An Energy Minimized Solution for Solving Redundancy of Underwater Vehicle-Manipulator System Based on Genetic Algorithm

2017 ◽  
pp. 394-401 ◽  
Author(s):  
Qirong Tang ◽  
Le Liang ◽  
Yinghao Li ◽  
Zhenqiang Deng ◽  
Yinan Guo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Underwater Vehicle

scholarly journals Using Genetic Algorithms to Optimize Bathymetric Sampling for Predictive Model Input

2012 ◽  
Vol 29 (3) ◽  
pp. 464-477 ◽  
Author(s):  
Dinesh Manian ◽  
James M. Kaihatu ◽  
Emily M. Zechman
Keyword(s):  
Genetic Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Forecast Model ◽  
Optimization Method ◽  
Underwater Vehicle ◽  
Bathymetric Data ◽  
Test Study ◽  
Limited Length ◽  
The Given ◽  
Sampling Pattern

Abstract This paper describes the use of an optimization method to effectively reduce the required bathymetric sampling for forcing a numerical forecast model by using the model’s sensitivity to this input. A genetic algorithm is developed to gradually evolve the survey path for a ship, autonomous underwater vehicle (AUV), or other measurement platform to an optimum, with the resulting effect of the corresponding measured bathymetry on the model used as a metric. Starting from an initial simulated set of possible random or heuristic sampling paths over the given bathymetry using certain constraints like limited length of track, the algorithm can be used to arrive at the path that would provide the best possible input to the model under those constraints. This suitability is tested by a comparison of the model results obtained by using these new simulated observations, with the results obtained using the most recent and complete bathymetric data available. Two test study areas were considered, and the algorithm was found to consistently converge to a sampling pattern that best captured the bathymetric variability critical to the model prediction.

A Genetic Algorithm Application in Planning Path Using B-Spline Model for Autonomous Underwater Vehicle (AUV)

2020 ◽  
Vol 902 ◽  
pp. 54-64
Author(s):  
Ngoc Huy Tran ◽  
Anh Duy Nguyen ◽  
Thanh Nam Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Spline Approximation ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
B Spline ◽  
High Smoothness ◽  
Physical Limitations ◽  
Turning Radius

This paper proposes a method of using the B-spline mathematical model to plan high smoothness curve trajectories with heading condition through given waypoints for autonomous underwater vehicles (AUVs) in particular and ships with rudder systems in general. In addition, this paper examines some of the physical limitations of this vehicle, which lead to some binding conditions of the trajectory. Besides, the paper applies B-spline approximation method to reduce the curvature of the trajectory, when waypoints are too close and we do not need to go through exactly these waypoints in the 2D plane. Finally, this paper also proposes the genetic algorithm application with some modifications to solve the optimization of B-spline path length with constraint in turning radius of the vehicle.

scholarly journals Quantum genetic sliding mode controller design for depth control of an underwater vehicle

2018 ◽  
Vol 51 (7-8) ◽  
pp. 336-348 ◽  
Author(s):  
Zahra Tavanaei-Sereshki ◽  
Mohammad Reza Ramezani-al
Keyword(s):  
Genetic Algorithm ◽  
Controller Design ◽  
Sliding Mode ◽  
Tracking Error ◽  
Underwater Vehicle ◽  
Small Population ◽  
Control Law ◽  
Quantum Genetic Algorithm ◽  
Depth Control ◽  
New Type

Quantum genetic algorithm (QGA) is an optimization algorithm based on the probability that combines the idea of quantum computing and traditional genetic algorithm. In this paper, a new type of control law is developed for an underwater vehicle along with the desired path. The proposed controller is based on sliding mode control (SMC) in which the reaching law is modified to overcome two challenging problems, chattering, and sensitivity against noise. The disturbance is considered as a set of sinus waves with different frequencies which its parameters are estimated by Particle Swarm Optimization (PSO). Since QGA has some advantages such as fast convergence speed, small population size, and strong global search capabilities, we use QGA to determine the gain of the proposed controller. Finally, the Lyapunov theorem is used to prove that trajectory-tracking error converges to zero. Simulation results show that QGA can converge to the optimal response with a population consist of one chromosome.

scholarly journals A Novel Network Scheduling Approach Based on Genetic Algorithm for Autonomous Underwater Vehicle Control

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 111053-111064 ◽  
Author(s):  
Le Li ◽  
Weidong Liu ◽  
Li-E Gao ◽  
Yangyang Zhang ◽  
Zeyu Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Vehicle Control ◽  
Network Scheduling ◽  
Underwater Vehicle Control

scholarly journals Ordering autonomous underwater vehicle inspection locations with a genetic algorithm

2010 ◽  
Author(s):  
Brandon Morton ◽  
Terence Soule ◽  
Anthony Kanago ◽  
James Frenzel ◽  
Dean Edwards
Keyword(s):  
Genetic Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Vehicle Inspection
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