CPS: Synergy: Tracking Fish Movement with a School of Gliding Robotic Fish

Abstract: Tracking Fish Movement with a School of Gliding Robotic Fish This project is focused on developing the technology for continuously tracking the movement of live fish implanted with acoustic tags, using a network of relatively inexpensive underwater robots called gliding robotic fish. The research addresses two fundamental challenges in the system design: (1) accommodating significant uncertainties due to environmental disturbances, communication delays, and apparent randomness in fish movement, and (2) balancing competing objectives (for example, accurate tracking versus long lifetime for the robotic network) while meeting multiple constraints on onboard computing, communication, and power resources. Fish movement data provide insight into choice of habitats, migratory routes, and spawning behavior. By advancing the state of the art in fish tracking technology, this project enables better-informed decisions for fishery management and conservation, including control of invasive species, restoration of native species, and stock assessment for high-valued species, and ultimately contributes to the sustainability of fisheries and aquatic ecosystems. By advancing the coordination and control of gliding robotic fish networks and enabling their operation in challenging environments such as the Great Lakes, the project also facilitates the practical adoption of these robotic systems for a myriad of other applications in environmental monitoring, port surveillance, and underwater structure inspection. The project enhances several graduate courses at Michigan State University, and provides unique interdisciplinary training opportunities for students including those from underrepresented groups. Outreach activities, including robotic fish demos, museum exhibits, teacher training, and "Follow That Fish" smartphone App, are specifically designed to pique the interest of pre-college students in science and engineering. The goal of this project is to create an integrative framework for the design of coupled robotic and biological systems that accommodates system uncertainties and competing objectives in a rigorous and holistic manner. This goal is realized through the pursuit of five tightly coupled research objectives associated with the application of tracking and modeling fish movement: (1) developing new robotic platforms to enable underwater communication and acoustic tag detection, (2) developing robust algorithms with analytical performance assurance to localize tagged fish based on time-of-arrival differences among multiple robots, (3) designing hidden Markov models and online model adaptation algorithms to capture fish movement effectively and efficiently, (4) exploring a two-tier decision architecture for the robots to accomplish fish tracking, which incorporates model-predictions of fish movement, energy consumption, and mobility constraints, and (5) experimentally evaluating the design framework, first in an inland lake for localizing or tracking stationary and moving tags, and then in Thunder Bay, Lake Huron, for tracking and modeling the movement of lake trout during spawning. This project offers fundamental insight into the design of robust robotic-physical-biological systems that addresses the challenges of system uncertainties and competing objectives. First, a feedback paradigm is presented for tight interactions between the robotic and biological components, to facilitate the refinement of biological knowledge and robotic strategies in the presence of uncertainties. Second, tools from estimation and control theory (e.g., Cramer-Rao bounds) are exploited in novel ways to analyze the performance limits of fish tracking algorithms, and to guide the design of optimal or near-optimal tradeoffs to meet multiple competing objectives while accommodating onboard resource constraints. On the biology side, continuous, dynamic tracking of tagged fish with robotic networks represents a significant step forward in acoustic telemetry, and results in novel datasets and models for advancing fish movement ecology.