Towards optimal hydro-blasting in reconfigurable climbing system for corroded ship hull cleaning and maintenance

被引:24
|
作者
Anh Vu Le [1 ,2 ]
Veerajagadheswar, Prabakaran [1 ]
Kyaw, Phone Thiha [1 ,3 ]
Muthugala, M. A. Viraj J. [1 ]
Elara, Mohan Rajesh [1 ]
Kuma, Madhu [4 ]
Nguyen Huu Khanh Nhan [2 ]
机构
[1] Singapore Univ Technol & Design, ROAR Lab, Engn Prod Dev, Singapore 487372, Singapore
[2] Ton Duc Thang Univ, Fac Elect & Elect Engn, Optoelect Res Grp, Ho Chi Minh City 700000, Vietnam
[3] Yangon Technol Univ, Dept Mechatron Engn, Insein, Myanmar
[4] Brightsun Marine Pte Ltd, 9 Tuas Ave 8, Singapore 639224, Singapore
关键词
Benchmarking hydro-blasting; Industrial automation; Ship hull maintenance; Reconfigurable robotics; Ship maintenance industry; COMPLETE COVERAGE; VISUAL SLAM; ROBOT; INSPECTION; DESIGN; OPTIMIZATION; TRACKING; HTETRO; MODEL;
D O I
10.1016/j.eswa.2020.114519
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The operation of a ship in the ocean depends crucially on the quality of routine offshore dry dock maintenance. Automation by robotics is an efficient solution to address the issues of saving water, energy, time, and easing the labour workload when conducting hydro-blasting hulls in the dry dock ship maintenance industry. In this paper, the automated hydro-blasting in corroded ship hull cleaning by a novel robot platform with reconfigurable manipulators named Hornbill is proposed. The robot is able to maneuver smoothly on a vertical surface by permanent magnetic force, to carry the heavy load, to clean the corroded ship hull by hydro-blasting, and to self evaluate hydro-blasting task by leveraging the Deep Convolutional Neural Network (DCNN) to synthesis the corrosion level map of the blasted workspace. We also propose an optimal complete waypoint path planning (CWPP) framework to help the robot re-blast the benchmarked workspace. The optimal CWPP problem, including objective functions of the shortest travel distance, the least upward moving direction to reduce water, energy spent while ensuring the visiting of the robot to all uncleaned waypoints defined by benchmarking output, is modeled as the classic Travel Salesman Problem (TSP). The evolutionary-based optimization techniques, including Genetic Algorithm (GA) and Ant Colony Optimization (ACO), are explored to derive the Paretooptima solution for given TSP. The experimental results show that the magnetic force and motors torque are synchronized to enable the proposed system to navigate smoothly on the vertical surfaces tested with different corrosion levels. The proposed corrosion level benchmarking achieves a mean accuracy of 0.956 with an execution time of 30 fps. Besides, the proposed CWPP enables the proposed robot to yield about 15%, 26%, and 5% the energy, water, and time, respectively, less than the conventional methods when the experiments are conducted in various workspaces on the real ship hull.
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页数:14
相关论文
共 3 条
  • [1] Hornbill: A Self-Evaluating Hydro-Blasting Reconfigurable Robot for Ship Hull Maintenance
    Prabakaran, Veerajagadheswar
    Vu Le, Anh
    Kyaw, Phone Thiha
    Mohan, Rajesh Elara
    Kandasamy, Prathap
    Nguyen, Tan Nhat
    Kannan, Madhukumar
    [J]. IEEE ACCESS, 2020, 8 (08): : 193790 - 193800
  • [2] Uniform hydro blasting for ship hull maintenance: A multi-objective optimization framework
    Ghanta, Sriharsha
    Rayguru, Madan Mohan
    Pathmakumar, Thejus
    Kalimuthu, Manivannan
    Elara, Mohan Rajesh
    Sheu, Bing J.
    [J]. OCEAN ENGINEERING, 2021, 242
  • [3] Reinforcement learning-based optimal complete water-blasting for autonomous ship hull corrosion cleaning system
    Anh Vu Le
    Phone Thiha Kyaw
    Veerajagadheswar, Prabakaran
    Muthugala, M. A. Viraj J.
    Elara, Mohan Rajesh
    Kumar, Madhu
    Nguyen Huu Khanh Nhan
    [J]. OCEAN ENGINEERING, 2021, 220