Estimation and Adaption of Indoor Ego Airflow Disturbance with Application to Quadrotor Trajectory Planning

TL;DR

This paper introduces a novel method for estimating and adapting indoor ego airflow disturbances in quadrotors, enhancing safe trajectory planning in confined environments through experimental modeling and reachability analysis.

Contribution

It presents a new disturbance estimation model based on acceleration variance and integrates it with Hamilton-Jacobi reachability for improved indoor quadrotor navigation.

Findings

Disturbance models accurately reflect airflow effects during hover.

The integrated planning framework ensures safer quadrotor trajectories.

Validated on multiple platforms in various indoor settings.

Abstract

It is ubiquitously accepted that during the autonomous navigation of the quadrotors, one of the most widely adopted unmanned aerial vehicles (UAVs), safety always has the highest priority. However, it is observed that the ego airflow disturbance can be a significant adverse factor during flights, causing potential safety issues, especially in narrow and confined indoor environments. Therefore, we propose a novel method to estimate and adapt indoor ego airflow disturbance of quadrotors, meanwhile applying it to trajectory planning. Firstly, the hover experiments for different quadrotors are conducted against the proximity effects. Then with the collected acceleration variance, the disturbances are modeled for the quadrotors according to the proposed formulation. The disturbance model is also verified under hover conditions in different reconstructed complex environments. Furthermore, the approximation of Hamilton-Jacobi reachability analysis is performed according to the estimated disturbances to facilitate the safe trajectory planning, which consists of kinodynamic path search as well as B-spline trajectory optimization. The whole planning framework is validated on multiple quadrotor platforms in different indoor environments.