Constraint-augmented Kalman Filter for Magnetometer-free 3D Joint Angle Determination

Lee, Jung Keun; Jeon, Tae Hyeong; Jung, Woo Chang

doi:10.1007/s12555-019-0948-x

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…In this paper, to smooth the value of the flexion level to improve the robustness and steadiness during the teleoperation control of the underactuated bionic hand, a real-time linear Kalman Filter [30] was utilised. The Kalman filter has been widely utilised in many wearable robotic systems, including lower limb wearable robotic devices [31] and 3D joint angle determination [32]. In this paper, the Kalman filter acts as a recursive algorithm based on a state space representation of the finger motion.…”

Section: Wearable-glove-based Motion Trackingmentioning

confidence: 99%

Teleoperation Control of an Underactuated Bionic Hand: Comparison between Wearable and Vision-Tracking-Based Methods

Poletti

Liu

et al. 2022

Robotics

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Bionic hands have been employed in a wide range of applications, including prosthetics, robotic grasping, and human–robot interaction. However, considering the underactuated and nonlinear characteristics, as well as the mechanical structure’s backlash, achieving natural and intuitive teleoperation control of an underactuated bionic hand remains a critical issue. In this paper, the teleoperation control of an underactuated bionic hand using wearable and vision-tracking system-based methods is investigated. Firstly, the nonlinear behaviour of the bionic hand is observed and the kinematics model is formulated. Then, the wearable-glove-based and the vision-tracking-based teleoperation control frameworks are implemented, respectively. Furthermore, experiments are conducted to demonstrate the feasibility and performance of these two methods in terms of accuracy in both static and dynamic scenarios. Finally, a user study and demonstration experiments are conducted to verify the performance of these two approaches in grasp tasks. Both developed systems proved to be exploitable in both powered and precise grasp tasks using the underactuated bionic hand, with a success rate of 98.6% and 96.5%, respectively. The glove-based method turned out to be more accurate and better performing than the vision-based one, but also less comfortable, requiring greater effort by the user. By further incorporating a robot manipulator, the system can be utilised to perform grasp, delivery, or handover tasks in daily, risky, and infectious scenarios.

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Section: Wearable-glove-based Motion Trackingmentioning

confidence: 99%