Path Following for the Soft Origami Crawling Robot

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9175

O. Angatkina, K. Gustafson, A. Wissa, A. Alleyne

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引用次数: 1

Abstract

Extensive growth of the soft robotics field has made possible the application of soft mobile robots for real world tasks such as search and rescue missions. Soft robots provide safer interactions with humans when compared to traditional rigid robots. Additionally, soft robots often contain more degrees of freedom than rigid ones, which can be beneficial for applications where increased mobility is needed. However, the limited number of studies for the autonomous navigation of soft robots currently restricts their application for missions such as search and rescue. This paper presents a path following technique for a compliant origami crawling robot. The path following control adapts the well-known pure pursuit method to account for the geometric and mobility constraints of the robot. The robot motion is described by a kinematic model that transforms the outputs of the pure pursuit into the servo input rotations for the robot. This model consists of two integrated sub-models: a lumped kinematic model and a segmented kinematic model. The performance of the path following approach is demonstrated for a straight-line following simulation with initial offset. Finally, a feedback controller is designed to account for terrain or mission uncertainties.

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柔性折纸爬行机器人的路径跟踪

软机器人领域的广泛发展使得软移动机器人应用于现实世界的任务，如搜索和救援任务成为可能。与传统的刚性机器人相比，软体机器人提供了更安全的人机交互。此外，软体机器人通常比刚性机器人包含更多的自由度，这对于需要增加机动性的应用是有益的。然而，目前软体机器人自主导航的研究数量有限，限制了软体机器人在搜救等任务中的应用。提出了一种柔性折纸爬行机器人的路径跟踪技术。路径跟踪控制采用了众所周知的纯追踪方法来考虑机器人的几何和移动约束。机器人的运动由一个运动学模型来描述，该模型将纯追逐的输出转化为机器人的伺服输入旋转。该模型由两个完整的子模型组成:集总运动模型和分段运动模型。通过具有初始偏移量的直线跟踪仿真，验证了路径跟踪方法的性能。最后，设计一个反馈控制器来考虑地形或任务的不确定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.40

自引率

66.70%

发文量

期刊介绍： This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.