A novel path planning algorithm based on plant growth mechanism

We propose a bio-inspired computing algorithm based on plant growth mechanism and describe its application in path planning in this paper. The basic rules of the algorithm include phototropism, negative geotropism, apical dominance, and branch in plant growth. The starting point of the algorithm is the seed germ (first bud) and the target point of the algorithm is the light source. The discretization of the plant growth process is used to realize computation in computer. The plant growth behavior in each iteration is assumed to be the same. The algorithm includes six steps: initialization, light intensity calculation, random branch, growth vector calculation, plant growth and path output. Several two-dimensional path planning problems are used to validate the algorithm. The test results show that the algorithm has good path planning ability and provides a novel path planning approach.

[1]  R. Srinivasas Rao,et al.  Optimal capacitor placement in a radial distribution system using Plant Growth Simulation Algorithm , 2008 .

[2]  Aaron Sloman,et al.  Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots , 2007 .

[3]  Dervis Karaboga,et al.  On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation , 2015, Inf. Sci..

[4]  Xiujuan Lei,et al.  Application of artificial fish school algorithm in UCAV path planning , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[5]  B. B. V. L. Deepak,et al.  Innate Immune based Path Planner of an Autonomous Mobile Robot , 2012 .

[6]  Farshad Merrikh-Bayat,et al.  A Numerical Optimization Algorithm Inspired by the Strawberry Plant , 2014, ArXiv.

[7]  S. N. Patro,et al.  Artificial Immune System Based Path Planning of Mobile Robot , 2012, Soft Computing Techniques in Vision Science.

[8]  Md. Arafat Hossain,et al.  Autonomous robot path planning in dynamic environment using a new optimization technique inspired by Bacterial Foraging technique , 2014, 2013 International Conference on Electrical Information and Communication Technology (EICT).

[9]  Oscar Castillo,et al.  Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation , 2009, Appl. Soft Comput..

[10]  Hanning Chen,et al.  Mobile robot path planning based on adaptive bacterial foraging algorithm , 2013 .

[11]  T. Miyaji,et al.  Mathematical analysis to an adaptive network of the Plasmodium system , 2007 .

[12]  Qing-Yun Hu,et al.  Removal of Refractory Organic Orange IV by Fe-Mn/GAC in the Presence of H2O2 , 2012 .

[13]  Abdellah Salhi,et al.  A Plant Propagation Algorithm for Constrained Engineering Optimisation Problems , 2014 .

[14]  Rui Zhou,et al.  UCAV path planning based on Ant Colony Optimization and satisficing decision algorithm , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[15]  Zheng Ran Zhang,et al.  The Study on Mobile Robot Path Planning Based on Frog Leaping Algorithm , 2012 .

[16]  Jianhua Zhang,et al.  Multi-objective Particle Swarm Optimization for Robot Path Planning in Environment with Danger Sources , 2011, J. Comput..

[17]  Dong-Ling Xu,et al.  Circuit Tolerance Design Using Belief Rule Base , 2015 .

[18]  Li Tong,et al.  Research on Plant Growth Simulation Algorithm based on Finite Element Method , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[19]  Sandeep Kumar,et al.  A New Ecologically Inspired Algorithm for Mobile Robot Navigation , 2014, FICTA.

[20]  Zili Zhang,et al.  Rapid Physarum Algorithm for shortest path problem , 2014, Appl. Soft Comput..

[21]  Hongwei Mo,et al.  Robot Path Planning Based on Differential Evolution in Static Environment , 2012 .

[22]  Yahui Yu,et al.  The Exponential Diophantine Equation 2x + b y = c z , 2014, TheScientificWorldJournal.

[23]  Carlos A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[24]  Haibin Duan,et al.  Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm , 2010, Simul. Model. Pract. Theory.

[25]  Feng Gao,et al.  Three-Dimensional Path Planning Method for Autonomous Underwater Vehicle Based on Modified Firefly Algorithm , 2015 .

[26]  Teng-Fa Tsao,et al.  Plan on Obstacle-Avoiding Path for Mobile Robots Based on Artificial Immune Algorithm , 2007, ISNN.

[27]  Fang Liu,et al.  Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning , 2010 .

[28]  Pratyusha Rakshit,et al.  Multi-robot path-planning using artificial bee colony optimization algorithm , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[29]  Pratyusha Rakshit,et al.  A hybridisation of Improved Harmony Search and Bacterial Foraging for multi-robot motion planning , 2012, 2012 IEEE Congress on Evolutionary Computation.

[30]  Miguel A. Vega-Rodríguez,et al.  MOSFLA-MRPP: Multi-Objective Shuffled Frog-Leaping Algorithm applied to Mobile Robot Path Planning , 2015, Eng. Appl. Artif. Intell..

[31]  Dayal R. Parhi,et al.  A New Real Time Path Planning for Mobile Robot Navigation Using Invasive Weed Optimization Algorithm , 2014 .

[32]  Toshiyuki Nakagaki,et al.  Physarum solver: A biologically inspired method of road-network navigation , 2006 .

[33]  Xing Li,et al.  Robot Global Path Planning Based on Improved Artificial Fish-Swarm Algorithm , 2013 .

[34]  A. Tero,et al.  A mathematical model for adaptive transport network in path finding by true slime mold. , 2007, Journal of theoretical biology.

[35]  Ni Jianjun,et al.  An improved shuffled frog leaping algorithm for robot path planning , 2014, ICNC 2014.

[36]  Yufeng Zhang,et al.  Path planning based on firefly algorithm and Bezier curve , 2014, 2014 IEEE International Conference on Information and Automation (ICIA).

[37]  Jiang Chang-sheng,et al.  Short communication: A modified ant optimization algorithm for path planning of UCAV , 2008 .

[38]  Ben Niu,et al.  Robot Path Planning Using Bacterial Foraging Algorithm , 2013 .

[39]  A. Karci,et al.  Natural Inspired Computational Intelligence Method: Saplings Growing up Algorithm , 2007, 2007 IEEE International Conference on Computational Cybernetics.

[40]  Guan-Chun Luh,et al.  An immunological approach to mobile robot reactive navigation , 2008, Appl. Soft Comput..

[41]  Mohammad Ali Badamchizadeh,et al.  Mobile robot path planning based on shuffled frog leaping optimization algorithm , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[42]  Dayal R. Parhi,et al.  A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm , 2014 .

[43]  Mingyue Ding,et al.  Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[44]  Chang Liu,et al.  A New Path Planning Method Based on Firefly Algorithm , 2012, 2012 Fifth International Joint Conference on Computational Sciences and Optimization.

[45]  Wang Chun-feng,et al.  A Global Optimization Bionics Algorithm for Solving Integer Programming - Plant Growth Simulation Algorithm , 2005 .

[46]  Bai Li,et al.  An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning , 2014, TheScientificWorldJournal.

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

[48]  Hakan Temeltas,et al.  Fuzzy-differential evolution algorithm for planning time-optimal trajectories of a unicycle mobile robot on a predefined path , 2004, Adv. Robotics.

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