Navigational control of underwater mobile robot using dynamic differential evolution approach

Abstract

This article focuses on the navigational control of underwater mobile robot. Differential evolution approach has been used to navigate the underwater robot from source to destination while avoiding various types of obstacles. Differential evolution algorithm has been employed to find out the robot's global best pose among a set of possible solutions based on the fitness value with respect to the current sensory data about obstacles and target. Such evolutionary computation scheme can provide desirable convergence, diversity and also robustness depending on proper selection and adaptive tuning of parameters. Self-learning ability of the parameters in the path planning algorithm is crucial to deal with nonlinearities and ambiguities of hydrodynamics as created by high-frequency oscillations during underwater motion. A sequence of intermediate positions chosen by proposed dynamic differential evolution algorithm between start and goal points can be defined as a near-optimum path for underwater robot. During navigation of the robot, the path smoothness and clearance from obstacles and computational time are also considered for performance evaluation of implemented algorithm. The feasibility of the proposed underwater motion planning approach has been authenticated through the simulation and experimental results.

Incorporating irregular nonlinear waves in simulation of dropped cylindrical objects

“…For the analysis of 6-degree-of-freedom (DOF) motion, two right-handed orthogonal coordinate systems, global and local coordinate systems, are considered. 9 The global coordinate system is OXYZ , as shown in Figure 1, where X-Y represents the still-water surface and Z -axis is vertical to X - Y , pointing upwards. The local coordinate system is a oxyz fixed on the dropped object and its origin is located at the geometric centre.…”

“…For equations (8) and (9), the first term represents the drag forces F dy , F dz and the moments M dy , M dz caused by drag force; the second term shows the inertial forces F IWy , F IWz and moments M IWy , M IWz induced by waves and the last term expresses the added mass force and moment which have been considered in equations (2)- (6). The three directional components of U(x) are given as…”

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

“…The mobile robot decides its path in real time to avoid randomly moving obstacles [39]. Other intelligent algorithms studied by researchers used by mobile robot to navigate in diverse environment are Differential Evolution (DE) algorithm [40], [41], Harmony Search (HS) algorithm [42], Bat Algorithm (BA) [43], and Invasive Weed Optimization (IWO) [44].…”

Mobile robots path planning and mobile multirobots control: A review

“…Other algorithms: Many researchers gave different intelligent techniques to perform the task of mobile robot navigation in various environmental situations such as Shuffled frog leaping algorithm [75,76,77], Cuckoo search algorithm [78,79], Artificial bee colony algorithm [80,81,82,83], InvasiveWeed Optimization [84,85,86], Harmony Search Algorithm [87,88], Bat Algorithm [89,90,91], Differential Evolution Algorithm [92,93,94], reactive obstacle avoidance based on boundary following using sliding mode control [95], collision avoidance based on geometric approach [96,97], and many more.…”