Biomimetic MEMS pressure/flow sensor arrays for passive fish-like underwater sensing applications
Certain fish have a superior ability to navigate blindly in a complex underwater environment. They perform this unique feat by relying on their lateral-line, consisting of arrays of biological sensors (neuromast) that interact with surrounding flow. An artificial man-made analogue similar to the lateral-line could greatly benefit current unmanned underwater vehicles (UUVs) that operate in a similarly difficult environments. In this work, we developed flexible arrays of microelectromechanical systems (MEMS) pressure and flow sensors that are easy to fabricate, highly sensitive, low-cost, low-powered, surface-mountable, and are capable of withstanding harsh seawater environments. In a quest for reliability and high sensitivity, we developed lithographically fabricated micro sensors out of novel soft polymer materials like polydimethylsiloxane (PDMS) and liquid crystal polymer (LCP) with conductive piezoresistive sensing elements over conventionally used silicon material.
MEMS haircell sensors with high-aspect ratio Si60 polymer haircell and a liquid crystal polymer membrane that are encapsulated in to a biomimetic canal package. Each haircell is located in between a pair of canal pores allowing a pressure-gradient sensing.
Three experimental pressure sensor arrays were mounted on the side of an autonomous surface vehicle and field-tested in Singapore. The pressure sensors provided an accurate means to monitor vehicle dynamics. Additional work will focus on using the sensor arrays for feedback control, object detection, and flow profiling.
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Ajay G. P KOTTAPALLI:
Bio-inspired MEMS artificial neuromast sensors for fish-like underwater sensing
Evolution bestowed the blind cave fish Astyanax mexicanus fasciatus with a resourcefully designed lateral-line of sensors that enable the fish to swim dexterously and navigate with great agility in hydrodynamically challenging underwater environment. The lateral-line plays a vital role in many tasks performed by the fish, including schooling, object detection and avoidance, energy-efficient maneuvering, rheotaxis, etc. The lateral-line works on a ‘touch at a distance’ sensing principle and enables the blind fish to “see” by imaging distortions in self-generated flow patterns created due to the presence of an underwater stimulus (object). The lateral-line of the fish is made of a number of neuromasts which form the principle sensing elements. Two types of neuromasts in the fish, namely, the superficial neuromasts (SNs) and the canal neuromasts (CNs), which work by the principle of division of labour, gratify most of the sensing needs of the blind fish. The SNs, present superficially on the surface of the skin, act as velocity sensors and the CNs present sub-dermally within a canal-like structure, act as acceleration sensors. Fundamentally, both these neuromasts consist of a cupula that extends into the flow, the haircells that generate electric impulses, and the cupular fibrils that offer mechanical strength to the cupula.
An artificial lateral-line system that replicates the functionality of the lateral-line in fish would be a great benefit for the navigation of underwater vehicles. An example would be - flow sensing around the bodies of the vehicles could benefit in achieving energy-efficient maneuvering and in real-time detection of near-field underwater objects. Researchers working in the fields of marine navigation are in a constant surge for improving maneuverability and finding inexpensive, light-weight, power saving alternatives for sensors on the vehicles which could perform passive sensing. Our research, for the first time, reports a complete biomimetic MEMS sensory analogue of the mechanosensory lateral-line developed using polymer materials. This includes the development both artificial SN and CN sensors that can perform a complete underwater sensing with abilities that rival those of the biological neuromasts on the blind cave fish. Liquid crystal polymer (LCP), owing to the plethora of advantages it offers for sensors for underwater sensing, in particular, is used as the structural membrane material in the MEMS sensor developed. The two types of haircell sensors developed in this work – SN inspired LCP haircell sensors, and the CN inspired PZT haircell sensors, showed excellent performance in sensing steady-state (dc) and oscillatory flows (ac) underwater respectively. The LCP haircell sensors demonstrate a high sensitivity of 0.022 mV/(m/s) and a threshold velocity detection limit of 0.015 m/s in sensing dc water flows. The piezoelectric haircell sensors display a sensitivity 22 mV/(mm/s) and a threshold velocity detection limit of 8.24 mm/s for sensing ac flows in water. Translating the knowledge gained from the morphology of the individual neuromast, we developed artificial cupula sensors employing Hyaluronic acid-methacrylic anhydride (HA-MA) hydrogel. HA-MA hydrogel resulted in having the same mechanical and material properties as the biological cupula of the fish in the nano-indentation and rheology measurements conducted. A nanofibril scaffold developed by electrospinning process assists the formation of a prolate-spheroid shaped cupula. The addition of the biomimetic cupula enhanced the sensitivity of the naked haircell sensor manifold times.
However the artificial SN sensors show a good performance, these sensors become saturated with strong background low frequency flow (noise). In order to detect stimulus generated disturbances underwater in noisy background flow conditions, artificial canal versions of the biological CNs are designed and fabricated. A comparative study of the performance of the artificial CN and the SN sensors is made by testing the sensors in the presence of both dc and ac flows. The artificial CNs, in comparison with the artificial SNs, revealed a high immunity to the background noise and act as a good bio-mechanical spectral filter.