ABSTRACT
This paper proposes a path planner for autonomous underwater vehicles (AUVs) in 3-D underwater space. We simulate an underwater space with rugged seabed and suspending obstacles, which is close to real world. In the proposed representation scheme, the problem space is decomposed into parallel subspaces and each subspace is described by a grid method. The paths of AUVs are simplified as a set of successive points in the problem space. By jointing these waypoints, the entire path of the AUV is obtained. A cost function with penalty method takes into account the length, energy consumption, safety and curvature constraints of AUVs. It is applied to evaluate the quality of paths. Differential evolution (DE) algorithm is used as a black-box optimization tool to provide optimal solutions for the path planning. In addition, we adaptively adjust the parameters of DE according to population distribution and the blockage of parallel subspaces so as to improve its performance. Experiments are conducted on 6 different scenarios. The results validate that the proposed algorithm is effective for improving solution quality and avoiding premature convergence.
- Blidberg, D. R. 2001. The development of autonomous underwater vehicles (auvs); a brief summary. IEEE Int. Conf. Robotics Automation, 6500.Google Scholar
- Latombe, J. C. 2005. Robot motion planning. Norwell, USA: Kluwer.Google Scholar
- Yilmaz, N. K., Evangelinos, C., Lermusiaux, P. and Patrikalakis, N. M. 2008. Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming. IEEE J. Ocean. Eng. 33, 4, 522--537.Google ScholarCross Ref
- Cetin, B., Bikdash, M. and Hadaegh, F. Y. 2007. Hybrid mixed-logical linear programming algorithm for collision-free optimal path planning. IET Control Theory and Applications. 1, 2, 522--531.Google ScholarCross Ref
- Garau, B., Alvarez, A. and Oliver, G. 2005. Path planning of autonomous underwater vehicles in current fields with complex spatial variability: an A* approach. in Proc. IEEE Conf. Robotics and Automation. 194--198.Google Scholar
- Guo, S.-X. and Gao, B.-F. 2009. Path planning optimization of underwater microrobots in 3-D space by PSO approach. in Proc. IEEE Int. Conf. Robotics and Biomimetics. 1655--1620. Google ScholarDigital Library
- Alvarez, A., Caiti, A. and Onken, R. 2004. Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J. Ocean. Eng. 29, 2, 418--429.Google ScholarCross Ref
- Zeng, Z., Lammas, A. and Sammut, K. 2012. Optimal path planning based on annular space decomposition for AUVs operating in a variable environment. IEEE/OES Autonomous Underwater Vehicles (AUV). 1--9.Google Scholar
- Cheng, C. T. Fallahi, Leung, K. H. and Tse, C. K. 2010. An AUVs path planner using genetic algorithms with a deterministic crossover operator. in Proc. IEEE Int. Conf. Robotics and Automation, Anchorage. 2995--3000.Google Scholar
- Roberge, V., Tarbouchi, M. and Labonte, G. 2013. Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning. IEEE Trans Industrial Informatics. 9, 1, 132--141.Google ScholarCross Ref
- Petres, C., Pailhas, Y., Patron, P., Petillot, Y., Evans, J. and Lane, D. 2007. Path planning for autonomous underwater vehicles. IEEE Trans. Robotics. 23, 2, 331--341. Google ScholarDigital Library
- Zhan, Z. H., Zhang, J., Li, Y. and Chung, H. H. 2009. Adaptive particle swarm optimization. IEEE Trans Systems, Man, and Cybernetics. 39, 6, 1362--1381. Google ScholarDigital Library
- Kennedy, J. and Eberhart, R. C. 1995. Particle swarm optimization. in Proc. IEEE Int. Conf. Neural Netw. 4, 1942--1948.Google ScholarCross Ref
- Storn, R. and Price, K. 1997. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization. 11, 4, 341--359. Google ScholarDigital Library
- Mallipeddia, R., Suganthana, P. N., Panb, Q. K. and Tasgetirenc, M. F. 2011. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing. 11, 2, 1679--1696. Google ScholarDigital Library
- Storn, R. 1996. On the usage of differential evolution for function optimization. Biennial Conference of the North American Fuzzy Information Processing Society. 519--523.Google ScholarCross Ref
- Brest, J., Greiner, S., Boskovic, B., Mernik, M. and Zumer, V. 2006. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10, 6, 646--657. Google ScholarDigital Library
Index Terms
- Automatic path planning for autonomous underwater vehicles based on an adaptive differential evolution
Recommendations
Review of Path Planning for Autonomous Underwater Vehicles
RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial IntelligenceAn autonomous underwater vehicle (AUV) is an important tool for the exploration and development of marine resources. Path planning plays an important role in the correct navigation and avoidance of obstacles by underwater robots in the sea. Firstly, ...
Adaptive autonomous underwater vehicles for littoral surveillance
Autonomous underwater vehicles (AUVs) have gained more interest in recent years for military as well as civilian applications. One potential application of AUVs is for the purpose of undersea surveillance. As research into undersea surveillance using ...
Constrained differential evolution optimization for underwater glider path planning in sub-mesoscale eddy sampling
Graphical abstractDisplay Omitted HighlightsAn approach for tackling constrained underwater glider sub-mesoscale path planning.The feasible path area is defined as a corridor around the border of an ocean eddy.A new configuration of constrained ...
Comments