Learning Closed Loop Kinematic Controllers for Continuum Manipulators in Unstructured Environments.

This article 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. The article addresses problems that are vital for practical realization of machine-learning techniques. The primary objective is to solve the redundancy problem while making the algorithm scalable, fast, and tolerant to stochasticity, requiring minimal sensor elements and involving few open parameters for tuning. In addition, we demonstrate that the proposed controller can exhibit adaptive behavior in the presence of external forces and in an unstructured environment with the help of the morphological properties of the manipulator. Experimental validation of the proposed controller is done on a six-degree-of-freedom tendon-driven manipulator for pose control of the end effector in three-dimensional space with and without external forces. The experimental results exhibit accurate, reliable, and adaptive behavior of the proposed system, which appears suitable for the field of continuum service robots.