Abstract
In recent decade years, AUG has been attached importance to oceanographic sampling tool. AUG is a buoyancy driven vehicle with low energy consumption, and capable of long-term and large-scale oceanographic sampling. However, ocean environment is characterized by variable and severe current fields, which jeopardizes AUG cruise. Therefore, an efficient path planning is a key point that can assist AUG to arrive at each waypoint and reduces the energy consumption to prolong AUG sampling time. To improve AUG cruise efficiency, a path planning framework with evolutionary computation is proposed to map out an optimal cruising path and increases AUG mission reachability in this work.
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References
Stommel, H.: The SLOCUM Mission. Oceanography 2(1), 22–25 (1989)
Curtin, T.B., Bellingham, J.G.: Autonomous Ocean-Sampling Networks. IEEE Journal of Oceanic Engineering 26, 421–423 (2001)
Eriksen, C.C., Osse, T.J., Light, R.D., Wen, T., Lehman, T.W., Sabin, P.L., Ballard, J.W.: Seaglider: A Long-Range Autonomous Underwater Vehicle for Oceanographic Research. IEEE Journal of Oceanic Engineering 26, 424–436 (2001)
Sherman, J., Davis, R., Owens, W.B., Valdes, J.: The Autonomous Underwater Glider “Spray”. IEEE Journal of Oceanic Engineering 26, 437–446 (2001)
Douglas, C.W., Paul, J.S., Clayton, P.J.: SLOCUM: An Underwater Glider Propelled by Environmental Energy. IEEE Journal of Oceanic Engineering 26, 447–452 (2001)
Alexander, V.I.: Underwater Vehicles. In-Tech, Vienna, Austria (2009)
Wikipedia, http://en.wikipedia.org/wiki/Dead_reckoning
Bäck, T., Hammel, U., Schwefel, H.P.: Evolutionary Computation: Comments on the History and Current State. IEEE Trans. on Evolutionary Computation 1, 3–17 (1997)
Gerry, D., Abdollah, H., Edward, T., Darryl, B.: An Introduction to Evolutionary Computation: Intelligent Control Systems Using Soft Computing Methodologies, ch. 17. CRC Press (2001)
Rudnick, D.L., Davis, R.E., Eriksen, C.C., Fratantoni, D.M., Perry, M.J.: Underwater Gliders for Ocean Research. Marine Technology Society Journal 38, 48–59 (2004)
Garau, B., Bonet, M., Álvarez, A., Ruiz, S., Pascual, A.: Path planning for autonomous underwater vehicles in realistic oceanic current fields: Application to gliders in the Western Mediterranean sea. Journal of Maritime Research 6, 5–21 (2009)
Shih, C.-C., Horng, M.-F., Pan, J.-S.: 3-D Adaptive Bearing Sampling for Route Planning in Extensible Ocean Model. In: COUTA 2012, pp. 71–84 (2012)
Jiang, S., Georgakopoulos, S.: Electromagnetic Wave Propagation into Fresh Water. Journal of Electromagnetic Analysis and Applications 3, 261–266 (2011)
John, H.H.: Adaption in Natural and Artificial Systems. MIT Press (1975)
Youngjun, A., Jiseong, P., Cheol-Gyun, L., Jong-Wook, K., Sang-Yong, J.: Novel Memetic Algorithm implemented With GA (Genetic Algorithm) and MADS (Mesh Adaptive Direct Search) for Optimal Design of Electromagnetic System. IEEE Transactions on Magnetics 46, 1982–1985 (2010)
Yin, W., Wenbo, L.: Routing protocol based on genetic algorithm for energy harvesting-wireless sensor networks. IET Wireless Sensor Systems 3, 112–118 (2013)
GuangJian, T., Yong, X., Zhang, Y., Dagan, F.: Hybrid Genetic and Variational Expectation-Maximization Algorithm for Gaussian-Mixture-Model-Based Brain MR Image Segmentation. IEEE Transactions on Information Technology in Biomedicine 15, 373–380 (2011)
Brinkman, W., Thayaparan, T.: Focusing inverse synthetic aperture radar images with higher-order motion error using the adaptive joint-time-frequency algorithm optimised with the genetic algorithm and the particle swarm optimisation algorithm - comparison and results. IET Signal Processing 4, 329–342 (2010)
Ip, W.H., Dingwei, W., Cho, V.: Aircraft Ground Service Scheduling Problems and Their Genetic Algorithm With Hybrid Assignment and Sequence Encoding Scheme. IEEE Systems Journal 7, 649–657 (2013)
Xiangning, L., Shuohao, K., Zhengtian, L., Hanli, W., Xionghui, H.: A Fault Diagnosis Method of Power Systems Based on Improved Objective Function and Genetic Algorithm-Tabu Search. IEEE Transactions on Power Delivery 25, 1268–1274 (2010)
Hsien-Chie, C., Chung, I.C., Wen-Hwa, C.: Thermal Chip Placement in MCMs Using a Novel Hybrid Optimization Algorithm. IEEE Transactions on Components, Packaging and Manufacturing Technology 2, 764–774 (2012)
Xuxun, L.: Sensor Deployment of Wireless Sensor Networks Based on Ant Colony Optimization with Three Classes of Ant Transitions. IEEE Communications Letters 16, 1604–1607 (2012)
Triay, J., Cervello-Pastor, C.: An ant-based algorithm for distributed routing and wavelength assignment in dynamic optical networks. IEEE Journal on Selected Areas in Communications 28, 542–552 (2010)
Karia, D.C., Godbole, V.V.: New approach for routing in mobile ad-hoc networks based on ant colony optimisation with global positioning system. IET Networks 2, 171–180 (2013)
Li-Ning, X., Rohlfshagen, P., Ying-Wu, C., Xin, Y.: A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41, 1110–1123 (2011)
Young-Min, K., Eun-Jung, L., Hong-Shik, P.: Ant Colony Optimization Based Energy Saving Routing for Energy-Efficient Networks. IEEE Communications Letters 15, 779–781 (2011)
Yangguang, F., Mingyue, D., Chengping, Z.: Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 42, 511–526 (2012)
Xiongbiao, L., Mori, K.: Robust Endoscope Motion Estimation Via an Animated Particle Filter for Electromagnetically Navigated Endoscopy. IEEE Transactions on Biomedical Engineering 61, 85–95 (2014)
Temel, S., Unaldi, N., Kaynak, O.: On Deployment of Wireless Sensors on 3-D Terrains to Maximize Sensing Coverage by Utilizing Cat Swarm Optimization With Wavelet Transform. IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, 111–120 (2014)
Genetic Algorithms Applied to Travelling Salesman Problems in C++, http://www.technical-recipes.com/2012/genetic-algorithms-applied-to-travelling-saleman-problems-in-c/
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Chien-Chou, S., Yih, Y., Mong-Fong, H., Tien-Szu, P., Jeng-Shyang, P. (2014). A Framework to Evolutionary Path Planning for Autonomous Underwater Glider. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8482. Springer, Cham. https://doi.org/10.1007/978-3-319-07467-2_1
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DOI: https://doi.org/10.1007/978-3-319-07467-2_1
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