Sensing water properties at precise depths from the air. Issue 8 (18th July 2018)

• Record Type: Journal Article
• Authors: Ore, John‐Paul; Detweiler, Carrick
• Other Names: Hutter Marco guestEditor.; Siegwart Roland guestEditor.
• Is Part Of: Journal of field robotics. Volume 35:Issue 8(2018)
• Page Start: 1205
• Page End: 1221
• Publication Date: 2018-07-18
• Subjects: aerial robotics -- environmental monitoring -- underwater robotics; Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21807
• Languages: English
• ISSNs: 1556-4959

Abstract

Abstract: Water properties are critical to our understanding and managing of freshwater systems that change rapidly with depth. This work presents an unmanned aerial vehicle (UAV) ‐based method of keeping a submerged, cable‐suspended sensor payload at a precise depth, with 95% of sensor readings within ±8.4 cm of the target depth. We use a depth altimeter attached at the terminus of a 3.5 m semirigid cable as the sole input to a depth controller actuated by the UAV's motors. First, we simulate the UAV‐cable‐payload system with disturbances of wind, water, signal delay, and GPS drift and then use parameters discovered during simulation to guide implementation. We characterize the depth altimeter during translation and find that 95% of the readings are within ±7 mm of ground truth, with a steady‐state error of ±3 mm. In field experiments, we compare the depth precision of our new method to previous methods that used the UAV's altitude as a proxy for submerged sensor depth, specifically 1) only using the UAV's air pressure altimeter; and 2) fusing UAV‐mounted ultrasonic sensors with the air pressure altimeter. Our new method reduces the standard deviation of depth readings from 16.1 to 4.2 cm in winds up to 8.0 ms −1 . We show the step response of the depth‐altimeter method when transitioning between target depths. Finally, we explore a longer, 8.0 m cable and show that our depth controller still outperforms air altimeter and ultrasonic methods and allows scientists to increase the spatiotemporal resolution of water property datasets.