This paper proposes a novel mathematical solution to solve the inverse kinematics (IK) of single section mobile continuum manipulators (SSMCMs). Thus, to achieve a given pose of the end-effector (EE), the proposed mathematical solution consists in determining the position and orientation parameters of the mobile platform and of a single section of the continuum manipulator. As advantages, the proposed mathematical solution eliminates the EE pose errors when the dynamic parameters are neglected and the continuum manipulator is cylindrical in shape. A simulation and an experiment validate the proposed approach.

Robotic 3D Concrete Printing (3DCP) is a process of additive manufacturing using building materials. The system that performs 3DCP is a complex system consisting of multiple parts that are independent of each other. However, conventional 3DCP workflows usually lack automatic monitoring of print quality which can be easily affected for various reasons. This paper proposes an integrated workflow of automatic detection of filament deviation in a 3DCP process. The deformation of the filament is adopted as the criterion for print quality evaluation. A Deep Learning-morphology-based filament width estimation method is developed, and a filament deviation detection algorithm with presence of parametric uncertainties is proposed. This workflow allows to detect width deviations in the printed filament by considering several parameters of the printing system. The integrated workflow is implemented and tested through on-site printing tests.

The task space control of robot manipulators requires solving the thorny problem of stabilizing the compound system {outer controller - inner controller - robot manipulator}. To stabilize this compound system, both controllers must be designed by the user to achieve convergence of the tracking error. This problem is tricky to solve in the case of the control of an industrial robot manipulator because its internal controller is not accessible to users. In this work, we propose an adaptive neural network outer controller. The neural networks approximate the dynamics of the inner controller, the kinematic and dynamic parameters of the robot. Besides, the adaptive part finds parameters that achieve the stability of the global system. Since an adaptive approach is sensitive to errors in initial values, we have integrated into the controller a term that constrains the closed-loop system to maintain the prescribed performances. The effectiveness of the approach is demonstrated through Lyapunov's theory, simulation comparisons, and experimental studies.

Soft robotics is a swiftly growing research area these days. Modeling continuum robots accurately is still a demanding field. The paper aims to propose a shape reconstruction method and the estimation of the kinematic behavior of heterogeneous continuum robot in closed loop kinematic configuration, by using Pythagorean Hodograph (PH) curves. The validation of the model approach has been tested on cooperative continuum robots, namely Compact Bionic Handling Arms (CBHA), driving an intermediate flexible rope (a passive flexible link), by using a 3D tracking system. Experimental comparison of the proposed approach with the existing approaches is performed in terms of accuracy as well as the time cost.

This paper addresses the position control of continuum manipulators. Their performances in terms of speed limitation and position accuracy are often mediocre compared with rigid body based robots. In regards to continuum manipulators control, nonadaptive kinematic schemes were shown poor performance in terms of tracking position accuracy, and existing adaptive schemes were time-consuming. This paper presents a novel adaptive control scheme, namely the adaptive support vector regressor controller. The proposed approach exploits the optimization learning methods which yield global solutions of the training problem while keeping small size regressors. These characteristics make it possible to accelerate the convergence of the closed-loop system, thus reducing the execution time. The experimental results obtained using the compact bionic handling assistant robot demonstrate that nonadaptive kinematic architectures even in the presence of accurate learning models are not robust enough to deal with these challenging platforms and that adaptive control schemes can significantly improve the performance.

This paper presents a development of a new method dedicated to the modeling and control of Continuum Robots, based on the Finite Element Method (FEM) using quasi-static assumption. The modeling relies on a discretization of the continuum robots using 6 DoFs Frames along the structure of the robot that is compatible with the modeling of a sequence of rigid vertebras. When the robot's structure relies on rods with constant sections, internal forces are computed with beam elements, placed between two adjacent frames, that applies forces and torques. In the opposite, when the robot is composed of a complex shape deformable backbone separated by the rigid vertebras, a domain decomposition strategy is used to obtain an equivalent stiffness between two vertebras using volumetric FEM. In both cases, for solving the whole robot model and inverting it in real-time, the numerical method takes advantage of the serial nature of continuum robots, using a Block-Tri-Diagonal solver. The factor of improvement in the computation time reaches several order of magnitude compared to a classical FEM model, while keeping a good precision. The method has also been implemented and tested on a real pneumatic CBHA trunk designed by Festo Robotics and some complementarity examples have been generated numerically.