Abstract
AbstractAutonomous underwater vehicles (AUVs) are robots that operate in underwater environment and do not need involvement of an operator when performing some tasks. In order to move independently in water environment, AUVs need navigation capabilities, on the one hand, they have to be able to detect obstacles and avoid them, and on the other hand, they also have to know their own position and spatial orientation, at least course. With regard to the orientation, there are many various solutions like inertial systems, inclinometers, magnetic compasses, optical gyro–compasses, whereas, position due to unavailability of GPS requires solutions dedicated to underwater environment such as inertial navigation. To this end, information about spatial orientation and velocity is necessary. When the vehicle is not equipped with a device to measure velocity, e.g. because of small size of the vehicle itself, the only solution is to use odometry, that is, to apply information from the drive to estimate the velocity. The paper presents Odometric Navigational System (ONS) designed for a small biomimetic autonomous underwater vehicle (BAUV) and tuned by means of neuro–evolutionary techniques. To verify system performance, data from the real BAUV were applied.
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Artificial Intelligence,Industrial and Manufacturing Engineering,Mechanical Engineering,Control and Systems Engineering,Software
Reference28 articles.
1. Bao, J., Li, D., Qiao, X., Rauschenbach, T.: Integrated navigation for autonomous underwater vehicles in aquaculture: A review, Information Processing in Agriculture, Available online 11 April 2019, In Press (2019)
2. Ben, Y., Zang, X., Li, Q., Liu, X., Chen, H.: System reset for underwater strapdown inertial navigation system. Ocean Eng. 182, 552–562 (2019)
3. Chen, L., Wang, S., Hu, H.: Pose–based GraphSLAM algorithm for robotic fish with a mechanical scanning sonar. In: IEEE international conference on robotics and biomimetics (ROBIO), pp 38–43 (2013)
4. Dinc, M., Hajiyev, C.: Integration of navigation systems for autonomous underwater vehicles. J. Marine Eng. Technol. 14(1), 32–43 (2015)
5. Einicke, G.A., White, L.B.: Robust extended kalman filtering. IEEE Trans. Signal Process. 47(9), 2596–2599 (1999). https://doi.org/10.1109/78.782219
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