Localization matters too: How localization error affects UAV flight

TL;DR

The paper addresses how UAV localization error affects the maximum safe flight speed during obstacle avoidance. It develops a two-stage theoretical framework that connects localization error, sensing range, and latency to speed, including a closed-form bound for the first stage and a second-stage constraint on lateral dynamics. Key contributions include an analysis of parameter coupling, design guidance for hardware/software tradeoffs, and simulation validation showing predictions within a modest error margin up to speeds of 18 m/s. The work demonstrates that joint optimization of multiple interacting parameters is essential for maximizing safe speed in dense environments and offers practical insights for UAV system design.

Abstract

The maximum safe flight speed of a Unmanned Aerial Vehicle (UAV) is an important indicator for measuring its efficiency in completing various tasks. This indicator is influenced by numerous parameters such as UAV localization error, perception range, and system latency. However, in terms of localization errors, although there have been many studies dedicated to improving the localization capability of UAVs, there is a lack of quantitative research on their impact on speed. In this work, we model the relationship between various parameters of the UAV and its maximum flight speed. We consider a scenario similar to navigating through dense forests, where the UAV needs to quickly avoid obstacles directly ahead and swiftly reorient after avoidance. Based on this scenario, we studied how parameters such as localization error affect the maximum safe speed during UAV flight, as well as the coupling relationships between these parameters. Furthermore, we validated our model in a simulation environment, and the results showed that the predicted maximum safe speed had an error of less than 20% compared to the test speed. In high-density situations, localization error has a significant impact on the UAV's maximum safe flight speed. This model can help designers utilize more suitable software and hardware to construct a UAV system.

Figures (8)

• Figure 1: Compared to previous strategy [13], our strategy requires the UAV to avoid obstacles directly ahead in the most extreme manner possible, and to realign its direction within a limited distance $L$. Additionally, we also take into consideration the expansion of obstacles in the UAV's map caused by localization error.
• Figure 2: The distance $L$ for the UAV to realign its direction can be considered to have a simple mathematical relationship with the distance $R$ to the obstacle.
• Figure 3: The localization error of the UAV causes the obstacles to expand in configuration space [21].
• Figure 4: (A) The physical performance of the UAV constrains its acceleration to reach its maximum value after a certain period of time. (B) When the localization error is zero, the model degrades to an ideal state, and the acceleration remains constant.
• Figure 5: (A)-(B) When $v < v_1$, the proportion of t' during the entire acceleration phase is very small, and the UAV's displacement in the y direction has a minimal deviation from the ideal. (C) The safe speed of the UAV is simultaneously constrained by two stages, leading to it being less than $v_1$. (D) Localization error causes the influence of sensing range on safe speed to deviate from linearity. (E) In high-density scenarios, localization error has a more pronounced impact on safe speed.