Movement Detection in Soft Robotic Gripper using Sinusoidally Embedded Fiber Optic Sensor

Multi-Degree-of-Freedom Force Sensor Incorporated into Soft Robotic Gripper for Improved Grasping Stability

In recent years, soft robotic grippers have emerged as a promising solution for versatile and safe manipulation of objects in various fields. However, precise force control is critical, especially when handling delicate or fragile objects, to avoid excessive grip force application or to prevent object slippage. Herein, we propose a novel three-degree-of-freedom force sensor incorporated within a soft robotic gripper to realize stable grasping with force feedback. The proposed optical sensor employs lightweight and compact optical fibers, thereby allowing for cost-effective fabrication, and a robust sensing system that is immune to electromagnetic fields. By innervating the soft gripper with optical fibers, a durable system is achieved with the fibers functioning as a strengthening layer, thereby eliminating the need for embedding an external stiffening structure for efficient bending actuation. The innovative contact-based light loss sensing mechanism allows for a robust and stable sensing mechanism with low drift (<0.1% over 9000 cycles) that can be applied to soft pneumatic bending grippers. We used the developed sensor-incorporated soft gripper to grasp various objects, including magnetic materials, and achieved slip detection along with grip force feedback without any signal interference. Overall, this study proposes a robust measuring multi-degree-of-freedom force sensor that can be incorporated into grippers for improved grasping stability.

Soft touchless sensors and touchless sensing for soft robots

Soft robots are characterized by their mechanical compliance, making them well-suited for various bio-inspired applications. However, the challenge of preserving their flexibility during deployment has necessitated using soft sensors which can enhance their mobility, energy efficiency, and spatial adaptability. Through emulating the structure, strategies, and working principles of human senses, soft robots can detect stimuli without direct contact with soft touchless sensors and tactile stimuli. This has resulted in noteworthy progress within the field of soft robotics. Nevertheless, soft, touchless sensors offer the advantage of non-invasive sensing and gripping without the drawbacks linked to physical contact. Consequently, the popularity of soft touchless sensors has grown in recent years, as they facilitate intuitive and safe interactions with humans, other robots, and the surrounding environment. This review explores the emerging confluence of touchless sensing and soft robotics, outlining a roadmap for deployable soft robots to achieve human-level dexterity.

Assistive robotic hand with bi-directional soft actuator for hand impaired patients

Soft wearable robotic hand can assist with hand function for the performance of activities of daily living (ADL). However, existing robotic hands lack a mathematical way to quantify the grip force generated for better controlling the grasp of objects during the performance of ADL. To address this issue, this article presents a soft wearable robotic hand with active control of finger flexion and extension through an elastomeric-based bi-directional soft actuator. This actuator bends and extends by pneumatic actuation at lower air pressure, and a flex sensor embedded inside the actuator measures the angles of the fingers in real-time. Analytical models are established to quantify the kinematic and tip force for gripping of the actuator in terms of the relationship between the input pressure and the bending angle, as well as the output force, and are validated experimentally and by the finite element method. Furthermore, the ability of the soft robotic hand to grasp objects is validated with and without being worn on a human hand. The robotic hand facilitates hand opening and closing by the wearer and successfully assists with grasping objects with sufficient force for ADL-related tasks, and the grip force provided by the actuator is further estimated by the analytical models on two healthy subjects. Results suggest the possibility of the soft robotic hand in providing controllable grip strength in rehabilitation and ADL assistance.

Soft-Sensor System for Grasp Type Recognition in Underactuated Hand Prostheses

This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips' contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types.

Speckle-based high-resolution multimodal soft sensing

Skin-like soft sensors are key components for human–machine interfaces; however, the simultaneous sensing of several types of stimuli remains challenging because large-scale sensor integration is required with numerous wire connections. We propose an optical high-resolution multimodal sensing approach, which does not require integrating multiple sensors. This approach is based on the combination of an optical scattering phenomenon, which can encode the information of various stimuli as a speckle pattern, and a decoding technique using deep learning. We demonstrate the simultaneous sensing of three different physical quantities—contact force, contact location, and temperature—with a single soft material. Another unique capability of the proposed approach is spatially continuous sensing with an ultrahigh resolution of few tens of micrometers, in contrast to previous multimodal sensing approaches. Furthermore, a haptic soft device is presented for a human–machine interface. Our approach encourages the development of high-performance smart skin-like sensors.

Adjustable Compliance Soft Sensor via an Elastically Inflatable Fluidic Dome

Soft sensors are essential for robotic systems to safely interact with humans and the environment. Although significant research has been carried out in the field of soft tactile sensing, most of these sensors are restricted to a predefined geometry and a fixed measurement range, hence limiting their application. This paper introduces a novel approach to soft sensing by proposing a soft load-sensing unit with an adjustable mechanical compliance achieved using an elastically inflatable fluidic dome. The sensor consists of a three-dimensional Hall-effect sensor, above which is a magnet whose movement is modulated by an intermediate elastomeric dome structure. Sensor configurations were designed and fabricated using three different silicone rubbers to cover ‘00–10’ and ‘20A’ durometer shore hardness scales. We demonstrated that the compliance of the sensor could be dynamically tuned by changing the internal pressure of the inflatable fluidic dome in all configurations. We performed finite element simulations to determine the reaction force of the sensor under load as well as the stresses within the internal structural behavior, which are not possible to capture experimentally. The proposed soft sensor has the potential to be readily adapted for use in various soft robotic applications of differing size, compliance range, and safety requirements.

Heterogeneous sensing in a multifunctional soft sensor for human-robot interfaces

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality.

Twining plant inspired pneumatic soft robotic spiral gripper with a fiber optic twisting sensor

The field of soft robotics has been significantly advanced with the recent developments of pneumatic techniques, soft materials, and high-precision motion control. While comprehensive motions can be achieved by sophisticated soft robots, multiple coordinated pneumatic controls are usually required to achieve even the simplest motions. Furthermore, most soft robotics are lacking the ability to sense the environment and provide feedback to the pneumatic control system. In this work, we design a twining plant inspired soft-robotic spiral gripper that requires only one single pneumatic control to perform the twining motion and to firmly hold onto a target object. The soft-robotic spiral gripper has an embedded high-birefringence fiber optic twisting sensor to provide critical information, including twining angle, presence of external perturbation, and physical parameter of the target object. Furthermore, finite element analyses (FEA) in parametric studies of the spiral gripper are performed for module design optimization. The unique single pneumatic channel design enables easy manipulation of the soft spiral gripper with a maximum of 540° twining angle and allows a firm grip of a target object as small as 1-mm in diameter. The embedded fiber optic sensor provides useful information of the target object as well as the twining angle of the soft robotic spiral gripper with high twining angle sensitivity of 0.03nm. The unique fiber-optic sensor embedded single-channel pneumatic spiral gripper that is made from non-toxic silicone rubber allows parallel and soft gripping of elongated objects located in a confined area, which is an essential building block for twining and twisting motions in soft robot.