Artificial lateral line based local sensing between two adjacent robotic fish
Source abstract
Abstract
The lateral line system (LLS) is a mechanoreceptive organ system with which fish and aquatic amphibians can effectively sense the surrounding flow field. The reverse Kármán vortex street (KVS), known to be a typical thrust-producing wake, is commonly observed in fish-like locomotion and is known to be generated by fish's tails. The vortex street generally reflects the motion information of the fish. A fish can use LLS to detect such vortex streets generated by its neighboring fish, thus sensing its own state and the states of its neighbors in a school of fish. Inspired by this typical biological phenomenon, we design a robotic fish with an onboard artificial lateral line system (ALLS) composed of pressure sensor arrays and use it to detect the reverse KVS-like vortex wake generated by its adjacent robotic fish. Specifically, the vortex wake results in hydrodynamic pressure variations (HPVs) in the flow field. By measuring the HPV using the ALLS and extracting meaningful information from the pressure sensor readings, the oscillating frequency/amplitude/offset of the adjacent robotic fish, the relative vertical distance and the relative yaw/pitch/roll angle between the robotic fish and its neighbor are sensed efficiently. This work investigates the hydrodynamic characteristics of the reverse KVS-like vortex wake using an ALLS. Furthermore, this work demonstrates the effectiveness and practicability of an artificial lateral line in local sensing for adjacent underwater robots, indicating the potential to improve close-range interaction and cooperation within a group of underwater vehicles through the application of ALLSs in the near future.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Underwater Vehicles and Communication Systems · Marine animal studies overview · Fish Ecology and Management Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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