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

机译:用于腐蚀船船体清洗和维护中可重构攀岩系统的最佳水力爆破

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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.
机译:海洋中船舶的运作依赖于常规海上干码头维护的质量。机器人的自动化是一种有效的解决方案,可以解决在干码头船舶维修行业进行水上爆破的船体时节约水,能源,时间和缓解劳动力工作的问题。本文提出了一种新颖的机器人平台与可重新配置的机械手的腐蚀船船体清洁的自动爆破,其名为Hornbill。机器人能够通过永久磁力平滑地操纵垂直表面,以携带重载,通过水力爆破清洁腐蚀船船体,并通过利用深卷积神经网络来自我评估水力爆破任务(DCNN )合成爆破的工作空间的腐蚀水平图。我们还提出了一个最佳的完整航路点路径规划(CWPP)框架,以帮助机器人重新爆炸基准工作空间。最佳的CWPP问题,包括最短行程距离的客观功能,降低水的最小移动方向,在确保机器人访问机器人到由基准输出定义的所有未切割的航点时,被建模为经典旅行推销员问题( TSP)。探索包括遗传算法(GA)和蚁群优化(ACO)的基于进化的优化技术,以导出给定TSP的ParopoOptima解决方案。实验结果表明,磁力和电动机扭矩同步以使得所提出的系统能够在用不同腐蚀水平测试的垂直表面上平稳地导航。所提出的腐蚀水平基准测试实现了0.956的平均准确性,执行时间为30 FPS。此外,所提出的CWPP使提出的机器人能够分别产生约15%,26%和5%的能量,水和时间,而实验在实验在真正的船船体上的各种工作空间中进行。

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