Automation of abrasive machining operations (AMO) has become a challenging aspect in the remanufacturing industry where it is required to conduct operations on a surface of which the exact dimensions are unknown. In such cases, skilled human workers have to step in to perform labor-intensive tasks with inconsistent quality. In existing research work, collaborative robots are used to partially automate such operations under human supervision. However, these methods do not perform learning and control simultaneously and are often affected by the interactions of the human operator. In this paper, a novel learning and control scheme is proposed where the robot explores an unknown surface iteratively while achieving the desired contact control performance under supervision and occasional interference from the human operator. The unknown surface is divided into sub-regions, and the learning and control parameters are updated each time the robot visits each sub-region. This method is independent of the path of the robot and thus is unaffected by the irregularities introduced by a human operator's interactions. The proposed method is applied to force control, stiffness learning, and orientation adaptation cases. The validity of this method is shown via simulations as well as experiments conducted using a Kinova Gen3 7-DOF robot.

Contact tooling operations like sanding and polishing have been high in demand for robotics and automation, as manual operations are labour-intensive with inconsistent quality. However, automating these operations remains a challenge since they are highly dependent on prior knowledge about the geometry of the workpiece. While several methods have been developed in existing research to automate the geometry learning process and adjust the contact force, human supervision is heavily required in the calibration of workpieces and the path planning of robot motion in such methods. Furthermore, the stiffness identification of the workpiece is not considered in most of these methods. This paper presents a human-robot collaboration (HRC) framework, which is able to perform surface exploration on an unknown object combining the operator's flexibility with the control precision of the robot. The operator moves the robot along the surface of the target object, and the robot recognizes the surface geometry and surface stiffness while exerting a desired contact force through control. For this purpose, a mesh iterative learning control (MILC) is developed to learn the surface stiffness, plan the exploration path, and adjust contact force through repetitive online correction based on HRC. The proof of learning convergence and the results of the simulation and experiments performed using a 7-DOF Sawyer robot demonstrate the validity of the proposed controller.

Soft robots are known for their ability to comply and having superior extensibility. However, one of the limitations of most of these robots is that they can stand only a limited amount of load before buckling, and they feature a non-negligible initial height. Hybrid soft-rigid actuators seem to offer a trade-off between compliance and the amount of load they can withstand, but only a few simple models have been proposed to describe the behavior of these actuators. In this paper, we propose a design, model and experimental validation of a soft actuator based on stackable Hyperelastic Ballooning Membranes (HBMA). This actuator shows an extensibility higher than 179%, as well as an ability to stand more than 20 times its own weight at a pressure as low as 35 kPa. Two models, giving the dynamic behavior of the HBMA in terms of displacement and pressure, have been derived from different hyperelastic models (Neo-Hookean and Mooney-Rivlin) and compared in terms of accuracy and robustness. Finally, an example of a hybrid soft-rigid continuum ballooning robot built with HBMAs is presented and characterized experimentally.

Robotic phantoms enable advanced physical examination training before using human patients. In this article, we present an abdominal phantom for palpation training with controllable stiffness liver nodules that can also sense palpation forces. The coupled sensing and actuation approach is achieved by pneumatic control of positive-granular jammed nodules for tunable stiffness. Soft sensing is done using the variation of internal pressure of the nodules under external forces. This article makes original contributions to extend the linear region of the neo-Hookean characteristic of the mechanical behavior of the nodules by 140% compared to no-jamming conditions and to propose a method using the organ level controllable nodules as sensors to estimate palpation position and force with a root-mean-square error of 4% and 6.5%, respectively. Compared to conventional soft sensors, the method allows the phantom to sense with no interference to the simulated physiological conditions when providing quantified feedback to trainees, and to enable training following current bare-hand examination protocols without the need to wear data gloves to collect data.

Robust mechanisms for slip resistance are an open challenge in legged locomotion. Animals such as goats show impressive ability to resist slippage on cliffs. It is not fully known what attributes in their body determine this ability. Studying the slip resistance dynamics of the goat may offer insight toward the biologically inspired design of robotic hooves. This article tests how the embodiment of the hoof contributes to solving the problem of slip resistance. We ran numerical simulations and experiments using a passive robotic goat hoof for different compliance levels of its three joints. We established that compliant yaw and pitch and stiff roll can increase the energy required to slide the hoof by ≈ 20% compared to the baseline (stiff hoof). Compliant roll and pitch allow the robotic hoof to adapt to the irregularities of the terrain. This produces an antilock braking system-like behavior of the robotic hoof for slip resistance. Therefore, the pastern and coffin joints have a substantial effect on the slip resistance of the robotic hoof, while the fetlock joint has the lowest contribution. These shed insights into how robotic hooves can be used to autonomously improve slip resistance.

Epidural anaesthesia is a medical gesture commonly performed by an anaesthesiologist. However, it remains one of the most difficult gestures to master for medical students. Given the lack of sufficiently realistic training devices available for future physicians, we propose a new haptic simulator which reproduces the haptic sensations felt by anaesthesiologists when performing this kind of operation. The originality of this simulator is the coupling of a Geomagic Touch®device with a pneumatic cylinder to reproduce the “Loss of Resistance” phenomenon which helps the physician to control the needle depth. In this paper, we introduce the parametric 3D model of the region of interest and the control laws used jointly. Even though this device could not reproduce the right level of forces required in this type of anaesthesia, an anaesthesiologist involved in the project gave positive feedback about its haptic tissue rendering.

This paper presents the BirthSIM model, a haptic interface of a childbirth simulator. The aim of this haptic interface is to improve haptic rendering and increase the biofidelity of the movements and forces which occur during the fetal descent. Thus the model of this 2 degrees of freedom pneumatic actuated interface is presented in this article, in order to synthesize a control law based on the backstepping method. The control law obtained allows targeted trajectories of positions and pneumatic stiffness to be tracked but also allows the equivalent stiffness of the end effector in Cartesian coordinates to be adapted in real time. The difference between pneumatic stiffness and closed loop stiffness is explained. Finally, some simulation results are presented to validate the control law behavior.