Swimming with the Machine: A New Approach to Underwater Exploration

by

Author: Denis Avetisyan

This research details the development of a bio-inspired, underactuated robot designed for efficient and simplified underwater navigation.

The initial prototype demonstrates a design for tail oscillation, a mechanism subject to the inevitable entropy of repeated motion yet poised to reveal the delicate balance between controlled movement and eventual degradation.
The initial prototype demonstrates a design for tail oscillation, a mechanism subject to the inevitable entropy of repeated motion yet poised to reveal the delicate balance between controlled movement and eventual degradation.

Researchers present a biomimetic, fish-like robot controlled via reinforcement learning, utilizing underactuation to streamline control and enable effective ecosystem monitoring.

Effective underwater ecosystem monitoring demands robust and adaptable robotic platforms, yet traditional designs often rely on complex and energy-intensive actuation systems. This paper, ‘Underactuated Biomimetic Autonomous Underwater Vehicle for Ecosystem Monitoring’, introduces a novel fish-like robot employing underactuation – simplifying control by actuating only one spinal joint – to achieve efficient locomotion. Preliminary results demonstrate the feasibility of this biomimetic approach, leveraging reinforcement learning for autonomous navigation within a simulated environment. Could this simplified control architecture pave the way for more sustainable and effective underwater exploration and data collection?

The Echo of Evolution: Biomimicry in Aquatic Robotics

Traditional underwater robots often rely on complex propulsion systems like propellers, limiting maneuverability and hydrodynamic efficiency, particularly in confined or turbulent environments. Biological systems, however, demonstrate a contrasting approach. Fish, for example, achieve remarkable efficiency and adaptability through streamlined shapes and flexible, undulating movements. Mimicking these principles offers a pathway to a new generation of underwater robots capable of navigating with greater efficiency and dexterity, crucial for tasks such as exploration, monitoring, and inspection. Every mechanical failure is a signal from time; the pursuit of graceful aquatic robotics is not merely about building machines, but about understanding the elegant dialogue between form and flow.

Carangiform Locomotion: Harnessing the Power of the Posterior

These robots utilize ‘Carangiform Swimming,’ a mode of locomotion where the posterior third of the body generates most of the propulsive thrust, inspired by efficient fish like jacks and tuna. An ‘Under-Actuated Robot’ design minimizes complexity and energy consumption by reducing actuated spine joints from 44 to a single actuator, with the remaining 33 functioning passively. This streamlines control and reduces weight. A ‘Cable-Driven Oscillating System’ translates motor input into fish-like tail motion, and ‘Passive Compliance’ in the tail structure further enhances adaptability in fluid environments, allowing navigation of complex underwater terrains.

Learning to Swim: Reinforcement Learning for Autonomous Navigation

The robot’s control system is trained using Reinforcement Learning, enabling it to develop effective swimming strategies through trial and error, adapting to the complexities of underwater locomotion without explicit pre-programming. To improve learning efficiency, Action Space Reduction techniques decreased control points from four to one without compromising performance. Training simulations are conducted within the FishGym Simulator, a physics-based environment that accurately models underwater fluid dynamics, assessing performance using metrics like Cruising Efficiency, Path Precision, and Pose Control.

Extending the Reach: Applications and the Future of Underwater Robotics

Recent advancements in biomimetic robotics have yielded underwater vehicles inspired by aquatic animal morphology and locomotion, demonstrating superior maneuverability and efficiency compared to traditional ROVs and AUVs. These robots, often utilizing undulating fin or jet propulsion, are well-suited for ‘Underwater Exploration’ tasks, including environmental monitoring, infrastructure inspection, and search and rescue. Their inherent maneuverability allows access to confined spaces and challenging environments. This research paves the way for a new generation of efficient, adaptable, environmentally friendly, and cost-effective underwater robots. Future work will focus on enhancing sensing capabilities and developing autonomous decision-making algorithms. Stability, it seems, is merely temporary, cached by the currents of innovation.

The pursuit of simplified control schemes, as demonstrated by this research into underactuated biomimetic robots, echoes a fundamental principle of enduring systems. The design, limiting actuation to a single spinal joint, isn’t about achieving perfect mimicry, but rather accepting inherent limitations and optimizing within them. As John von Neumann observed, “The best way to predict the future is to invent it.” This approach doesn’t seek to prevent the inevitable decay of complex control structures, but to build a system resilient enough to navigate its limitations gracefully. The work acknowledges that stability is a transient state, and focuses on robust performance despite underactuation, embracing latency as an unavoidable component of interaction with the environment.

What’s Next?

The pursuit of biomimicry in robotics invariably reveals not how close systems are to replication, but how fundamentally different the constraints are. This work, elegantly simplifying propulsion through underactuation, is not a step toward a living system, but a distillation of its essential mechanics for a different kind of existence. Versioning, in this case, is a form of memory – each iteration acknowledging the gap between organic fluidity and engineered approximation. The challenge now lies not in adding complexity, but in understanding which complexities are vestigial – the unnecessary weight of evolved history.

The reliance on reinforcement learning, while effective, underscores a critical point. The robot learns within a simulated environment, then translates that learning to the real world. This is a bridge built across an inherent discontinuity. Future work must confront the problem of embodiment more directly, recognizing that the ‘truth’ of the environment is always mediated by sensors, and therefore, always a constructed reality.

Ultimately, the arrow of time always points toward refactoring. This design, like all designs, will accrue limitations. The next generation will not be defined by mirroring nature more closely, but by accepting the inevitability of decay and building systems that age gracefully—that can adapt, reconfigure, and even forget with a degree of autonomy. The true metric isn’t fidelity, but resilience.

Original article: https://arxiv.org/pdf/2511.06578.pdf

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

See also:

2025-11-11 13:42