Tactile sensor-less fingertip contact detection and force estimation for stable grasping with an under-actuated hand

IF 1.5 Q3 INSTRUMENTS & INSTRUMENTATION
Ha Thang Long Doan, Hikaru Arita, Kenji Tahara
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引用次数: 0

Abstract

Detecting contact when fingers are approaching an object and estimating the magnitude of the force the fingers are exerting on the object after contact are important tasks for a multi-fingered robotic hand to stably grasp objects. However, for a linkage-based under-actuated robotic hand with a self-locking mechanism to realize stable grasping without using external sensors, such tasks are difficult to perform when only analyzing the robot model or only applying data-driven methods. Therefore, in this paper, a hybrid of previous approaches is used to find a solution for realizing stable grasping with an under-actuated hand. First, data from the internal sensors of a robotic hand are collected during its operation. Subsequently, using the robot model to analyze the collected data, the differences between the model and real data are explained. From the analysis, novel data-driven-based algorithms, which can overcome noted challenges to detect contact between a fingertip and the object and estimate the fingertip forces in real-time, are introduced. The proposed methods are finally used in a stable grasp controller to control a triple-fingered under-actuated robotic hand to perform stable grasping. The results of the experiments are analyzed to show that the proposed algorithms work well for this task and can be further developed to be used for other future dexterous manipulation tasks.
无需触觉传感器的指尖接触检测和力估算,实现动力不足手的稳定抓取
检测手指接近物体时的接触情况以及估计接触后手指对物体施加的力的大小,是多指机械手稳定抓取物体的重要任务。然而,对于具有自锁机构的基于联动的欠动机械手来说,要在不使用外部传感器的情况下实现稳定抓取,仅分析机器人模型或仅应用数据驱动方法很难完成这些任务。因此,本文采用了一种混合前人方法的方法来寻找实现欠动手稳定抓取的解决方案。首先,在机械手运行过程中收集其内部传感器的数据。随后,利用机器人模型对收集到的数据进行分析,解释模型与真实数据之间的差异。通过分析,介绍了基于数据驱动的新型算法,该算法可以克服检测指尖与物体之间的接触以及实时估算指尖力方面的挑战。最后将所提出的方法用于稳定抓取控制器,以控制三指欠动机械手进行稳定抓取。实验结果分析表明,所提出的算法在这项任务中运行良好,并可进一步开发用于未来的其他灵巧操纵任务。
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
ROBOMECH Journal
ROBOMECH Journal Mathematics-Control and Optimization
CiteScore
3.20
自引率
7.10%
发文量
21
审稿时长
13 weeks
期刊介绍: ROBOMECH Journal focuses on advanced technologies and practical applications in the field of Robotics and Mechatronics. This field is driven by the steadily growing research, development and consumer demand for robots and systems. Advanced robots have been working in medical and hazardous environments, such as space and the deep sea as well as in the manufacturing environment. The scope of the journal includes but is not limited to: 1. Modeling and design 2. System integration 3. Actuators and sensors 4. Intelligent control 5. Artificial intelligence 6. Machine learning 7. Robotics 8. Manufacturing 9. Motion control 10. Vibration and noise control 11. Micro/nano devices and optoelectronics systems 12. Automotive systems 13. Applications for extreme and/or hazardous environments 14. Other applications
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