Three-Dimensional Path Planning Method for Autonomous Underwater Vehicle Based on Modified Firefly Algorithm

Abstract

Path planning is a classic optimization problem which can be solved by many optimization algorithms. The complexity of three-dimensional (3D) path planning for autonomous underwater vehicles (AUVs) requires the optimization algorithm to have a quick convergence speed. This work provides a new 3D path planning method for AUV using a modified firefly algorithm. In order to solve the problem of slow convergence of the basic firefly algorithm, an improved method was proposed. In the modified firefly algorithm, the parameters of the algorithm and the random movement steps can be adjusted according to the operating process. At the same time, an autonomous flight strategy is introduced to avoid instances of invalid flight. An excluding operator was used to improve the effect of obstacle avoidance, and a contracting operator was used to enhance the convergence speed and the smoothness of the path. The performance of the modified firefly algorithm and the effectiveness of the 3D path planning method were proved through a varied set of experiments.

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

“…Tighzert et al introduces a set of new compact FF algorithms with minimal computational costs to a legged robot with a specific application. A new three‐dimensional path planning method for autonomous underwater vehicles using a modified FF algorithm is proposed in order to improve the speed of convergence in FF algorithm by adjusting the brightness and attraction parameters . Reactive approaches such as FF and ACO perform better than classical approaches because they have a higher capability to handle uncertainty present in the environment.…”

“…A new three-dimensional path planning method for autonomous underwater vehicles using a modified FF algorithm is proposed in order to improve the speed of convergence in FF algorithm by adjusting the brightness and attraction parameters. 38 Reactive approaches such as FF and ACO perform better than classical approaches because they have a higher capability to handle uncertainty present in the environment. Reactive approaches are used for real-time navigation problems and classical approaches can be improved by hybridizing with the reactive approaches.…”

Mobile Robot Path Planning Method Using Firefly Algorithm for 3D Sphere Dynamic & Partially Known Environment

“…proposing a nee modified firefly algorithm to solve the UCAV path planning problem [10], and Liu et al who modified version of the firefly algorithm was proposed for AUVs path planning problem in three-dimensional form [11].…”

“…In the inspired Firefly Algorithm three rules have being used to idealize the behavior of the fireflies [11] [9] [8], those are:  All the fireflies are considered as one gender, so that any firefly can be attracted to the other regardless of their gender.  The Attractiveness of the fireflies is proportional to their brightness and they both decrease when the distance increase, this way one firefly will move towards the other depending on their brightness, and if there is no brighter one then the firefly will move randomly.…”

A Systematic Review on Firefly Algorithm: Past, Present, and Future

“…Dependence of distance function had been shifted from location in feasible region to brightness or functional value calculated using f (x b ) − f (xi). They considered that, if two fireflies had similar performance, they must be nearby.α, γ parameters of FA were modified, and used in path planning of autonomous underwater vehicles[58].α was calculated using α = α b + Itr M axGen (αe − α b ) where αe < α b . γ was modified as γ = γ b + Itr M axGen (γe − γ b ) where γe > γ b .…”