English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Due to the lack of transparent and friendly human–robot interaction (HRI) interface, as well as various uncertainties, it is usually a challenge to remotely manipulate a robot to accomplish a complicated task. To improve the teleoperation performance, we propose a new perception mechanism by integrating a novel learning method to operate the robots in the distance. In order to enhance the perception of the teleoperation system, we utilize a surface electromyogram signal to extract the human operator’s muscle activation. As a response to the changes in the external environment, as sensed through haptic and visual feedback, a human operator naturally reacts with various muscle activations. By imitating the human behaviors in task execution, not only motion trajectory but also arm stiffness adjusted by muscle activation, it is expected that the robot would be able to carry out the repetitive tasks autonomously or uncertain tasks with improved intelligence. To this end, we develop a robot learning algorithm based on probability statistics under an integrated framework of the hidden semi-Markov model (HSMM) and the Gaussian mixture method. This method is employed to obtain a generative task model based on the robot’s trajectory. Then, Gaussian mixture regression based on HSMM is applied to correct the robot trajectory with the reproduced results from the learned task model. The execution procedures consist of a learning phase and a reproduction phase. To guarantee the stability, immersion, and maneuverability of the teleoperation system, a variable gain control method that involves electromyography (EMG) is introduced. Experimental results have demonstrated the effectiveness of the proposed method. Note to Practitioners—This paper is inspired by the limitations of teleoperation to perform a task with unfriendly HRI and lack of intelligence. The human operators need to concentrate on the manipulation in the traditional setup of a teleoperation system; thus, it is quite a labor intensive for a human operator. This is a huge challenge for the requirement of increasingly complicated, diverse tasks in teleoperation. Therefore, efficient ways of the robot intelligence need to be urgently developed for the telerobots. In this paper, we develop a robot intelligence framework by merging robot learning technology and perception mechanism. The proposed framework is effective where the task performed with repeatability and rapidity in a teleoperated mode. The proposed method includes three following ideas: 1) remote operation information can be actively sensed by infusing muscle activation with a haptics EMG perception mechanism; 2) the robot intelligence can be enhanced by employing a robot learning method. The developed approach is verified by the experimental results; and 3) the proposed method can be potentially used for telemanufacturing, teletehabilitation, and telemedicine, and so on. In our future work, more interactive information between humans and telerobots should be taken into consideration in the telerobot perception system to enhance the robot intelligence.

Haptics Electromyography Perception and Learning Enhanced Intelligence for Teleoperated Robot