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arXiv 2201.03719cs.RO
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模块化机器人位置估计的低成本、高度可定制解决方案

A Low-Cost, Highly Customizable Solution for Position Estimation in Modular Robots

University of Pennsylvania(宾夕法尼亚大学)

Chao Liu, Tarik Tosun, Mark Yim

摘要:

精确的位置传感对于机器人的状态估计和控制是非常重要的。可靠且精确的位置传感器通常价格昂贵且难以定制。将它们组装到具有非常严格的体积限制的系统中,例如模块化机器人,是特别困难的。PaintPots是低成本、可靠且高度可定制的位置传感器,但其性能高度依赖于制造和校准过程。本文提出了一种卡尔曼滤波器与一个简化的观察模型,以处理非线性问题,导致使用低成本的微控制器。此外,一个完整的解决方案,使用PaintPots在各种传感模式,包括制造,表征和估计,提出了一个示例模块化机器人,SMORES-EP。该解决方案可轻松适应广泛的应用。

英文摘要:

Accurate position sensing is important for state estimation and control in robotics. Reliable and accurate position sensors are usually expensive and difficult to customize. Incorporating them into systems that have very tight volume constraints such as modular robots are particularly difficult. PaintPots are low-cost, reliable, and highly customizable position sensors, but their performance is highly dependent on the manufacturing and calibration process. This paper presents a Kalman filter with a simplified observation model developed to deal with the non-linearity issues that result in the use of low-cost microcontrollers. In addition, a complete solution for the use of PaintPots in a variety of sensing modalities including manufacturing, characterization, and estimation is presented for an example modular robot, SMORES-EP. This solution can be easily adapted to a wide range of applications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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