Three-dimensional path planning for unmanned aerial vehicles using glowworm swarm optimization algorithm

被引：48

作者：

Pandey P. ^{[1
]}

Shukla A. ^{[1
]}

Tiwari R. ^{[1
]}

机构：

[1] Robotics and Intelligent System Design Lab, Indian Institute of Information Technology and Management, Gwalior

来源：

International Journal of System Assurance Engineering and Management | 2018年 / 9卷 / 4期

关键词：

Glowworm swarm optimization; Meta-heuristics; Path planning; Robotics; Swarm intelligence;

D O I：

10.1007/s13198-017-0663-z

中图分类号：

学科分类号：

摘要：

Robot path planning is a task to determine the most viable path between a source and destination while preventing collisions in the underlying environment. This task has always been characterized as a high dimensional optimization problem and is considered NP-Hard. There have been several algorithms proposed which give solutions to path planning problem in deterministic and non-deterministic ways. The problem, however, is open to new algorithms that have potential to obtain better quality solutions with less time complexity. The paper presents a new approach to solving the 3-dimensional path planning problem for a flying vehicle whose task is to generate a viable trajectory for a source point to the destination point keeping a safe distance from the obstacles present in the way. A new algorithm based on discrete glowworm swarm optimization algorithm is applied to the problem. The modified algorithm is then compared with Dijkstra and meta-heuristic algorithms like PSO, IBA and BBO algorithm and their performance is compared to the path optimization problem. © 2017, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.

引用

页码：836 / 852

页数：16

共 29 条

[1]

Arantes M.D., Arantes J.D., Toledo C.F., Williams B.C.A., Hybrid multi-population genetic algorithm for UAV path planning, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp. 853-860, (2016)

[2]

Bansal J.C., Et al., Self-adaptive artificial bee colony, Optimization, 63, 10, pp. 1513-1532, (2014)

[3]

Bansal J.C., Et al., Spider monkey optimization algorithm for numerical optimization, Memet Comput, 6, 1, pp. 31-47, (2014)

[4]

Bortoff S.A., Path planning for UAVs, In: Proceedings of the 2000 American Control Conference on ACC, pp. 364-368, (2000)

[5]

Chen Y., Et al., Modified central force optimization (MCFO) algorithm for 3D UAV path planning, Neurocomputing, 171, pp. 878-888, (2016)

[6]

Corke P., Robotics, vision and control: fundamental algorithms in MATLAB, 73, (2011)

[7]

Duan H., Li P., Bio-inspired computation in unmanned aerial vehicles, (2014)

[8]

Foo J., Knutzon J., Oliver J., Winer E., Three-dimensional path planning of unmanned aerial vehicles using particle swarm optimization, 11Th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, pp. 123-156, (2006)

[9]

Foo J.L., Et al., Path planning of unmanned aerial vehicles using B-splines and particle swarm optimization, J Aerosp Comput Inf Commun, 6, 4, pp. 271-290, (2009)

[10]

Fu S.Y., Han L.W., Tian Y., Yang G.S., Path planning for unmanned aerial vehicle based on genetic algorithm, 2012 IEEE 11Th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), pp. 140-144, (2012)

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