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.