Abstract
AUV mission planning route avoidance purpose is to be able to successfully avoid the threat of a number of different levels of obstacles between the start and end of the route , and plan the optimal route planning to meet certain performance indicators. Through the differential evolution algorithm analysis and description , the avoidance route mission planning problem into a multi-dimensional function optimization problems, optimization problems for AUV mission planning route avoidance functions , based on differential evolution algorithm is proposed route obstacle avoidance task planning methods and after a comprehensive analysis and simulation results validate the differential evolution algorithm in high-dimensional function optimization convergence and stability demonstrated good performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Isern-Gonzalez, J., Hernandez-Sosa, D., et al.: Obstacle Avoidance in Underwater Glider Path Planning. Physical Agents 6(1), 11–20 (2012)
Tsou, M.-C., Hsueh, C.-K.: The Study of Ship Collision Avoidance Route Planningby Ant Colony Algorithm. Journal of Marine Science and Technology, 746–756 (2010)
Cruz, G.C.S., Encarnação, P.M.M.: Obstacle Avoidance for Unmanned Aerial Vehicles. Journal of Intelligent & Robotic Systems, 203–217 (2012)
Yan, G., Wang, L., Zhou, J., Zha, Z.: Path Planning Based on Improved Genetic Algorithm for AUV. Journal of Chongqing University of Technology, 115–120 (2010)
Storn, R., Price, K.: Differential Evolution A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 341–359 (1997)
Price, K.: Differential Evolution A Fast and Simple Numerical Optimizer. In: Proceedings of Biennial Conference of the North American Fuzzy Information Processing Society, pp. 524–527 (1996)
Islam, S.M., Das, S.: An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernet ICS, 482–500 (2012)
Deng, Y., Beaujean, P.-P.J., An, E., Carlson, E.: Task Allocation and Path Planning for Collaborative Autonomous Underwater Vehicles Operating through an Underwater Acoustic Network. Journal of Robotics, 1–15 (2013)
Qu, H., Xing, K., Alexander, T.: An Improved Genetic Algorithm With Coevolution Strategy for Global Path Planning of Multiple Mobile Robots. Neuro Computing 120, 509–517 (2013)
Ye, W., Wang, C., Yang, M., Wang, B.: Virtual Obstacles Based Path Planning for Mobile Robots. Robot, 273–286 (2011)
Song, Q., Liu, L.: Mobile Robot Path Planning Based on Dynamic Fuzzy Artificial Potential Field Method. International Journal of Hybrid Information Technology 5, 85–94 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, JJ., Zhang, RB., Yang, Y. (2014). Research on Route Obstacle Avoidance Task Planning Based on Differential Evolution Algorithm for AUV. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-11897-0_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11896-3
Online ISBN: 978-3-319-11897-0
eBook Packages: Computer ScienceComputer Science (R0)