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Mechanism-Parameters Optimization of a Reconfigurable Tracked Mobile Modular Deep-Sea Rover ROV

机译:可重构履带式模块化深海漫游车ROV的机构参数优化

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It is extremely difficult and expensive to operate a traditional remotely operated vehicle (ROV) in the deep sea. In this paper, a small and reconfigurable tracked mobile modular deep-sea rover ROV (R-ROV) is designed to conduct regional fine-sounding operations. The R-ROV can be placed inside a deep-sea lander, which can be operated in 3000m depth seafloor. When the lander lands seafloor, R-ROV can walk out in crawling or cruising mode operated in a fan-shaped area with long working time. In order to obtain mechanism-parameters and improve R-ROV's adaptability and motility, multi-objective genetic algorithm is used in this paper. So the relationship between the objective functions and mechanism-parameters of the transformable vehicle is established. There are two typical objective obstacles for the crawl mode R-ROV, stair and gully. However, different from the robot walking on land, it is difficult to find the optimal configuration across the stairs underwater because of the buoyancy of water. This paper proposes a method to obtain optimized mechanism-parameters for the R-ROV considering the underwater environment, which has been calculated through simulation. The results are reported in detail.
机译:在深海中操作传统的遥控车(ROV)极其困难且昂贵。在本文中,设计了一种小型且可重新配置的履带式移动模块化深海漫游车ROV(R-ROV),以进行区域精细探测。 R-ROV可以放置在深海着陆器内部,该着陆器可以在3000m深的海底中操作。当着陆器着陆于海底时,R-ROV可以在爬行或巡航模式下走出扇形区域,且工作时间较长。为了获得机构参数,提高R-ROV的适应性和机动性,本文采用了多目标遗传算法。因此,建立了变形车辆目标函数与机构参数之间的关系。爬行模式R-ROV有两个典型的客观障碍,即楼梯和沟壑。但是,与在地面上行走的机器人不同,由于水的浮力,很难在水下的楼梯上找到最佳配置。本文提出了一种通过模拟计算得出的考虑水下环境的R-ROV优化机构参数的方法。结果被详细报告。

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