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.

Control Strategies for Soft Robotic Manipulators: A Survey

“…Recent model-free approaches have mostly focused on learning the IK representation of continuum robots. In Ref., 44 an approach for learning the direct mapping between task space and joint space (potentiometer voltage, in this case) is proposed. This involves learning the forward kinematic model first using a neural network and then inverting this learned network using distal supervised learning.…”

Novel Design and Position Control Strategy of a Soft Robot Arm

“…Amongst this research, Melingui, et al [35] used an adaptive NN to control a compact bionic handling arm (CBHA). Melingui, et al [36] used the NN to find the inverse kinematics model of the CBHA by mapping the task data space and the actual joint space. Melingui, et al [37] used the Radial Basis Function (RBF) and the Multilayer Perceptron (MLP) NN to find the forward kinematics model (FKM) of the CBHA.…”

A metaheuristic approach to solve inverse kinematics of continuum manipulators

“…On the other hand, a modeling approach that uses constant/variable curvature continuum kinematics for a class of continuum robots has been proposed by Mahl et al 17 The validation of this model has been done on a robot named ''Bionic Handling Assistant (BHA designed by Festo Company. 18 Other authors such as Melingui et al 19 have used neural networks to approximate the IKM of the Compact Bionic Handling Arm (CBHA) manipulator. With the same idea, another contribution is suggested by Lakhal et al, 20 where the authors have used a qualitative approach based on neural networks cited in works by Melingui et al 19,21 for modeling the approximate behavior of the CBHA manipulator.…”

“…18 Other authors such as Melingui et al 19 have used neural networks to approximate the IKM of the Compact Bionic Handling Arm (CBHA) manipulator. With the same idea, another contribution is suggested by Lakhal et al, 20 where the authors have used a qualitative approach based on neural networks cited in works by Melingui et al 19,21 for modeling the approximate behavior of the CBHA manipulator. Also, Giorelli et al 22 have applied the same feedforward neural networks to approximate the IKM of a soft manipulator.…”