Learning closed loop kinematic controllers for continuum manipulators in unstructured environments

This paper introduces a machine learning based approach for closed loop kinematic control of continuum manipulators in the task space. For this purpose we propose a unique formulation for learning the inverse kinematics of a continuum manipulator while integrating end effector feedback. We demonstrate that this model-free approach for kinematic control is very well suited for nonlinear stochastic continuum robots. Specifically, the paper addresses problems which are vital for practical realization of machine learning techniques. The primary objective is to solve the redundancy problem while making the algorithm scalable, fast, tolerant to stochasticity, requires minimal sensor elements and involves few open parameters for tuning. In addition, we present that the proposed controller can exhibit adaptive behaviour in the presence of external forces and unstructured environment with the help of the morphological properties of the manipulator. Experimental validation of the proposed controller is done on a 6 Degree of Freedom tendon driven manipulator control of the end effector in three dimensional space with and without external forces. The experimental results exhibit accurate, reliable and adaptive behaviour of the proposed system which appears suitable for the field of continuum service robots