Abstract
Autonomous 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.
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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)
Ben, Y., Zang, X., Li, Q., Liu, X., Chen, H.: System reset for underwater strapdown inertial navigation system. Ocean Eng. 182, 552–562 (2019)
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)
Dinc, M., Hajiyev, C.: Integration of navigation systems for autonomous underwater vehicles. J. Marine Eng. Technol. 14(1), 32–43 (2015)
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
Goldberg, D. E.: Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading, Massachusetts (1989)
Gustafsson, F., Hendeby, G.: Some relations between extended and some relations between filters. IEEE Trans. Signal Process. 60(2), 545–555 (2012)
Hegrenaes, O., Berglund, E.: Doppler water–track aided inertial navigation for autonomous underwater vehicles. In: Proceedings of the oceans conference and exhibition, Bremen, Germany (2009)
Huang, L., He, B., Zhang, T.: An autonomous navigation algorithm for underwater vehicles based on inertial measurement units and sonar, 2nd International Asia Conference on Informatics in Control Automation and Robotics (2010)
Kalman, R. E.: Contributions to the theory of optimal control. Bol. Soc. Mat. Mexicana: 110, 102–119 (1960)
Kalman, R. E., Bucy, R. S.: New results in linear filtering and prediction theory. J. Basic Eng. 83(1), 95–108 (1961)
Li, J.H., Lee, M.J., Kim, J.G., Park, S.K., Park, S. H., Suh, J. H.: Guidance and control of P–SURO II hybrid AUV. In: Proceedings of the OCEANS MTS/IEEE conference, 1–6 (2012)
Liu, X., Xu, X., Liu, Y., Wang, L.: Kalman filter for cross–noise in the integration of SINS and DVL. Math. Probl. Eng. 2014, 1–8 (2014)
Malec, M., Morawski, M., Zajac, J.: Fish–like swimming prototype of mobile underwater robot. J. Auto. Mobile Robot. Intell. Syst. 4(3), 25–30 (2010)
Mallios, A., Ridao, P., Petillot, Y. R.: EKF–SLAM for AUV navigation under probabilistic sonar scan–matching. In: IEEE/RSJ international conference on intelligent robots and systems, pp 4404–4411 (2010)
Miller, P. A., Farrell, J., Zhao, Y., Djapic, V.: Autonomous underwater vehicle navigation. IEEE J. Ocean. Eng. 35(3), 663–678 (2010)
Potter, M.: The design and analysis of a computational model of cooperative coevolution, PhD thesis, George Mason University, Fairfax, Virginia (1997)
Potter, M. A., De Jong, K. A.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)
Praczyk, T., Szymak, P.: Decision system for a team of autonomous underwater vehicles – preliminary report. Neurocomputing 74(17), 3323–3334 (2011)
Praczyk, T.: Neural anti–collision system for autonomous surface vehicle. Neurocomputing 149(Part B, 3), 559–572 (2015)
Praczyk, T.: Assembler encoding with evolvable operations. Comput. Methods Sci. Technol. 21(3), 123–139 (2015)
Praczyk, T.: Correction of navigational information provided for biomimetic autonomous underwater vehicle. Polish Marit. Res. 25(1/2018), 13–24 (2018)
Rahman, S., Quattrini Li, A., Rekleitis, I. M.: SVIn2: Sonar visual–inertial slam with loop closure for underwater navigation. arXiv:1810.03200v1
Stutters, L., Liu, H., Tiltman, C., Brown, D. J.: Navigation technologies for autonomous underwater vehicles. IEEE Trans Syst. Man Cybern. Part C Appl. Rev. 38(4), 581–589 (2008)
Szymak, P., Praczyk, T., Naus, K., Malec, M., Morawski, M.: Research on biomimetic underwater vehicles for underwater ISR, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII. In: Kolodny, M.A., Pham, Tien (eds.) Proc. of SPIE Vol. 9831, 98310K (2016)
Szymak, P., Malec, M., Morawski, M.: Directions of development of underwater vehicle with undulating propulsion, Polish Journal of Environmental Studies. Hard Publishing Company 19(3), 107–110 (2010)
Yang, Y., Huang, G.: Acoustic–inertial underwater navigation. In: IEEE international conference on robotics and automation, pp 4927–4933 (2017)
Yuan, X., Martinez–Ortega, J.F., Fernandez, J. A. S., Eckert, M.: AEKF–SLAM: A new algorithm for robotic underwater navigation, Sensors, 17(5) (2017)
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The paper is supported by European Defense Agency project no. B-1452–GP entitled ”Swarm of Biomimetic Underwater Vehicle for Underwater ISR”.
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Appendix A: Appendix – Parameters of AEEO, ASNS, and ONS
Appendix A: Appendix – Parameters of AEEO, ASNS, and ONS
Parameters of AEEO: stopping criterion = position error lower than 1 meter or performing the maximum number of evolutionary generations equal to 60 000 (in the experiments, all evolutionary runs ended after performing 60 000 iterations), number of evolutionary generations without progress which were necessary to add a new population with ANN–operations = 10 000, probability of crossover = 0.7, probability of cut–splice = 0.1, probability of mutation = 0.06, size of tournament = 1, number of elite individuals = 1, number of subpopulations (ANN–operations) = 1–5, size of subpopulations = 50, no. of integer genes in chromosomes = 7–20, type of neurons in ANN–operations = <sigmoid, radial, linear, sinusoid, cosinusoid>, hidden neurons in ANN–operations = 4.
All the above parameters of AEEO were specified arbitrary before the experiments based on experience from all previous AEEO applications.
Estimated parameters of the ASNS: VM(la) = 0.5m/s, VM(m) = 1m/s, VM(b) = 0.2m/s, \(V_{M(la)}^{ST}=0.1m/s\), \(V_{M(la)}^{NT}=0.3m/s\), \(V_{M(m)}^{ST}=0.3m/s\), \(V_{M(m)}^{NT}=0.6m/s\).
Evolved parameters of the most accurate ONS: 𝜖0 = 52.0012, 𝜖1 = 68.8815, 𝜖2 = 22.8636, 𝜖3..𝜖10 = 0, e0 = − 4.9, e1 = − 9.8, e2 = 0, e3 = − 7.5, e4 = − 12.3, e5 = − 17.2, e6 = − 20.3, e7 = − 18.5, e8 = − 19.4, e9 = − 16.5, e10 = − 10.2, e11 = − 3.7, e12 = 0, e13 = 3.4, e14 = − 1.2, e15..e20 = 0, e21 = 4.2, e22 = 3.8, e23 = − 2.2, e24..e30 = 0, e31 = − 2.6, e32 = − 2.1, e33 = − 4.2, e34 = − 3.7, e35 = − 4.1, p0 = 0, p1 = 0.91, p2 = 0.91, p3 = 0.
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Praczyk, T. Using Neuro–Evolutionary Techniques to Tune Odometric Navigational System of Small Biomimetic Autonomous Underwater Vehicle – Preliminary Report. J Intell Robot Syst 100, 363–376 (2020). https://doi.org/10.1007/s10846-020-01191-3
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DOI: https://doi.org/10.1007/s10846-020-01191-3