Towards Reducing the Impact of Localisation Errors on the Behaviour of a Swarm of Autonomous Underwater Vehicles
Localisation errors have a great impact on Autonomous Underwater Vehicles (AUVs) as search agents. Different approaches for solving the localisation problem can be used and combined together for greater accuracy in estimating AUVs’ locations. The effect of localisation errors on locating a target can be lightened by designing a search algorithm that avoids extensive use of exact lo-cation information. In this paper, two cooperative search algorithms are proposed and evaluated. In these algorithms, a high-level mechanism is employed for building a global view of the search space using minimum possible search information. These algorithms rely on low-level search algorithms with exploring roles. Particle Swarm Optimisation (PSO) and all-to-one Self-Organising Migrating Algorithm (SOMA) are selected as high-level mechanisms. The conducted experiments demonstrate that both algorithms show a robust behaviour within a range of localisation errors.
Akawwi, E. J. Locating Zones and Quantify the Submarine Groundwater Discharge into the Eastern Shores of the Dead Sea-Jordan. PhD thesis, Gottingen Uni, Germany, 2006.
Curey, R. K., Ash, M. E., Thielman, L. O., and Barker, C. H. Proposed ieee inertial systems terminology standard and other inertial sensor standards. In PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556) (2004), pp. 83-90.
El-Mihoub, T., Tholen, C., and Nolle, L. Informed search patterns for alleviating the impact of the localisation problem. In 33rd. European Conference on Modelling and Simulation ECMS 2019 (Caserta, Italy, 2019), pp. 1-6.
Gordon, N. J., Salmond, D. J., and Smith, A. F. M. Novel approach to nonlinear/nongaussian bayesian state estimation. IEE Proceedings F - Radar and Signal Processing 140, 2 (1993), 107-113.
Kalman, R. E. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering 82, 1 (03 1960), 35-45.
Leonard, J. J., and Durrant-Whyte, H. F. Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation 7, 3 (1991), 376-382.
Matousek, R., Popela, P., and Kudela, J. Heuristic approaches to stochastic quadratic assignment problem: Var and cvar cases. MENDEL 23, 1 (Jun. 2017), 73-78.
Moore, W. S. The effect of submarine groundwater discharge on the ocean. Annual Review of Marine Science 2, 1 (2010), 59-88.
Nelson, C. E., Donahue, M. J., Dulaiova, H., Goldberg, S. J., La Valle, F. F., Lubarsky, K., Miyano, J., Richardson, C., Silbiger, N. J., and Thomas, F. I. Fluorescent dissolved organic matter as a multivariate biogeochemical tracer of submarine groundwater discharge in coral reef ecosystems. Marine Chemistry 177 (2015), 232-243.
Nolle, L. On a search strategy for collaborating autonomous underwater vehicles. Mendel (2015), 159-164.
Prasser, D., and Dunbabin, M. Sensor network based auv localisation. In Field and Service Robotics (Berlin, Heidelberg, 2010), A. Howard, K. Iagnemma, and A. Kelly, Eds., Springer Berlin Heidelberg, pp. 285-294.
Rigby, P., Pizarro, O., and Williams, S. B. Towards geo-referenced auv navigation through fusion of usbl and dvl measurements. In OCEANS 2006 (2006), pp. 1-6.
Stutters, L., Liu, H., Tiltman, C., and Brown, D. J. Navigation technologies for autonomous underwater vehicles. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38, 4 (2008), 581-589.
Tholen, C., El-Mihoub, T. A., Nolle, L., and Zielinski, O. On the robustness of self-adaptive levy-flight. In 2018 OCEANS -MTS/IEEE Kobe Techno-Oceans (OTO) (2018), pp. 1-5.
Tholen, C., and Nolle, L. Parameter search for a small swarm of auvs using particle swarm optimisation. In Articial Intelligence XXXIV (Cham, 2017), M. Bramer and M. Petridis, Eds., Springer International Publishing, pp. 384-396.
Tholen, C., Nolle, L., and Werner, J. On the influence of localisation and communication error on the behaviour of a swarm of autonomous underwater vehicles. In Recent Advances in Soft
Computing (Cham, 2019), R. Matousek, Ed., Springer International Publishing, pp. 68-79.
Tvrdik, J. Competition and cooperation in evolutionary algorithms: A comparative study. In MENDEL 2005, 11-th Int. Conference on Soft Computing (2005), pp. 108-113.
Whitcomb, L., Yoerger, D., and Singh, H. Advances in doppler-based navigation of underwater robotic vehicles. In Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C) (1999), vol. 1, pp. 399-406.
Zelinka, I. SOMA | Self-Organizing Migrating Algorithm. Springer Berlin Heidelberg, Berlin, Heidelberg, 2004, pp. 167-217.
Zielinski, O., Busch, J., Cembella, A., Daly, K., Engelbrektsson, J., Hannides, A. K., and Schmidt, H. Detecting marine hazardous substances and organisms: sensors for pollutants, toxins, and pathogens. Ocean Sci. 5 (September 2009), 329-349.
Copyright (c) 2020 MENDEL
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
MENDEL open access articles are normally published under a Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ . Under the CC BY-NC-SA 4.0 license permitted 3rd party reuse is only applicable for non-commercial purposes. Articles posted under the CC BY-NC-SA 4.0 license allow users to share, copy, and redistribute the material in any medium of format, and adapt, remix, transform, and build upon the material for any purpose. Reusing under the CC BY-NC-SA 4.0 license requires that appropriate attribution to the source of the material must be included along with a link to the license, with any changes made to the original material indicated.