The Development of MOPSO-Based Dynamic Routing Algorithm for the Inspection of Autonomous Underwater Vehicle

2017 ◽  
Author(s):  
Yu-Hsien Lin ◽  
Lin-Chin Huang ◽  
Shao-Yu Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Autonomous Underwater Vehicle ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Dynamic Routing ◽  
Ocean Currents ◽  
Modular Structure ◽  
Swarm Optimization ◽  
Fixed Weight

In this study, the authors developed the dynamic routing algorithm combining an image detection technique to support the optimal route plan of Autonomous Underwater Vehicle (AUV) inspecting an offshore wind farm affected by ocean currents. A modular structure is applied to program design by the graphical language, LabVIEW (Laboratory Virtual Instrument Engineering Workbench). The modular structure is composed of 6-DOF (Six Degrees-of-Freedom) motion module, a self-tuning fuzzy control module, a stereo-vision detection module, and a dynamic routing module. In terms of path planning for inspection, several Pareto frontiers are solved iteratively according to two objectives, namely, cruise time and energy consumption. Performances obtained from MOPSO (Multi-Objective Particle Swarm Optimization) -based dynamic routing algorithm would be in comparison with those from SOPSO (Single-Objective Particle Swarm Optimization) -based dynamic routing algorithm. In addition, selections of fixed weight and dynamic weight of MOPSO-based dynamic routing algorithms would be discussed in the environment with or without ocean currents. Eventually, the image inspection mode is not only beneficial for optimizing feasible routes but it can also identify features of obstacles for positioning.

scholarly journals Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

2019 ◽  
Vol 52 (21) ◽  
pp. 315-322 ◽  
Author(s):  
Hui Sheng Lim ◽  
Shuangshuang Fan ◽  
Christopher K.H. Chin ◽  
Shuhong Chai ◽  
Neil Bose ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Swarm Optimization

scholarly journals Path Planning for Unmanned Underwater Vehicle Based on Improved Particle Swarm Optimization Method

2018 ◽  
Vol 14 (12) ◽  
pp. 137
Author(s):  
Jianhua Xu ◽  
Hao Gu ◽  
Hongtao Liang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Potential Field ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Time Varying ◽  
Slowly Varying Function ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Unmanned Underwater Vehicle

Path planning of Unmanned Underwater Vehicle (UUV) is of considerable significance for the underwater navigation, the objective of the path planning is to find an optimal collision-free and the shortest trajectory from the start to the destination. In this paper, a new improved particle swarm optimization (IPSO) was proposed to process the global path planning in a static underwater environment for UUV. Firstly, the path planning principle for UUV was established, in which three cost functions, path length, exclusion potential field between the UUV and obstacle, and attraction potential field between UUV and destination, were considered and developed as an optimization objective. Then, on the basis of analysis traditional particle swarm optimization (PSO), the time-varying acceleration coefficients and slowly varying function were employed to improve performance of PSO, time-varying acceleration coefficients was utilized to balance the local optimum and global optimum, and slowly varying function was introduced into the updating formula of PSO to expand search space and maintain particle diversity. Finally, numerical simulations verify that, the proposed approach can fulfill path planning problems for UUN successfully.

scholarly journals A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haoqian Huang ◽  
Chao Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Synthesis Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Feedback Mechanism ◽  
Swarm Optimization ◽  
Learning Mechanism

In order to solve the problems of rapid path planning and effective obstacle avoidance for autonomous underwater vehicle (AUV) in 2D underwater environment, this paper proposes a path planning algorithm based on reinforcement learning mechanism and particle swarm optimization (RMPSO). A feedback mechanism of reinforcement learning is embedded into the particle swarm optimization (PSO) algorithm by using the proposed RMPSO to improve the convergence speed and adaptive ability of the PSO. Then, the RMPSO integrates the velocity synthesis method with the Bezier curve to eliminate the influence of ocean currents and save energy for AUV. Finally, the path is developed rapidly and obstacles are avoided effectively by using the RMPSO. Simulation and experiment results show the superiority of the proposed method compared with traditional methods.

Improving AODV route recovery mechanism using PSO in WSN

2019 ◽  
Vol 09 ◽  
Author(s):  
Sunil Kumar K N ◽  
Shiva Shankar
Keyword(s):  
Particle Swarm Optimization ◽  
Network Performance ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Delivery Ratio ◽  
Swarm Optimization ◽  
Packet Delivery ◽  
Control Overhead ◽  
Route Failure

Objective: The conventional Ad Hoc On-Demand Distance Vector (AODV) routing algorithm, route discovery methods pose route failure resulting in data loss and routing overhead. In the proposed method, needs significant low energy consumption while routing from one node to another node by considering the status of node forwards the packet. So that while routing it avoids unnecessary control overhead and improves the network performance. Methods: Particle Swarm Optimization (PSO) algorithm is a nature- inspired, population-based algorithm. Particle Swarm Optimization (PSO) is a Computational Intelligence technique which optimizes the objective function. It works by considering that every member of the swarm contributes in finding the ideal solution by keeping a track of their own best known location and the best-known location of the group and keeps updating them whenever there is a change and hence minimizes the objective fitness function. The fitness function which we considered here is the Node lifetime, Link Lifetime and available Bandwidth. If these parameters are with good then status of node will be strong and hence routing of packet over those nodes will reduce delay and improves network performance. Result: To verify the feasibility and effectiveness of our proposal, the routing performance of AODV and PSO-AODV is compared with respect to various network metrics like Network Lifetime, packet delivery ratio and routing overhead and validated the result by comparing both routing algorithm using Network Simulator 2. The results of the PSO-AODV has outperformed the AODV in terms of low energy, less end to end delay and high packet delivery ratio and less control overhead. Conclusion: Here we proposed to use Particle Swarm Optimization in order to obtain the more suitable parameters for the decision making. The existing AODV protocol was modified to make a decision to recover from route failure; at the link failure predecessor node implementing PSO based energy prediction concept and using weights for each argument considered in the decision function. The fitness values for each weight were found through PSO basic form. We observed that the PSO showed satisfactory behaviour improvement than the performance of AODV for all metrics on the investigated scenarios.

Sign in / Sign up

Export Citation Format

Share Document