State Estimation for Continuum Multirobot Systems on SE(3)

IF 9.4 1区 计算机科学 Q1 ROBOTICS
Sven Lilge;Timothy Barfoot;Jessica Burgner-Kahrs
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引用次数: 0

Abstract

In contrast to conventional robots, accurately modeling the kinematics and statics of continuum robots is challenging due to partially unknown material properties, parasitic effects, or unknown forces acting on the continuous body. Consequentially, state estimation approaches that utilize additional sensor information to predict the shape of continuum robots have garnered significant interest. This article presents a novel approach to state estimation for systems with multiple coupled continuum robots, which allows estimating the shape and strain variables of multiple continuum robots in an arbitrary coupled topology. Simulations and experiments demonstrate the capabilities and versatility of the proposed method, while achieving accurate and continuous estimates for the state of such systems, resulting in average end-effector errors of 3.3 mm and 5.02$^\circ$ depending on the sensor setup. It is further shown, that the approach offers fast computation times of below 10 ms, enabling its utilization in quasi-static real-time scenarios with average update rates of 100–200 Hz. An open-source C++ implementation of the proposed state estimation method is made publicly available to the community.
基于SE(3)的连续统多机器人系统状态估计
与传统机器人相比,由于部分未知的材料特性、寄生效应或作用在连续体上的未知力,连续体机器人的运动学和静力学的准确建模是具有挑战性的。因此,利用附加传感器信息来预测连续体机器人形状的状态估计方法已经引起了人们的极大兴趣。本文提出了一种新的连续统机器人系统状态估计方法,该方法允许在任意耦合拓扑中估计多个连续统机器人的形状和应变变量。仿真和实验证明了所提出方法的能力和通用性,同时实现了对此类系统状态的准确和连续估计,根据传感器设置,导致平均末端执行器误差为3.3 mm和5.02$^\circ$。进一步表明,该方法提供了低于10 ms的快速计算时间,使其能够在平均更新率为100-200 Hz的准静态实时场景中使用。所建议的状态估计方法的开源c++实现已向社区公开提供。
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
IEEE Transactions on Robotics
IEEE Transactions on Robotics 工程技术-机器人学
CiteScore
14.90
自引率
5.10%
发文量
259
审稿时长
6.0 months
期刊介绍: The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles. Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.
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