# Wind Source Localization System Based on a Palm-Sized Quadcopter
## 5 Conclusions  
In this study, we constructed a system using a palm-sized quadcopter equipped with an anemotaxis algorithm to achieve WSL. Many organisms elicit the ability to move against flows, a crucial strategy for locating food sources or mates. This biological strategy has been successfully implemented in terrestrial and underwater robots to enhance odor source localization performance. However, the detection of environmental winds was believed to be challenging using quadcopters capable of three-dimensional flight due to the influence of wake effects. Recent research advances have shown that installing anemometers and wind vanes away from the quadcopter body allows for the effective monitoring of environmental winds. Thus, in this study, we integrated a wind direction sensor capable of observing environmental winds on the palm-sized quadcopter and implemented an anemotaxis algorithm to construct a system capable of localizing a wind source. We installed a lightweight heat-probe-type wind direction sensor weighing approximately 1.2 g on the quadcopter, with its position and height determined experimentally. Based on insights from prior studies, we placed the wind direction sensor at the center of the quadcopter but adjusted its height according to the quadcopter size to account for  
Figure 7: Wind speed and direction distributions for each scenario during WSL. (**a**) Measured wind speed distribution for each scenario. Each color indicates a different scenario. (**b–d**) Rose plot of measured wind direction values for Scenarios 1–3. The red line represents the mean value. variations in wake strength. We attached the sensor at 50 and 100 mm heights in experiments to investigate whether the true wind source direction could be accurately estimated under conditions where the propeller rotation generated the wake. The results demonstrate that even at a lower sensor height (50 mm), the accurate estimation of the true wind source direction was feasible, thereby determining the appropriate sensor arrangement to achieve anemotaxis. We developed an anemotaxis algorithm based on wind direction and speed data from the wind direction sensor. Due to the inherent characteristics of the wind direction sensor, which continues to output values even in the absence of environmental winds, we calculated the mode value from wind direction data when a consistent wind speed was observed to determine the direction of movement. This approach effectively suppressed erroneous movements due to false detections and enabled accurate localization toward the direction of the wind source. Through WSL experiments conducted in three environmental scenarios, we confirmed the capability of the system to localize the wind source independently of wind strength. Additionally, in scenarios without wind, the constructed anemotaxis algorithm operated by entering an exploration mode involving random movement due to the lack of correct wind direction information. Thus, by implementing the wind direction sensor in areas of minimal wake influence on the palm-sized quadcopter, we successfully detected environmental winds and constructed a system capable of WSL. This technique is a navigation strategy that works in windy environments and may not work in enclosed, windless environments. To develop a practical system, it will be important to switch navigation strategies depending on the environmental conditions, such as integrating other sensory information, utilizing GNSS information, and using statistical position estimation methods. In addition, because the focus of this study was to clarify how reliable the information from the wind direction sensor is, we employed the simplest behavior decision algorithm that directly uses the information from the sensor. However, in environments where wind direction constantly varies or multiple wind sources exist, this simple algorithm is insufficient.