Robotics & Machine Learning Daily News2024,Issue(Jun.3) :33-34.

Reports Summarize Robotics Findings from University of the Chinese Academy of Sc iences (Robust Depth and Heading Control System for a Novel Robotic Dolphin With Multiple Control Surfaces)

报告总结了中国科学院大学的机器人学研究成果(具有多个控制面的新型机器人海豚的鲁棒深度和航向控制系统)

Robotics & Machine Learning Daily News2024,Issue(Jun.3) :33-34.

Reports Summarize Robotics Findings from University of the Chinese Academy of Sc iences (Robust Depth and Heading Control System for a Novel Robotic Dolphin With Multiple Control Surfaces)

报告总结了中国科学院大学的机器人学研究成果(具有多个控制面的新型机器人海豚的鲁棒深度和航向控制系统)

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摘要

由一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于机器人的最新研究结果已经发表。根据NewsRx记者从北京发来的消息,研究表明:“对于野外任务,水下机器人,特别是具有多个操纵面的水下机器人,由于响应和遗传算法的不同,难以在复杂的环境中操作,为此,本文开发了一种高度集成的海豚机器人,并设计了一套鲁棒的运动控制系统。”本报记者引用了中国科学院大学的一篇研究报告:“为了提高机器人的机动性和容错能力,设计了一种新型的海豚机器人,该海豚机器人具有多个传感器阵列和多个控制面,特别采用被动吸爪,在此基础上提出了一种鲁棒运动控制系统。”针对被动吸虫引起的运动性能下降的问题,设计了基于速度相关分配策略的深度控制器和基于间隙补偿的航向控制器,并针对惯性吸虫响应特性设计了滑模控制器f或增益不确定性以及与分配相关的参数整定策略,进行了大量的仿真和水上实验。该研究为进一步开发具有鲁棒运动系统的海豚机器人在野外执行复杂任务奠定了基础。摘要:被动尾翼和红色舵面。传统的控制方法往往容易受到被动尾翼增益的影响,导致控制性能下降。由于不同舵面特性的不同,往往会导致控制振荡和收敛速度慢。本文提出了一种基于水平位相关分配策略的鲁棒深度控制器和基于水平位差补偿的鲁棒航向控制器,具体地说,考虑输入响应特性,包括响应速度、饱和度和水动力变化规律,给出了一种与分配相关的参数整定方法,以保证航向控制的良好调节。提出了一种基于非线性干扰观测器(ndob)的噪声补偿方法,并在新设计的海豚机器人上进行了大量的水下实验,验证了该方法的有效性。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news originating from Beijing, People’s Republic o f China, by NewsRx correspondents, research stated, “For field tasks, it is quit e challenged to operate in a complex environment for the underwater robots, espe cially for those with multiple control surfaces due to different response and ga in characteristics. To this end, this paper develops a highly integrated robotic dolphin followed by a robust motion control system.” Our news journalists obtained a quote from the research from the University of t he Chinese Academy of Sciences, “For better maneuverability and fault-tolerant c apabilities, a newly-designed robotic dolphin is presented, owning a wide array of sensors and multiple control surfaces, in which passive flukes are particular ly applied. On this basis, a robust motion control system is proposed, including a depth controller based on velocity-related allocation strategies and a headin g controller based on clearance compensation. In detail, considering the degrada tion of motion performance caused by passive flukes, a sliding mode controller f or gain uncertainty and an allocation-related parameter tuning strategy for inpu ts response characteristics are designed. Extensive simulations and aquatic expe riments are conducted, and the obtained results demonstrate the satisfied maneuv erability of the designed prototype and the effectiveness of the proposed method s. This study can lay a foundation for further development of robotic dolphins w ith a robust motion system to execute complex tasks in the field. Note to Practi tioners-This paper is inspired by the issue of robust motion control system for a newly-designed practical robotic dolphin that possesses a passive tail and red undant control surfaces. The traditional methods are usually susceptible to unce rtainties in the passive tail gain, exhibiting degraded control performance. Mor eover, control oscillations and slow convergence speed often occur caused by neg lecting the characteristics of different control surfaces, including response pa tterns and clearance. This paper suggests a robust depth controller based on vel ocity-related allocation strategies and a robust heading controller based on cle arance compensation. Specifically, an allocationrelated parameter tuning strate gy is given by considering inputs response characteristics, including response s peed, saturations, and hydrodynamic force variation patterns. To guarantee fine regulations of heading control, a nonlinear disturbance observer (NDOB)-based cl earance compensation is proposed. Extensive aquatic experiments on the newly-des igned robotic dolphin verified the effectiveness of the proposed methods.”

Key words

Beijing/People’s Republic of China/Asi a/Emerging Technologies/Machine Learning/Robotics/Robots/University of the Chinese Academy of Sciences

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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