Swimming with the Robots: A Bioinspired Approach to Underwater Exploration

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Author: Denis Avetisyan

Researchers have developed an autonomous underwater vehicle modeled after sea turtles, enabling agile navigation and tracking in challenging marine environments.

The autonomous underwater vehicle, inspired by sea turtle locomotion and utilizing biomimetic soft-rigid flippers coupled with visual feedback, demonstrates the capacity for untethered navigation and tracking of marine life within complex environments like coral reefs, embodying a graceful adaptation to the challenges of underwater observation-a system designed not to resist decay, but to navigate it.
The autonomous underwater vehicle, inspired by sea turtle locomotion and utilizing biomimetic soft-rigid flippers coupled with visual feedback, demonstrates the capacity for untethered navigation and tracking of marine life within complex environments like coral reefs, embodying a graceful adaptation to the challenges of underwater observation-a system designed not to resist decay, but to navigate it.

This review details the design and implementation of ‘Crush,’ a soft robotic platform demonstrating advancements in bioinspired robotics, sensor fusion, and autonomous underwater fieldwork.

Observing marine ecosystems remains challenging due to the difficulty of navigating complex, fragile environments without disrupting natural behaviors. This limitation motivates the research presented in ‘Autonomous Sea Turtle Robot for Marine Fieldwork’, which introduces Crush, a bioinspired underwater vehicle designed for agile maneuvering and close-range interaction. Through a tightly integrated hardware and vision-driven control stack, Crush demonstrates robust obstacle avoidance and reliable tracking of marine animals in both controlled and live reef environments. Could this approach pave the way for minimally disruptive, long-term monitoring of sensitive ecosystems using autonomous, bioinspired robots?

The Inevitable Drift: Redefining Underwater Robotics

Conventional underwater robots frequently employ rigid body designs and rely on substantial computational power for even basic maneuvers, a characteristic that significantly restricts their agility in complex, real-world environments. These systems often struggle to navigate confined spaces or adapt to unpredictable currents due to the limitations imposed by their inflexible structures and the processing demands of their control algorithms. The need for precise calculations to counteract every disturbance, combined with the physical constraints of rigid materials, results in slow response times and limited maneuverability – hindering the robot’s ability to efficiently explore, inspect, or interact within challenging underwater landscapes. This reliance on brute force computation and inflexible hardware presents a significant bottleneck in the development of truly autonomous and versatile underwater vehicles.

Current underwater navigation and obstacle avoidance systems frequently falter when faced with the unpredictable nature of real-world aquatic environments. Traditional sonar and camera-based approaches require significant processing power to interpret data and react to changes, rapidly depleting onboard energy reserves and restricting operational duration. The inherent delays in processing and the difficulty in distinguishing between static and dynamic obstacles – a swirling sediment cloud versus a school of fish, for example – contribute to inefficient path planning and increased risk of collision. This struggle to adapt to dynamic scenarios severely limits the effective range of these robots, hindering their ability to perform sustained, long-distance surveys or complex tasks like pipeline inspection and underwater archaeology. Consequently, a more robust and energy-efficient solution is needed to enable truly autonomous underwater exploration.

The inherent difficulties facing traditional underwater robotics – stemming from rigid designs and computationally demanding algorithms – are driving a paradigm shift towards bio-inspired designs. Researchers are increasingly looking to the streamlined efficiency of aquatic animals, such as fish and cephalopods, to overcome limitations in maneuverability and navigation. This biomimicry extends beyond simply replicating external forms; it encompasses the undulating movements, jet propulsion, and sensory systems that allow marine life to thrive in complex, dynamic environments. By emulating these natural strategies, engineers aim to create underwater robots capable of greater agility, reduced energy consumption, and enhanced operational range – ultimately unlocking new possibilities for exploration, inspection, and intervention in the world’s oceans.

The robotic sea turtle exhibits repeatable and consistent underwater locomotion, achieving a forward swimming speed of [latex]1.18[/latex] cost of transport with a mean power consumption of 24.0 W, alongside controlled diving maneuvers reaching approximately 1.5 body lengths in depth, as demonstrated by synchronized flipper motion and low trial-to-trial variability.
The robotic sea turtle exhibits repeatable and consistent underwater locomotion, achieving a forward swimming speed of [latex]1.18[/latex] cost of transport with a mean power consumption of 24.0 W, alongside controlled diving maneuvers reaching approximately 1.5 body lengths in depth, as demonstrated by synchronized flipper motion and low trial-to-trial variability.

Embracing Fluidity: A Biomimetic Approach to Underwater Autonomy

The Crush Robot is an autonomous underwater vehicle (AUV) developed utilizing biomimicry, specifically drawing design inspiration from the morphology and locomotion patterns of sea turtles. This approach prioritizes both agility – the ability to maneuver in complex environments – and energy efficiency during operation. The robot’s overall form and its method of propulsion are modeled after the streamlined body and flipper-driven movements of sea turtles, allowing for a reduction in drag and improved hydrodynamic performance. This design philosophy aims to overcome limitations found in traditional AUVs, which often sacrifice maneuverability for speed or endurance, and allows the Crush Robot to operate effectively in confined or obstacle-rich underwater settings.

The Crush Robot utilizes hydrofoil propulsion in conjunction with a novel gait control system to facilitate navigation in confined spaces and effective obstacle avoidance. Testing demonstrated a 91.1% success rate in obstacle avoidance, achieved through 41 successful maneuvers out of 45 detected obstacles. This system allows the robot to adjust its flipper movements, optimizing for both speed and precision, and enabling it to circumvent barriers without relying on complex path-planning algorithms. The hydrofoil design contributes to maneuverability by generating lift during movement, reducing drag and increasing responsiveness to control inputs.

The Crush Robot’s biomimetic design significantly reduces computational demands compared to conventional underwater vehicle control systems. By replicating the efficient hydrodynamics of sea turtle locomotion, the robot requires fewer calculations for maneuver planning and stabilization. This reduction in processing load enables real-time autonomous operation, a critical factor for dynamic environments and extended deployments. Testing has demonstrated the robot’s capability to track marine animals continuously for up to 341 seconds, indicating sustained performance within acceptable computational constraints and validating the efficiency gains achieved through the biomimetic approach.

Crush operates autonomously using onboard cameras and sensors, while also allowing for manual override and high-level tasking.
Crush operates autonomously using onboard cameras and sensors, while also allowing for manual override and high-level tasking.

Perceiving the Submerged World: A System for Robust Navigation

Stereo vision on the robot utilizes two cameras to capture slightly different perspectives of the environment, mimicking human binocular vision. By calculating the disparity – the difference in pixel locations of corresponding points in the two images – the system can triangulate the distance to objects, generating a depth map. This depth map provides a 3D representation of the surroundings, enabling accurate obstacle detection and avoidance. The resulting data is critical for path planning algorithms, allowing the robot to navigate complex environments by identifying navigable space and adjusting trajectories to circumvent obstructions. The system’s ability to perceive depth is independent of lighting conditions, enhancing robustness in variable environments.

The robot utilizes a visual tracking system composed of the CUTIE Tracker and the Segment Anything Model (SAM) to identify and follow objects within its environment. The CUTIE Tracker provides robust tracking capabilities, while SAM, a foundational vision model, enables the system to segment and recognize a wide variety of objects without requiring specific training for each instance. This combination allows the robot to dynamically identify objects of interest and maintain tracking throughout a sequence, facilitating tasks such as following specific sea animals or monitoring moving obstacles. The system is designed to operate in real-time, providing continuous tracking data for navigation and interaction.

The implementation of FOMO (Few-shot Object Matting and One-stage detection) provides the robot with onboard, real-time object detection capabilities. This model is specifically designed for low-power devices, allowing it to run efficiently on the Raspberry Pi 5 without compromising operational speed. FOMO’s lightweight architecture enables the robot to process visual data and identify objects with minimal latency, which is critical for responsive navigation and interaction within dynamic environments. The model’s efficiency is achieved through a streamlined detection pipeline and optimized computational graph, facilitating timely inference for real-world applications.

Trajectory tracking performance was evaluated by following the paths of 20 distinct sea animal tracks. The robot demonstrated an average track time of 25.85 seconds, indicating its ability to maintain course despite external factors like currents or disturbances. The standard deviation of 14.7 seconds across these trials reflects the variability in tracking performance, potentially due to differing environmental conditions or the complexity of each animal’s movement pattern. This data suggests a robust system capable of adhering to desired paths in dynamic underwater environments.

During a 32-second segment of a longer trial, the robot autonomously tracked a moving toy turtle using a combination of external observation and onboard stereo vision, with the detected centroid [latex] ext{(red dot)}[/latex] guiding its motor commands in real time.
During a 32-second segment of a longer trial, the robot autonomously tracked a moving toy turtle using a combination of external observation and onboard stereo vision, with the detected centroid [latex] ext{(red dot)}[/latex] guiding its motor commands in real time.

The Ebb and Flow of Progress: Expanding the Horizon of Underwater Exploration

The development of the Crush Robot signifies a notable advancement in underwater technology by embracing biomimetic principles. Unlike conventional remotely operated vehicles (ROVs) that rely on propellers and rigid structures, the Crush Robot mimics the fluid movements of marine animals, allowing it to navigate complex underwater environments with greater agility and efficiency. This innovative design, inspired by the body plan of jellyfish and other invertebrates, overcomes the limitations of traditional systems, particularly in confined spaces and delicate ecosystems. By distributing force across a flexible body, the robot minimizes disturbance while maximizing maneuverability, opening possibilities for detailed inspection of coral reefs, exploration of shipwrecks, and access to previously unreachable areas of the ocean floor. This approach promises a future where underwater robots can seamlessly integrate into marine environments, providing unprecedented insights into the planet’s aquatic realms.

The Crush Robot’s unique design lends itself to a diverse array of practical applications beyond fundamental research. Its ability to navigate confined spaces and withstand significant pressure makes it ideally suited for detailed environmental monitoring in sensitive ecosystems, such as coral reefs or deep-sea hydrothermal vents, where traditional remotely operated vehicles (ROVs) struggle. Furthermore, the platform offers a novel approach to infrastructure inspection, capable of assessing the integrity of underwater pipelines, bridges, and other submerged structures with greater efficiency and precision. Perhaps most critically, the robot’s maneuverability and resilience also present opportunities for advancements in search and rescue operations, allowing for rapid assessment of wreckage or access to areas inaccessible to human divers, potentially increasing the chances of locating and assisting individuals in distress.

Ongoing development of the biomimetic robot prioritizes expanded operational capabilities through increased autonomy and range. Researchers are actively integrating advanced sensor modalities to provide richer data collection during underwater missions. Current performance metrics reveal a measured cost of transport of 1.173, indicating efficient movement relative to energy expenditure, and a forward swimming speed of 0.32 body lengths per second – a velocity allowing for effective surveying and detailed inspection. These improvements aim to transition the robot from a proof-of-concept to a reliable platform for extended underwater tasks, ultimately broadening its utility in areas like environmental monitoring and infrastructure assessment.

The pursuit of effective underwater exploration increasingly benefits from biomimicry, the innovation inspired by biological design and processes. Nature has already solved many of the engineering challenges inherent in aquatic environments – efficient locomotion, pressure resistance, and sensory perception – over millions of years of evolution. Researchers are now actively translating these natural solutions into robotic systems, moving beyond rigid, conventional designs towards more flexible and adaptable machines. This approach not only promises improved performance in challenging underwater conditions but also opens doors to novel exploration strategies, allowing robots to navigate complex environments, access previously unreachable areas, and gather data with greater precision and efficiency. Continued investigation into the intricacies of marine life will undoubtedly yield further breakthroughs, reshaping the future of underwater discovery and offering unprecedented insights into the world beneath the waves.

Autonomous tracking trials in the MIT Sea Grant testing tank demonstrate the robot's ability to follow a target across multiple paths, though reliable tracking near the central glass wall required target repositioning to maintain model detection.
Autonomous tracking trials in the MIT Sea Grant testing tank demonstrate the robot’s ability to follow a target across multiple paths, though reliable tracking near the central glass wall required target repositioning to maintain model detection.

The development of Crush, as detailed in the research, embodies a pragmatic acceptance of systemic imperfection. Like all complex systems, autonomous robots inevitably encounter unforeseen challenges and require iterative refinement. This mirrors the natural world, where adaptation, not flawless design, ensures survival. As Paul Erdős once stated, “A mathematician knows a lot of things, but he doesn’t know everything.” This sentiment applies directly to the engineering process; acknowledging limitations and embracing continual improvement-through sensor fusion and obstacle avoidance, for example-is crucial. The pursuit of robustness, as demonstrated by Crush’s agile maneuvering, isn’t about eliminating errors, but about gracefully accommodating them as the system matures within its operational medium-time.

The Current’s Pull

The architecture of Crush, as presented, is less a final form than a carefully considered provisional one. The successful imitation of biological locomotion, while demonstrably achieved, reveals the limitations inherent in translating fluid dynamics into rigid algorithmic control. Every delay in achieving true biomimicry is, in essence, the price of understanding-a necessary expenditure to avoid the brittle elegance of designs divorced from evolutionary pressures. The current iteration functions; the next must endure.

Further development will inevitably confront the issue of long-term deployment. Sensor degradation, material fatigue, and the accumulation of biofouling are not bugs to be fixed, but intrinsic properties of any system existing within a dynamic environment. The true test of this work will not be initial performance, but graceful degradation-the ability to maintain functionality, even diminished, over extended periods.

The promise of autonomous marine fieldwork hinges not simply on replicating what marine animals do, but how they do it-the subtle interplay of sensing, prediction, and reactive control honed over millennia. Architecture without this history is fragile and ephemeral. The field now faces the challenge of building not merely robots that resemble turtles, but systems that embody the principles of resilient, adaptable marine life.

Original article: https://arxiv.org/pdf/2602.21389.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-26 07:53