Qualitative approach for inverse kinematic modeling of a Compact Bionic Handling Assistant trunk

Abstract

Compact Bionic Handling Assistant (CBHA) is a continuum manipulator, with pneumatic-based actuation and compliant gripper. This bionic arm is attached to a mobile robot named Robotino. Inspired by the elephant's trunk, it can reproduce biological behaviors of trunks, tentacles, or snakes. Unlike rigid link robot manipulators, the development of high performance control algorithm of continuum robot manipulators remains a challenge, particularly due to their complex mechanical design, hyper-redundancy and presence of uncertainties. Numerous studies have been investigated for modeling of such complex systems. Such continuum robots, like the CBHA present a set of nonlinearities and uncertainties, making difficult to build an accurate analytical model, which can be used for control strategies development. Hence, learning approach becomes a suitable tool in such scenarios in order to capture un-modeled nonlinear behaviors of the continuous robots. In this paper, we present a qualitative modeling approach, based on neuronal model of the inverse kinematic of CBHA. A penalty term constraint is added to the inverse objective function into Distal Supervised Learning (DSL) scheme to select one particular inverse model from the redundancy manifold. The inverse kinematic neuronal model is validated by conducting a real-time implementation on a CBHA trunk.