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Optimal Sensor Placement of the Artificial Lateral Line for Flow Parametric Identification.
Dong Xu1, Yuanlin Zhang1, Jian Tian1
1School of Automation Science and Electrical Engineering, Beihang University, No.37 Xueyuan Road, Beijing 100191, China.
Sensors (Basel, Switzerland)
|July 2, 2021
Summary
Optimizing sensor placement for artificial lateral line systems (ALLS) enhances underwater robotic fish control. This method improves flow-field parameter prediction for better perception and navigation.
Area of Science:
- Robotics
- Fluid Dynamics
- Sensor Technology
Background:
- Underwater robotic fish require accurate flow-field identification for effective control.
- Current artificial lateral line systems (ALLS) face challenges with sensor placement accuracy.
- Suboptimal sensor configuration can lead to imprecise flow-field parameter identification.
Purpose of the Study:
- To develop and validate a method for optimizing the sensor placement of ALLS.
- To improve the accuracy of flow-field parameter identification for robotic fish control.
- To enhance the perception and control capabilities of underwater robotic systems.
Main Methods:
- Proposed an optimization method for ALLS sensor configuration.
- Utilized feature importance algorithms: mean and variance (MVF) and distance evaluation (DF).
- Incorporated an information redundancy (IR) algorithm to refine sensor placement.
Main Results:
- Simulation and experimental verification confirmed the effectiveness of the proposed method.
- Optimal sensor placement significantly improved flow-field parameter prediction accuracy.
- The optimized ALLS outperformed uniform sensor configurations in experimental tests.
Conclusions:
- The developed sensor placement optimization method enhances ALLS performance.
- Improved flow-field identification strengthens underwater robotic fish perception and control.
- This research contributes to more capable and efficient underwater robotic systems.
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