Optimal sensor placement for artificial swimmers

Abstract

Natural swimmers rely for their survival on sensors that gather information from the environment and guide their actions. The spatial organization of these sensors, such as the visual fish system and lateral line, suggests evolutionary selection, but their optimality remains an open question. Here, we identify sensor configurations that enable swimmers to maximize the information gathered from their surrounding flow field. We examine twodimensional, self-propelled and stationary swimmers that are exposed to disturbances generated by oscillating, rotating and D-shaped cylinders. We combine simulations of the Navier-Stokes equations with Bayesian experimental design to determine the optimal arrangements of shear and pressure sensors that best identify the locations of the disturbance-generating sources. We find a marked tendency for shear stress sensors to be located in the head and the tail of the swimmer, while they are absent from the midsection. In turn, we find a high density of pressure sensors in the head along with a uniform distribution along the entire body. The resulting optimal sensor arrangements resemble neuromast distributions observed in fish and provide evidence for optimality in sensor distribution for natural swimmers. † Email address for correspondence: petros@ethz.ch arXiv:1906.07585v1 [physics.flu-dyn]

Robust flow control and optimal sensor placement using deep reinforcement learning

“…(2016), Mons, Chassaing & Sagaut (2017) and Verma et al. (2020) for data assimilation. Bright, Lin & Kutz (2013) took advantage of compressed sensing to perform flow reconstruction using a limited number of sensors.…”

Optimal Flow Sensing for Schooling Swimmers

“…The support of the prior probability is discretized with 21 × 31 gridpoints. Since the experiments are independent, the total expected utility function for the three cases is the sum of the expected utility of each experiment [56]. Figure 3.…”

“…Following an earlier work for detection of flow disturbances generated from single obstacles [56], we examine the optimality of the spatial distribution of sensors in a self-propelled swimmer that infers the size and the relative position of the leading school. We combine numerical simulations of the two-dimensional Navier-Stokes equation and Bayesian optimal sensor placement to examine the extraction of flow information by pressure gradients and shear stresses and the optimal positioning of associated sensors.…”

Fish Lateral Line Inspired Flow Sensors and Flow-aided Control: A Review

“…Verma et al used a larva-shaped swimmer exposed in disturbances induced by oscillating, rotating and cylinders to conduct experiments in 2020. Combining Navier-Stokes equations with Bayesian experimental design and with a purpose of detecting the location of the source, they presented that shear sensors should be installed on the head and the tail while pressure sensors should be distributed uniformly along the body and intensively on the head, which is similar to real fish lateral line [48] . In 2019, Xu et al put forward an optimal weight analysis algorithm combined with feature distance and variance evaluation and 3 indexes to evaluate the performance of the sensor array.…”