A study on position control of continuum arm using MAML(Model-Agnostic Meta-Learning) for adapting changing conditions
preprintposted on 26.06.2021, 07:24 by Alok Sahoo, pavan chakraborty
Non linear deformation of spring based continuum manipulators cause difficulty in predicting the position of the tip. When we put different tools on the tip for various purposes, the difficulty further increases. Model less control of the manipulator has shown great success in tip positioning of these types of manipulators. One of the major drawbacks of model less control is the requirement of a large data set and time. Hence, this paper studies the effect of the implementation of MAML(Model-Agnostic Meta-Learning) for fast adaptation of different offset conditions. The effects have been studied in the simulation environment and on the real prototype. The continuum arm used for the experimentation is a tendon based non constant curvature spring based manipulator. An average error of 0.03m has been achieved on the prototype. MAML was successful in bringing down the relative tip positioning error of the manipulator from 7.02% to 1.55% in the simulation environment. It also showed success in bringing down the relative tip positioning error from 11.06% to 4.09% on real prototype. We also studied the effectiveness of the same in trajectory following.