Underwater Biomimetic Lateral Line Sensor Based on Triboelectric Nanogenerator for Dynamic Pressure Monitoring and Trajectory Perception.

Developing desirable sensors is crucial for underwater perceptions and operations. The perceiving organs of marine creatures have greatly evolved to react accurately and promptly underwater. Inspired by the fish lateral line, this study proposes a triboelectric dynamic pressure sensor for underwater perception. The biomimetic lateral line sensor (BLLS) has high sensitivity to the disturbance amplitude/frequency, good adaptability to underwater environments and (relative) low cost. The sensors are deployed at the bottom of the test basin to perceive various moving objects, such as a robotic fish, robotic seal, etc. By analyzing the electrical signal of the sensor, the motion parameters of the objects passed over can be obtained. By monitoring signal variations across multiple sensors, the ability to sense different disturbance movement trajectories, including linear and angular trajectories, is achievable. The study will prove significant in forming an unconventional underwater perceiving method, which can back-up the sonic/optical sensors when are impaired in complex underwater environments.

Deep-Learning-Assisted Underwater 3D Tactile Tensegrity.

The growth of underwater robotic applications in ocean exploration and research has created an urgent need for effective tactile sensing. Here, we propose an underwater 3-dimensional tactile tensegrity (U3DTT) based on soft self-powered triboelectric nanogenerators and deep-learning-assisted data analytics. This device can measure and distinguish the magnitude, location, and orientation of perturbations in real time from both flow field and interaction with obstacles and provide collision protection for underwater vehicles operation. It is enabled by the structure that mimics terrestrial animals' musculoskeletal systems composed of both stiff bones and stretchable muscles. Moreover, when successfully integrated with underwater vehicles, the U3DTT shows advantages of multiple degrees of freedom in its shape modes, an ultrahigh sensitivity, and fast response times with a low cost and conformability. The real-time 3-dimensional pose of the U3DTT has been predicted with an average root-mean-square error of 0.76 in a water pool, indicating that this developed U3DTT is a promising technology in vehicles with tactile feedback.