Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle

垃圾 实时计算 环境科学 避障 水质 障碍物 计算机科学 海洋工程 地表水 模拟 工程类 遥感 汽车工程 环境工程 人工智能 废物管理 地质学 地理 生物 机器人 考古 移动机器人 生态学
作者
Hsing Cheng Chang,Ya-Wen Hsu,San-Shan Hung,Guan-Ru Ou,Jia-Ron Wu,Chuan Hsu
出处
期刊:Sensors [MDPI AG]
卷期号:21 (4): 1102-1102 被引量:26
标识
DOI:10.3390/s21041102
摘要

Water is one of the most precious resources. However, industrial development has made water pollution a critical problem today and thus water quality monitoring and surface cleaning are essential for water resource protection. In this study, we have used the sensor fusion technology as a basis to develop a multi-function unmanned surface vehicle (MF-USV) for obstacle avoidance, water-quality monitoring, and water surface cleaning. The MF-USV comprises a USV control unit, a locomotion module, a positioning module, an obstacle avoidance module, a water quality monitoring system, a water surface cleaning system, a communication module, a power module, and a remote human–machine interface. We equip the MF-USV with the following functions: (1) autonomous obstacle detection, avoidance, and navigation positioning, (2) water quality monitoring, sampling, and positioning, (3) water surface detection and cleaning, and (4) remote navigation control and real-time information display. The experimental results verified that when the floating garbage located in the visual angle ranged from −30° to 30° on the front of the MF-USV and the distances between the floating garbage and the MF-USV were 40 and 70 cm, the success rates of floating garbage detection are all 100%. When the distance between the floating garbage and the MF-USV was 130 cm and the floating garbage was located on the left side (15°~30°), left front side (0°~15°), front side (0°), right front side (0°~15°), and the right side (15°~30°), the success rates of the floating garbage collection were 70%, 92%, 95%, 95%, and 75%, respectively. Finally, the experimental results also verified that the applications of the MF-USV and relevant algorithms to obstacle avoidance, water quality monitoring, and water surface cleaning were effective.
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