Anemoi: A Low-cost Sensorless Indoor Drone System for Automatic Mapping of 3D Airflow Fields

Minghui (Scott) Zhao, Stephen Xia, Charuvahan Adhivarahan, Kaiyuan Hou, Yuyang Chen, Jingping Nie, Eugene Wu, Karthik Dantu, Xiaofan (Fred) Jiang
October, 2023
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Abstract

Mapping 3D airflow fields is important for many HVAC, industrial, medical, and home applications. However, current approaches are expensive and time-consuming. We present Anemoi, a sub-$100 drone-based system for autonomously mapping 3D airflow fields in indoor environments. Anemoi leverages the effects of airflow on motor control signals to estimate the magnitude and direction of wind at any given point in space. We introduce an exploration algorithm for selecting optimal waypoints that minimize overall airflow estimation uncertainty. We demonstrate through microbenchmarks and real deployments that Anemoi is able to estimate wind speed and direction with errors up to 0.41 m/s and 25.1 degree lower than the existing state of the art and map 3D airflow fields with an average RMS error of 0.73 m/s.

Type
Conference paper
Publication
In Proceedings of the 29th Annual International Conference on Mobile Computing And Networking
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Minghui (Scott) Zhao
Minghui (Scott) Zhao
Ph.D. Candidate in Electrical Engineering

My research focuses on developing embodied and embedded AI systems that enable intelligent agents to perceive, understand, and act in the physical world through hardware-software co-design and physics-informed machine learning.