Marine Technology Society A Unique , Multidisciplinary , Oceans and Marine Network Autonomous Robotic Fish as Mobile Sensor Platforms : Challenges and Potential Solutions

in biological swimming have stimulated extensive theoretical, experimental, and computational research by biologists, mathematicians, and engineers, in an effort to understand and mimic locomotion, maneuvering, and sensing mechanisms adopted by aquatic animals. Over the past two decades, there has also been significant interest in developing underwater robots that propel and maneuver themselves like real fish do (Triantafyllou & Triantafyllou, 1995; Kato, 2000; Anderson & Chhabra, 2002; Alvarado & Youcef-Toumi, 2006; Hu et al., 2006; Low, 2006; Epstein et al., 2006; Morgansen et al., 2007; Lauder et al., 2007; Chen et al., 2010; Aureli et al., 2010; Smithers, 2011). Often termed robotic fish, these robots provide an experimental platform for studying fish swimming and hold strong promise for a number of underwater applications. Instead of using propellers, robotic fish accomplish swimming by deforming the body and/or fin-like appendages, mostly functioning as caudal fins and sometimes as pectoral fins. Body deformation and fin movements are typically achieved with motors. On the other hand, advances in smart materials have been explored to actuate robotic fish in a noiseless and compact way (Paquette & Kim, 2004; Tangorra et al., 2007; Chen et al., 2010; Aureli et al., 2010). Robotic fish produce wake signatures similar to those of real fish and are thus less detectable than propeller-driven underwater vehicles, which is an important advantage in applications requiring stealth. Recent advances in computing, communication, electronics, and materials have made it possible to create untethered robotic fish with onboard power, control, navigation, July/Au wireless communication, and sensing modules, which turns these robots into mobile sensing platforms in aquatic environments. Schools of robotic fish can form wireless sensor networks, which will have numerous promising applications, such as monitoring water quality, tracing oil spills, and patrolling harbors and coasts. Figure 1a shows a prototype of a robotic fish swimming in an inland lake. Figure 1b shows the close-up of another prototype, equipped with a dissolved oxygen (DO) sensor, global positioning system (GPS), and other electronic components, which has been developed for monitoring the DO level in aquafarms. Collected DO information will then be used to control the aerators to maintain a healthy environment for the aquatic animals on the farm. Autonomous robotic fish schools will provide a competitive alternative gust 2011 Volume 45 Number 4 31 to existing sensing technologies for aquatic and marine environments. Manual sampling, sometimes boat or ship-based, is still a common practice in environmental monitoring, which is labor-intensive with difficulty in capturing dynamic phenomena of interest. In-situ sensing with fixed or buoyed sensors or vertical profilers is another approach (Doherty et al., 1999; Reynolds-Fleming et al . , 2002). However, these sensors have little freedom to move laterally, and it would require prohibitively many units for capturing distributed, spatially inhomogeneous information. The past decade has seen great progress in the use of robotic technology in aquatic sensing. Autonomous underwater vehicles (AUVs) (Bandyopadhyay, 2005), for example, are being used for hydrographic survey, fishery operations, and environmental monitoring (Hydroid, 2009). Another highly successful technology is autonomous sea gliders, which has remarkable duration for continuous field operation because of highly energy-efficient design (Ericksen et al., 2001; Sherman et al., 2001; Webb et al., 2001; Rudnick et al., 2004). The downside for both AUVs and gliders is their cost, starting at US $50,000 per unit (not including 32 Marine Technology Society Journa the cost of sensors), prohibiting the deployment of many of them for observing with high spatial resolution, and excluding them from many applications (such as aquafarm monitoring) where cost is critical. The size (meters long) and weight (at the order of 50 kg) of these vehicles also make them cumbersome to handle by a single person. Small autonomous robotic fish have the potential to address many of the aforementioned challenges. By a small robotic fish, we mean one that has length of 50 cm or less, displaces volume of up to 5 liters, and costs no more than US $5,000 (excluding that of aquatic sensors to be mounted). Its low cost, compact size, and light weight would make it affordable and convenient to deploy these robots in groups for versatile applications and various environments, such as ponds, lakes, rivers, and even oceans. Schools of robotic fish could form dynamic, adaptive sensor networks and provide distributed sensing coverage with desired spatiotemporal resolution. The realization of such a vision, however, is faced with a myriad of challenges. The size and cost considerations put stringent constraints on the robot’s locomotion, battery, computing, and communication cal pacities. The wide adoption of the robotic fish-based sensing technology will hinge on the robots’ ability to operate robustly in the unfriendly and often unpredictable environment, with their limited onboard resources and with minimal human intervention. This poses challenges across a wide spectrum, ranging from locomotion and maneuvering mechanisms, to energy-efficient designs, localization and communication schemes, and control and coordination strategies, to name a few. In this paper, we outline some of the most critical challenges and discuss potential approaches or opportunities in research and technology advancement for addressing the challenges. Maneuvering in Uncertain Environment As a sensor platform, the robotic fish often needs to survey a given path or hover over a particular region in the presence of ambient disturbances caused by wind, waves, currents, and turbulences. Regardless of its propulsion mechanism, however, a small robotic fish has limited actuation authority to counteract the disturbances. It is thus of great interest to be able to sense the flow and react in the most effective way under the actuation constraints. We can look to live fish for inspiration, because they deal with this very problem on a regular basis and have developed intricate sensing and actuation systems that offer us interesting insight. Artificial Lateral Line Most fish use the lateral line system as an important sensory organ to probe their environment (Coombs, 2001). A lateral line consists of arrays of so FIGURE 1 Prototypes of autonomous robotic fish developed by the Smart Microsystems Laboratory at Michigan State University: (a) testing in a lake and (b) prototype for dynamically monitoring the DO level in aquafarms. called neuromasts, each containing bundles of sensory hairs, encapsulated in a gelatinous structure called cupula. Under an impinging flow, the hairs are deflected, which elicits firing of the hair cell neurons and thus enables the animal to sense the flow field, perform hydrodynamic imaging, and identify new field objects of interest. The lateral line system plays an important role in various fish behaviors, including prey/ predator detection, schooling, rheotaxis, courtship and communication. A lateral line-like sensory module or an artificial lateral line will be very useful for a robotic fish to improve its maneuverability. For example, with feedback from the lateral line, the robot could manipulate vortices in the flow with its actuated fins and exploit the ambient flow energy for locomotion (Beal et al., 2006) or perform station-keeping by responding appropriately to the sensed ambient flow. Artificial lateral line systems, where arrays of beam or hair-like structures are used to measure flow velocities, have been proposed based on various physical transduction principles, including hot wire anemometry (Yang et al., 2006), piezoresistivity (Yang et al., 2010), capacitive sensing (Dagamseh et al., 2010), and encapsulated interface bilayers (Sarles et al., 2011). Recently, we have exploited the intrinsic mechanosensory property of ionic polymer-metal composites (IPMCs) to construct artificial lateral lines (Abdulsadda & Tan, 2011; Abdulsadda et al., 2011). As illustrated in Figure 2, an IPMC consists of three layers, with an ion-exchange polymer membrane (e.g., Nafion) sandwiched bymetal electrodes. Inside the polymer, (negatively charged) anions covalently fixed to polymer chains are balanced by mobile (positively charged) cations. An applied mechanical stimulus, such as a flow impinging on the IPMC, redistributes the cations inside and produces a detectable electrical signal (typically open-circuit voltage or short-circuit current) that is correlated with the mechanical or hydrodynamic stimulus (Chen et al., 2007). Conversely, an applied voltage across an IPMC leads to the transport of cations and accompanying solvent molecules, resulting in both differential swelling and electrostatic forces inside the July/Au material, which cause the material to bend and hence the actuation effect (Shahinpoor & Kim, 2001). Figure 3a shows a prototype of an artificial lateral line consisting of four IPMC sensors. While the physical construction of robust and sensitive artificial lateral lines remains an active research area, it is of equal importance to make sense out of the data collected by the lateral line. Existing studies on biological and artificial lateral lines have FIGURE 2 Illustration of the IPMC sensing principle.