic Things ecosystem. However, achieving safe autonomous navigation and high-level tasks such as exploration and surveillance with these tiny platforms is extremely challenging due to their limited resources. This work focuses on enabling the safe and autonomous flight of a pocket-size, 30-gram platform called Crazyflie 2.1 in a partially known environment. We propose a novel AI-aided, vision-based reactive planning method for obstacle avoidance under the ambit of Integrated Sensing, Computing and Communication paradigm. We deal with the constraints of the nano-drone by splitting the navigation task into two parts: a deep learning-based object detector runs on the edge (external hardware) while the planning algorithm is executed onboard. The results show the ability to command the drone at ~8 frames-per-second and a model performance reaching a COCO mean-average-precision of 60.8. Field experiments demonstrate the feasibility of the solution with the drone flying at a top speed of 1 m/s while steering away from an obstacle placed in an unknown position and reaching the target destination. The outcome highlights the compatibility of the communication delay and the model performance with the requirements of the real-time navigation task. We provide a feasible alternative to a fully onboard implementation that can be extended to autonomous exploration with nano-drones.