Robots can replace worker to finish a lot of complex works in complex environment. Recently, much research work has been done in the application of robots. To which robot navigation has become one of the most popular research field. The much emphasis is on programming of a complete, deterministic algorithm which is able to generate an optimum path in real time will and allow achieving a high level of autonomy. This means, for example, that you can read the newspaper when your car takes you to work on its own. In this paper we used the plant growth optimization algorithm for route selection in obstacle environment. The plant grow optimization algorithm is multi-objective optimization technique with multiple constraints such as growing of leaf and competition of branch. The system that needs to be optimized first "grows" from the root of a plant and then continues to "grow" until you find the optimal solution

Path Planning, Robotics, Obstacles, Plant Grow Optimisation

[1] Belinda Matebese, Daniel Withey and Mapundi K. Banda "Path planning with the leapfrog method in the presence of obstacles", IEEE, 2016, Pp 613-618.
[2] B.T. Matebese, D.J. Withey and M.K. Banda “Application of the leapfrog method to robot path planning”, IEEE, 2014, Pp 710-715.
[3] Hao-dong ZHU and Bao-feng HE “Path planning of mobile robot using optimized ACA”, AITA, 2016, Pp 92-98.
[4] Prithviraj Dasgupta “coverage path planning using mobile robot team formations”, IGI Global, 2015, Pp 214-227.
[5] Andrea I. Schäfer, Gordon Hughes and Bryce S. Richards “Renewable energy powered membrane technology: a leapfrog approach to rural water treatment in developing countries”, Elsevier, 2014, Pp 1-44.
[6] S. Sekar and K. Prabhavathi “Numerical solution of first order linear fuzzy differential equations using leapfrog method”, IOSR Journal of Mathematics, 2014, Pp 7-12.
[7] Noor Hazarina Hashim, Jamie Murphy, Olaru Doina and Peter O’Connor “Bandwagon and leapfrog effects in internet implementation”, International Journal of Hospitality Management, 2014, Pp 91–98.
[8] S. Sekar and M. Vijayarakavan “Numerical investigation of first order linear singular systems using leapfrog method”, International Journal of Mathematics Trends and Technology, 2014, Pp 89-93.
[9] Ki-Baek Lee and Jong-Hwan Kim “Multi-objective particle swarm optimization with preference-based sort and its application to path following footstep optimization for humanoid robots”, IEEE, 2013, Pp 1-13.
[10] P. Raja and S. Pugazhenthi “Optimal path planning of mobile robots: a review”, International Journal of Physical Sciences, 2012, Pp 1314-1320.
[11] Jonathan Binney, Andreas Krause and Gaurav S. Sukhatme “Informative path planning for an autonomous underwater vehicle”, IEEE, 2010, Pp 4791-4796.
[12] Yogita Gigras, Kavita Choudhary, Kusum Gupta and Vandana “A hybrid ACO-PSO technique for path planning”, IEEE, 2015, Pp 1616-1621.
[13] Ahlam Ansari and Mohd Amin Sayyed, Khatija Ratlamwala and Parvin Shaikh “An Optimized Hybrid Approach for Path Finding”, International Journal in Foundations of Computer Science & Technology, 2015, Pp 47-58.
[14] Wei Cai, Weiwei Yang and Xiaoqian Chen “A Global Optimization Algorithm Based on Plant Growth Theory: Plant Growth Optimization”, Intelligent Computation Technology and Automation, 2008, Pp 1194-1199.
[15] Manoj Garg and Dinesh Kumar “Simple GA & Hybrid GA for Basis Path Testing under BDFF”, International Journal of Scientific Research in Computer Sciences and Engineering, 2016,Pp 28-35.
[16] G.P. Sunitha, B.P. Vijay Kumar, S.M. Dilip Kumar “A Nature Inspired Optimal Path Finding Algorithm to Mitigate Congestion in WSNs”, International Journal of Scientific Research in Network Security and Communication, 2018 Pp50-57.