Inhaltszusammenfassung

The movement of stones is important in a variety of disciplines such as geomorphology or hydraulic engineering. Plenty of different sensors, visual, active or passive tracers exist to capture movements in various ways. However, none of them is sufficiently small to be implanted in pebbles with a longest axis of approx. 60 mm. In this article, a sufficiently small probe is introduced: the Smartstone probe. It consists of a metal cylinder (diameter 8 mm, length 55 mm) with a flexible antenna an...The movement of stones is important in a variety of disciplines such as geomorphology or hydraulic engineering. Plenty of different sensors, visual, active or passive tracers exist to capture movements in various ways. However, none of them is sufficiently small to be implanted in pebbles with a longest axis of approx. 60 mm. In this article, a sufficiently small probe is introduced: the Smartstone probe. It consists of a metal cylinder (diameter 8 mm, length 55 mm) with a flexible antenna and contains a Bosch BMX055 sensor composed of a triaxial accelerometer, magnetometer and gyroscope, respectively. Additional components inside the probe are memory to store data, active RFID (Radio-frequency identification) technique to transmit data and two button cells as power supply. Mounted into a pebble, the applicability of this probe was tested in laboratory flume experiments by determining the pebble movement using the Smartstone measurements and comparing them to the movement pattern captured by a high-speed camera. The derived orientations and positions in these test experiments resulted in deviations of 32.4% compared to the visual footage. The different reasons for deviations are noise, quantization error, integration error, orientation error and clipping. The error sources were divided with supplementary experiments resulting in mean absolute deviation (MAE) of 33% due to noise, quantization, and integration errors; orientation errors result in an increased MAE of 13.7% in natural environment and 21.7% in laboratory. The MAE of all experiments containing clipping was 632%. These deviations will be reduced in future by application of methods like Kalman filtering or Markov models, which are established in other disciplines like computer science, robotics or (pedestrian) navigation. (C) 2016 The Authors. Published by Elsevier B.V. » weiterlesen» einklappen

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