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

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.

[1]  Elisa Caprari.  ρ-Invex Functions and ( F , ρ)Convex Functions: Properties and Equivalences , 2003 .

[2]  George J. Vachtsevanos,et al.  Handbook of Unmanned Aerial Vehicles , 2014 .

[3]  Eliot Winer,et al.  Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization , 2009, J. Aerosp. Comput. Inf. Commun..

[4]  Debasish Ghose,et al.  Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions , 2009, Swarm Intelligence.

[5]  Wei Pan,et al.  Grey wolf optimizer for unmanned combat aerial vehicle path planning , 2016, Adv. Eng. Softw..

[6]  Harish Sharma,et al.  Self-adaptive artificial bee colony , 2014 .

[7]  Yu Tian,et al.  Path planning for unmanned aerial vehicle based on genetic algorithm , 2012, 2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing.

[8]  Rui Wang,et al.  An Improved Flower Pollination Algorithm for Optimal Unmanned Undersea Vehicle Path Planning Problem , 2016, Int. J. Pattern Recognit. Artif. Intell..

[9]  Yongquan Zhou,et al.  A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning , 2015, J. Intell. Syst..

[10]  Harish Sharma,et al.  Spider Monkey Optimization algorithm for numerical optimization , 2014, Memetic Computing.

[11]  Xin Zhao,et al.  Research on the Problem of the Shortest Path Based on the Glowworm Swarm Optimization Algorithm , 2015 .

[12]  Gai-Ge Wang,et al.  A modified firefly algorithm for UCAV path planning , 2012 .

[13]  Harish Sharma,et al.  Artificial bee colony algorithm with global and local neighborhoods , 2014, International Journal of System Assurance Engineering and Management.

[14]  Scott A. Bortoff,et al.  Path planning for UAVs , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[15]  Claudio Fabiano Motta Toledo,et al.  A Hybrid Multi-Population Genetic Algorithm for UAV Path Planning , 2016, GECCO.

[16]  Pei Li,et al.  Bio-inspired computation in unmanned aerial vehicles , 2014 .

[17]  Eliot Winer,et al.  Three-Dimensional Path Planning of Unmanned Aerial Vehicles Using Particle Swarm Optimization , 2006 .

[18]  Pragya Sharma,et al.  Locally Informed Shuffled Frog Leaping Algorithm , 2016, SocProS.

[19]  Seyedali Mirjalili,et al.  Three-dimensional path planning for UCAV using an improved bat algorithm , 2016 .

[20]  Haibin Duan,et al.  Chaotic predator–prey biogeography-based optimization approach for UCAV path planning , 2014 .

[21]  Cong Xie,et al.  Application of Improved Cuckoo Search Algorithm to Path Planning Unmanned Aerial Vehicle , 2016, ICIC.

[22]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[23]  Bo Zhang,et al.  Predator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planning , 2014, ICSI.

[24]  Peter I. Corke,et al.  Robotics, Vision and Control - Fundamental Algorithms in MATLAB® , 2011, Springer Tracts in Advanced Robotics.

[25]  Jianqiao Yu,et al.  Modified central force optimization (MCFO) algorithm for 3D UAV path planning , 2016, Neurocomputing.

[26]  Jie Chen,et al.  3D Multi-Constraint Route Planning for UAV Low-Altitude Penetration Based on Multi-Agent Genetic Algorithm , 2011 .

[27]  Vincent Roberge,et al.  Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning , 2013, IEEE Transactions on Industrial Informatics.

[28]  Du Peng-zhe.  Global Path Planning for ALV Based on Improved Glowworm Swarm Optimization Under Uncertain Environment , 2014 .

[29]  Bo Zhang,et al.  Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[30]  Sun Xiu-xia,et al.  A Route Planning's Method for Unmanned Aerial Vehicles Based on Improved A-Star Algorithm , 2008 .