cs.ROFeb 24, 2026

Autonomous Sea Turtle Robot for Marine Fieldwork

Authors: Zach J. Patterson, Emily Sologuren, Levi Cai, Daniel Kim, Alaa Maalouf, Pascal Spino, Daniela Rus

Organizations: Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, 44106, USA. · Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, 02139, USA. · Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, 02543, USA. · Computer Science, University of Haifa, Haifa, 3498838, Israel.

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Abstract

Autonomous robots can transform how we observe marine ecosystems, but close-range operation in reefs and other cluttered habitats remains difficult. Vehicles must maneuver safely near animals and fragile structures while coping with currents, variable illumination and limited sensing. Previous approaches simplify these problems by leveraging soft materials and bioinspired swimming designs, but such platforms remain limited in terms of deployable autonomy. Here we present a sea turtle-inspired autonomous underwater robot that closed the gap between bioinspired locomotion and field-ready autonomy through a tightly integrated, vision-driven control stack. The robot combines robust depth-heading stabilization with obstacle avoidance and target-centric control, enabling it to track and interact with moving objects in complex terrain. We validate the robot in controlled pool experiments and in a live coral reef exhibit at the New England Aquarium, demonstrating stable operation and reliable tracking of fast-moving marine animals and human divers. To the best of our knowledge, this is the first integrated biomimetic robotic system, combining novel hardware, control, and field experiments, deployed to track and monitor real marine animals in their natural environment. During off-tether experiments, we demonstrate safe navigation around obstacles (91% success rate in the aquarium exhibit) and introduce a low-compute onboard tracking mode. Together, these results establish a practical route toward soft-rigid hybrid, bioinspired underwater robots capable of minimally disruptive exploration and close-range monitoring in sensitive ecosystems.

Figures & tables

Figure 1: Sea turtle–inspired robot for autonomous marine observation. a, A photograph of the turtle robot swimming in the New England Aquarium in Boston, MA. b, The turtle robot uses biomimetic soft-rigid flippers and visual feedback for tracking sea animals autonomously. c, Superimposed frames of robot swimming autonomously and untethered around coral in aquarium tank (the orange line denotes the path of the robot).
Figure 2: Autonomous animal following. a, Time series showing the robot’s point of view (POV) with target segmentation masks (green overlays) during autonomous sea turtle tracking over ∼\sim 12 seconds. b, Target centroid trajectories throughout a minute-long tracking sequence, with corner indices marking trajectory extremities (top left, top right, bottom left, and bottom right). c, Additional tracking examples: stingray track ending when the animal dived beneath the robot, barracuda track maintaining target lock despite specular reflections, and sustained sea turtle pursuit. d, Tracking pipeline: stereo cameras are stitched into a wide-field view, segmented to extract target centroid, which drives autonomous swimming control.
Animal TypeTrack durationFailedEnd Track Reason  
 (secs)    
Turtle59Nostopped swimming a  
Turtle57Nostopped swimming a  
Turtle43Novisibility  
Turtle38Nocoral collision b  
Stingray32Noswam over robot  
Table 1: Analyses of several aquarium tracks
Figure 3: Autonomous underwater obstacle avoidance. Top, Depth over time showing the robot maintaining a target depth of 1.1 m (RMSE = 0.195 m, MAE = 0.134 m). Bottom, Obstacle distance estimates over time with the 2.5 m avoidance threshold (dashed); yellow and blue shaded regions indicate right and left turn commands, respectively. Annotated time points highlight three representative avoidance events—coral reef (26 s), glass wall (68 s), and a diver (320 s)—each shown with the robot’s stereo camera view (Robot POV), the corresponding stereo depth map, and an external observer perspective.
Figure S1: System Diagram of Crush’s Autonomous Control and Tasking: the robot is equipped with cameras and several sensors, allowing it to operate autonomously with manual override and high-level tasking capability.
Figure S2: Robotic Sea Turtle Characterization: a) Image sequence showing the robot’s forward swimming trajectory in a pool environment. b) Underwater view of the robot during a diving maneuver, demonstrating flipper-based propulsion and pitch control. c) Motor kinematics during steady swimming: i) joint positions showing synchronized oscillatory flipper motion across all 10 motors (front and rear flippers), and ii) corresponding motor velocities demonstrating consistent stroke patterns. d) Performance characterization across N=5 trials: i) Forward swimming speed showing mean position ± 1 standard deviation over 30 seconds, ii) diving behavior showing depth excursion of approximately 1.5 body lengths with controlled ascent and descent phases, and iii) power consumption profile during steady swimming with mean power of 24.0 W yielding a cost of transport of 1.18 (dimensionless). The robot demonstrates repeatable locomotion with low trial-to-trial variability in both forward swimming and diving maneuvers.
Figure S3: Three untethered obstacle avoidance trials at the New England Aquarium (132 s, 94 s, and 341 s; left to right). For each trial, depth over time (top) and estimated obstacle distance (bottom) are shown, with yellow and blue shaded regions indicating right and left turn commands, respectively, and red shaded regions marking diver interference events. The robot maintained a 1.1 m target depth across all trials (RMSE = 0.213, 0.266, and 0.195 m; MAE = 0.135, 0.158, and 0.134 m) while executing 10, 7, and 28 avoidance maneuvers per trial, respectively. Across all three trials, the robot autonomously avoided 41 of 45 detected obstacles (91.1% success rate, 113.5 s per intervention), with the three wall-collision failures attributable to the glass tank environment rather than reef-relevant obstacles.
Figure S4: Untethered toy turtle tracking over a 32-second window of a 3 min 34 s trial. Top row: external observer view of the robot pursuing the toy turtle. Bottom row: onboard stereo camera view with the FOMO-detected centroid (red dot) used by the motion planner to drive the robot’s motor commands in real time.
Figure S5: Tetherless Tracking Trials. Top-down view of the autonomous tracking robot pursuing a toy turtle target across multiple trials in the MIT Sea Grant testing tank. Each semi-transparent silhouette represents the robot at successive positions during left, right, and center target trials. The glass wall at the center of the tank presented detection challenges, requiring the target to be repositioned closer to the robot for reliable model detection.
Animal TypeTrack durationFailedEnd Track Reason  
 (secs)    
Turtle59Nostopped swimming  
Turtle57Nostopped swimming  
Turtle43Novisibility  
Turtle38Nocoral collision b  
Turtle36Nodepth limitation and visibility  
Table S1: Analyses of pool and aquarium tracks
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