The ability to precisely perceive external physical interactions would enable robots to interact effectively with the environment and humans. While vision-based tactile sensing has improved robotic grippers, it is challenging to realize high resolution vision-based tactile sensing in robot arms due to presence of curved surfaces, difficulty in uniform illumination, and large distance of sensing area from the cameras. In this article, we propose a novel piezoluminescent skin that transduces external applied pressures into changes in light intensity on the other side for viewing by a camera for pressure estimation. By engineering elastomer layers with specific optical properties and integrating a flexible electroluminescent panel as a light source, we develop a compact tactile sensing layer that resolves the layout issues in curved surfaces. We achieved multipoint pressure estimation over an expansive area of 502 cm2with high spatial resolution, a two-point discrimination distance of 3 mm horizontally and 5 mm vertically which is comparable to that of human fingers as well as a high localization accuracy (RMSE of 1.92 mm). These promising attributes make this tactile sensing technique suitable for use in robot arms and other applications requiring high resolution tactile information over a large area.

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.

Robotic grasping in cluttered and real-world human environments is a challenging task. It requires unique kinematic capabilities to deal with spatial constraints as well as compliance and softness to offer collision safety and safe manipulation of sensitive objects. To address this challenge, we propose a novel robotic gripper with two steerable fingers whose lengths can be adjusted by way of a soft eversion mechanism. This enables the gripper to work in confined spaces while interacting safely with the environment. We also developed a new Electroadhesion (EA) pad design with a multilayer structure and a single insulating layer that can be safely integrated with the evertable fingers avoiding short-circuiting or dielectric breakdown to enhance the gripper payload. The resulting gripper can retrieve an object in a confined space and partially occluded by a barrier. It exhibits remarkable versatility in terms of object sizes, grasping objects with varying widths at least ranging from 70 mm to 600 mm. These results provide a promising avenue for new robotic applications in real-world environments.

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.

Despite their soft and compliant bodies, most of today’s soft robots have limitations when it comes to elongation or extension of their main structure. In contrast to this, a new type of soft robot called the eversion robot can grow longitudinally, exploiting the principle of eversion. Eversion robots can squeeze through narrow openings, giving the possibility to access places that are inaccessible by conventional robots. The main drawback of these types of robots is their limited bending capability due to the tendency to move along a straight line. In this paper, we propose a novel way to fuse bending actuation with the robot’s structure. We devise an eversion robot whose body forms both the central chamber that acts as the backbone as well as the actuators that cause bending and manoeuvre the manipulator. The proposed technique shows a significantly improved bending capability compared to externally attaching actuators to an eversion robot showing a 133% improvement in bending angle. Due to the increased manoeuvrability, the proposed solution is a step towards the employment of eversion robots in remote and difficult-to-access environments.

In the last decade, soft robots have been at the forefront of a robotic revolution. Due to the flexibility of the soft materials employed, soft robots are equipped with a capability to execute new tasks in new application areas -beyond what can be achieved using classical rigid-link robots. Despite these promising properties, many soft robots nowadays lack the capability to exert sufficient force to perform various real-life tasks. This has led to the development of stiffness-controllable inflatable robots instilled with the ability to modify their stiffness during motion. This new capability, however, poses an even greater challenge for robot control. In this paper, we propose a model-based kinematic control strategy to guide the tip of an inflatable robot arm in its environment. The bending of the robot is modelled using an Euler-Bernoulli beam theory which takes into account the variation of the robot's structural stiffness. The parameters of the model are estimated online using an observer based on the Extended Kalman Filter (EKF). The parameters' estimates are used to approximate the Jacobian matrix online and used to control the robot's tip considering also variations in the robot's stiffness. Simulation results and experiments using a fabric-based planar 3-degree-of-freedom (DOF) inflatable manipulators demonstrate the promising performance of the proposed control algorithm.

Thin and imperceptible soft skins that can detect internal deformations as well as external forces, can go a long way to address perception and control challenges in soft robots. However, decoupling proprioceptive and exteroceptive stimuli is a challenging task. In this paper, we present a silicone-based, capacitive E-skin for exteroception and proprioception (SCEEP). This soft and stretchable sensor can perceive stretch as along with touch at 100 different points via its 100 tactels. In this paper, we present a novel algorithm that decouples global strain from local indentations due to external forces. The soft skin is 10.1cm in length and 10cm in width and can be used to accurately measure the global strain of up to 25% with an error of under 3%; while at the same time, can determine the amplitude and position of local indentations. This is a step towards a fully soft electronic skin that can act as a proprioceptive sensor to measure internal states while measuring external forces.

This paper presents a novel approach for developing robotic grippers with variable stiffness hinges for dexterous grasps. This approach for the first time uses pneumatically actuated pouch actuators to fold and unfold morphable flaps of flexure hinges thus change stiffness of the hinge. By varying the air pressure in pouch actuators, the flexure hinge morphs into a beam with various open sections while the flaps bend, enabling stiffness variation of the flexure hinge. This design allows 3D printing of the flexure hinge using printable soft filaments. Utilizing the variable stiffness flexure hinges as the joints of robotic fingers, a light-weight and low-cost two-fingered tendon driven robotic gripper is developed. The stiffness variation caused due to the shape morphing of flexure hinges is studied by conducting static tests on fabricated hinges with different flap angles and on a flexure hinge with flaps that are bent by pouch actuators subjected to various pressures. Multiple grasp modes of the two-fingered gripper are demonstrated by grasping objects with various geometric shapes. The gripper is then integrated with a robot manipulator in a teleoperation setup for conducting a pick-and-place operation in a confined environment.