Enabling a Freely Accessible Open Source Remotely Controlled Robotic Articulator with a Neuro-Inspired Control Algorithm

Asha Vijayan, Chaitanya Nutakki, Dhanush Kumar, Krishnashree Achuthan, Bipin Nair, Shyam Diwakar

Abstract


Internet-enabled technologies for robotics education are gaining importance as online platforms promoting skill training.  Understanding the use and design of robotics are now introduced at university undergraduate levels, but in developing economies establishing usable hardware and software platforms face several challenges like cost, equipment etc. Remote labs help providing alternatives to some of the challenges. We developed an online laboratory for bioinspired robotics using a low-cost 6 degree-of-freedom robotic articulator with a neuro-inspired controller. Cerebellum-inspired neural network algorithm approximates forward and inverse kinematics for movement coordination. With over 210000 registered users, the remote lab has been perceived as an interactive online learning tool and a practice platform. Direct feedback from 60 students and 100 university teachers indicated that the remote laboratory motivated self-organized learning and was useful as teaching material to aid robotics skill education.

Keywords


remote labs; robotic articulator; neural network; open source; ICT

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International Journal of Online Engineering (iJOE).ISSN: 1861-2121
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