Analysis of FPA and BA meta-heuristic controllers for optimal path planning of mobile robot in cluttered environment

This study proposes the design of two efficient nature inspired intelligent optimal controllers, flower pollination algorithm (FPA) and bat algorithm (BA) for obtaining optimal path using an autonomous mobile robot in an unknown environment. FPA is based on the pollination process of flowering plants, which transfer pollens by using different pollinators. On the contrary, BA depends on echolocation and frequency tuning to solve different types of optimisation problems in engineering. To accomplish the path-planning task of mobile robot autonomously, a fitness function has been introduced considering the distance between robot-obstacle and robot-goal to satisfy the conditions of obstacle avoidance and goal reaching behaviour of robot. Based on the values of objective function of the algorithms, mobile robot avoids obstacles in the unknown environment and moves towards the goal. In this work, the efficiency of such controllers is verified using some simulations in MATLAB environment. Further, an experimental work is carried out in real-world environment using ARDUINO Mega 2560 microcontroller to ascertain the path length, travelling time and convergence speed of the two algorithms.

[1]  Shilpa Samal Shilpa Samal.  Optimizing Mobile Robot Path Planning and Navigation by Use of Differential Evolution , 2013 .

[2]  Syamsiah Mashohor,et al.  A highly interpretable fuzzy rule base using ordinal structure for obstacle avoidance of mobile robot , 2011, Appl. Soft Comput..

[3]  Ibrahim El-Henawy,et al.  A Comparison between FPPSO and B&B Algorithm for Solving Integer Programming Problems , 2015 .

[4]  Nazmus Sakib,et al.  A Comparative Study of Flower Pollination Algorithm and Bat Algorithm on Continuous Optimization Problems , 2014 .

[5]  Akhilesh Kumar Mishra,et al.  A LITERATURE BASED SURVEY ON SWARM INTELLIGENCE INSPIRED OPTIMIZATION TECHNIQUE , 2015 .

[6]  Lefteris Doitsidis,et al.  Evolution of Fuzzy Controllers for Robotic Vehicles: The Role of Fitness Function Selection , 2009, J. Intell. Robotic Syst..

[7]  Emmanuel G. Collins,et al.  Robot navigation in very cluttered environments by preference-based fuzzy behaviors , 2008, Robotics Auton. Syst..

[8]  Amir Hossein Karami,et al.  An adaptive genetic algorithm for robot motion planning in 2D complex environments , 2015, Comput. Electr. Eng..

[9]  Chee Peng Lim,et al.  A modified Intelligent Water Drops algorithm and its application to optimization problems , 2014, Expert Syst. Appl..

[10]  Ataollah Ebrahimzadeh,et al.  A novel technique for blind source separation using bees colony algorithm and efficient cost functions , 2014, Swarm Evol. Comput..

[11]  Sampa Sahoo,et al.  Navigation of autonomous mobile robot using different activation functions of wavelet neural network , 2014, International Conference for Convergence for Technology-2014.

[12]  Amer Draa,et al.  On the performances of the flower pollination algorithm - Qualitative and quantitative analyses , 2015, Appl. Soft Comput..

[13]  Oscar Castillo,et al.  Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots , 2006, Soft Comput..

[14]  Christopher A. Sikorski,et al.  Grid-based mapping for autonomous mobile robot , 1993, Robotics Auton. Syst..

[15]  M. J. Mahjoob,et al.  Bee colony algorithm for real-time optimal path planning of mobile robots , 2009, 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control.

[16]  Dayal R. Parhi,et al.  A novel intelligent mobile robot navigation technique for avoiding obstacles using RBF neural network , 2014, Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC).

[17]  B. B. V. L. Deepak,et al.  Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment , 2013, Intell. Serv. Robotics.

[18]  Joarder Kamruzzaman,et al.  Search and tracking algorithms for swarms of robots: A survey , 2016, Robotics Auton. Syst..

[19]  Bakir Lacevic,et al.  Neural Network Controller for Mobile Robot Motion Control , 2008 .

[20]  Ellips Masehian,et al.  A voronoi diagram-visibility graph-potential field compound algorithm for robot path planning , 2004 .

[21]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[22]  Andries Petrus Engelbrecht,et al.  Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems , 2014, Swarm Evol. Comput..

[23]  Mohamed Abdel-Baset,et al.  A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems , 2014 .

[24]  Anup Kumar Panda,et al.  Potential field method to navigate several mobile robots , 2006, Applied Intelligence.

[25]  Lisa L. Grant,et al.  Collective robotic search using hybrid techniques: Fuzzy logic and swarm intelligence inspired by nature , 2009, Eng. Appl. Artif. Intell..