Simulation for path planning of autonomous underwater vehicle using Flower Pollination Algorithm, Genetic Algorithm and Q-Learning

The motivation behind this paper is to address the necessity for exploration in near bottom ocean environment employing Autonomous Underwater Vehicles. This paper presents a simulation for an optimized path planning for an autonomous underwater vehicle in benthic ocean zones. The statistical data pertaining to the near-bottom ocean currents has been sourced from the Bedford Institute of Oceanography, Canada. A cost function is developed which incorporates the interaction of the underwater vehicle with the ocean currents. This cost function takes the source and destination coordinates as the inputs and outputs the time taken by the vehicle to travel between them. This paper aims to minimize this cost function to obtain a path having the least travel time for the vehicle. Various biologically inspired algorithms such as Flower Pollination Algorithm and Genetic Algorithm have been used to optimize this cost function. The optimization of the cost function has also been performed using Q-Learning technique and the results have been compared with the biologically inspired algorithms. The results depict that Q-Learning Algorithm is better in computational complexity and ease of simulating the environment. Thus, an efficient Path planning technique, which has been tested for the cost function of an autonomous underwater vehicle is proposed through this paper.