Moth: A Low-Cost IR-Based Approach towards Autonomous Precision Drone Landing
Yanchen Liu, Minghui Zhao, Kaiyuan Hou, Junxi Xia, Charles Carver, Stephen Xia, Xia Zhou, Xiaofan Jiang
AI summary
Problem
Palm-sized microdrones lack the power, payload, and computational resources for complex vision-based or UWB landing systems, while GPS fails indoors, creating a critical barrier to reliable autonomous docking in everyday environments.
Approach
The system uses an off-the-shelf infrared light bulb at the landing station and a lightweight photodiode array on the drone to guide the aircraft toward the light source via intensity gradient ascent, eliminating the need for heavy computation or pattern recognition.
Key results
- Sub-10 cm landing accuracy from up to 11.1 m away
- Under 83 USD cost and under 18 g weight
- Microwatt-level power consumption versus milliwatt vision systems
- Reliable guidance in low-light and partially non-line-of-sight conditions
Why it matters
Enables reliable, ultra-low-cost autonomous docking for resource-constrained microdrones in GPS-denied and visually degraded indoor environments.
Abstract
As micro-drones become increasingly deployed in indoor environments for applications ranging from warehouse inspection to emergency response, the challenge of precise au- tomated landing emerges as a crucial barrier to their practical operation and ubiquitous adoption. Existing landing approaches often require complex hardware and substantial computation or perform unreliably indoors, making them impractical for palm-sized microdrones. We propose Moth, a low-cost infrared light-based solution that targets precise and efficient landing of low-resource microdrones. Moth consists of an infrared light source at the landing station along with an energy-efficient photodiode (PD) sensing platform attached to the bottom of the drone. At a cost under 83 USD, Moth achieves comparable performance to vision-based methods but at a fraction of the energy consumption and computation. Moth requires only three PDs without any complex pattern recognition models to land the drone accurately, under 10 cm of error, from up to 11.1 m away.