Applications of a vacuum-actuated multi-material hybrid soft gripper: lessons learnt from RoboSoft manipulation challenge.

Soft grippers are garnering increasing attention for their adeptness in conforming to diverse objects, particularly delicate items, without warranting precise force control. This attribute proves especially beneficial in unstructured environments and dynamic tasks such as food handling. Human hands, owing to their elevated dexterity and precise motor control, exhibit the ability to delicately manipulate complex food items, such as small or fragile objects, by dynamically adjusting their grasping configurations. Furthermore, with their rich sensory receptors and hand-eye coordination that provide valuable information involving the texture and form factor, real-time adjustments to avoid damage or spill during food handling appear seamless. Despite numerous endeavors to replicate these capabilities through robotic solutions involving soft grippers, matching human performance remains a formidable engineering challenge. Robotic competitions serve as an invaluable platform for pushing the boundaries of manipulation capabilities, simultaneously offering insights into the adoption of these solutions across diverse domains, including food handling. Serving as a proxy for the future transition of robotic solutions from the laboratory to the market, these competitions simulate real-world challenges. Since 2021, our research group has actively participated in RoboSoft competitions, securing victories in the Manipulation track in 2022 and 2023. Our success was propelled by the utilization of a modified iteration of our Retractable Nails Soft Gripper (RNSG), tailored to meet the specific requirements of each task. The integration of sensors and collaborative manipulators further enhanced the gripper's performance, facilitating the seamless execution of complex grasping tasks associated with food handling. This article encapsulates the experiential insights gained during the application of our highly versatile soft gripper in these competition environments.

Design and Performance Evaluation of a Fully Integrated Knitted Knee Brace for Knee Motion Sensing.

The use of e-textiles in wearable sensor design has recently received much interest in many applications, such as robotics, rehabilitation, personal wellness, and sports. Particularly in the rehabilitation domain, it has provided a potential alternative tool for telerehabilitation. In this paper, we designed and evaluated a knitted knee brace with interconnects, resistors, and sensors for real-time kinematic data acquisition. The real-time data acquisition is transmitted using a printed circuit board (PCB) connected to the knee brace through snap pins. The knitted knee brace was tested on three male and one female participant , aged between 30 and 50 years old. All participants were instructed to perform a walking activity at 1.5 km/h for a duration of 10 seconds on the Advanced Mechanical Technology, Inc (AMTI) treadmill over two sessions. The results demonstrated that the fully integrated knitted wearable knee brace could monitor and track human joint locomotion in real time with a standard deviation of 0.39V and 0.41V , respectively, for these two sessions. However, double peak signals were noticeable from the knitted knee brace at a mean of 80.54% during the gait cycles across the four subjects; this observation could be due to the coupled motion along the transverse and coronal planes during the activity.

All Knitted and Integrated Soft Wearable of High Stretchability and Sensitivity for Continuous Monitoring of Human Joint Motion.

E-textiles have recently gained significant traction in the development of soft wearables for healthcare applications. However, there have been limited works on wearable e-textiles with embedded stretchable circuits. Here, stretchable conductive knits with tuneable macroscopic electrical and mechanical properties are developed by varying the yarn combination and the arrangement of stitch types at the meso-scale. Highly extensible piezoresistive strain sensors are designed (>120% strain) with high sensitivity (gauge factor 8.47) and durability (>100,000 cycles), interconnects (>140% strain) and resistors (>250% strain), optimally arranged to form a highly stretchable sensing circuit. The wearable is knitted with a computer numerical control (CNC) knitting machine that offers a cost effective and scalable fabrication method with minimal post-processing. The real-time data from the wearable is transmitted wirelessly using a custom designed circuit board. In this work, an all knitted and fully integrated soft wearable is demonstrated for wireless and continuous real-time sensing of knee joint motion of multiple subjects performing various activities of daily living.

Gecko-Inspired Dry Adhesive Based on Micro-Nanoscale Hierarchical Arrays for Application in Climbing Devices.

The unusual ability of geckos to climb vertical walls underlies a unique combination of a hierarchical structural design and a stiffer material composition. While a dense array of microscopic hierarchical structures enables the gecko toe pads to adhere to various surfaces, a stiffer material (ß-keratin) composition enables them to maintain reliable adhesion over innumerable cycles. This unique strategy has been seldom implemented in engineered dry adhesives because fabrication of high-aspect-ratio hierarchical structures using a stiffer polymer is challenging. Herein, we report the fabrication of high-aspect-ratio hierarchical arrays on flexible polycarbonate sheets (stiffness comparable to that of ß-keratin) by a sacrificial-layer-mediated nanoimprinting technique. Dry-adhesive films comprising the hierarchical arrays showed a formidable shear adhesion of 11.91 ± 0.43 N/cm2. Cyclic adhesion tests also showed that the shear adhesion of the adhesive films reduced by only about 20% after 50 cycles and remained nearly constant until about 200 cycles. Most importantly, the high-aspect-ratio hierarchical arrays were integrated onto the feet of a miniature robot and the locomotion on a 30° inclined surface was demonstrated.

A Compact Magnetic Field-Based Obstacle Detection and Avoidance System for Miniature Spherical Robots.

Due to their efficient locomotion and natural tolerance to hazardous environments, spherical robots have wide applications in security surveillance, exploration of unknown territory and emergency response. Numerous studies have been conducted on the driving mechanism, motion planning and trajectory tracking methods of spherical robots, yet very limited studies have been conducted regarding the obstacle avoidance capability of spherical robots. Most of the existing spherical robots rely on the "hit and run" technique, which has been argued to be a reasonable strategy because spherical robots have an inherent ability to recover from collisions. Without protruding components, they will not become stuck and can simply roll back after running into bstacles. However, for small scale spherical robots that contain sensitive surveillance sensors and cannot afford to utilize heavy protective shells, the absence of obstacle avoidance solutions would leave the robot at the mercy of potentially dangerous obstacles. In this paper, a compact magnetic field-based obstacle detection and avoidance system has been developed for miniature spherical robots. It utilizes a passive magnetic field so that the system is both compact and power efficient. The proposed system can detect not only the presence, but also the approaching direction of a ferromagnetic obstacle, therefore, an intelligent avoidance behavior can be generated by adapting the trajectory tracking method with the detection information. Design optimization is conducted to enhance the obstacle detection performance and detailed avoidance strategies are devised. Experimental results are also presented for validation purposes.

A study on estimation of planar gait kinematics using minimal inertial measurement units and inverse kinematics.

This paper describes a preliminary study of using four inertial measurement units (IMUs) attached to the heel and pelvis to estimate the joint angles of normal subjects during walking. The IMU, consisting of a 3-D accelerometer and gyroscope, is used to estimate the planar displacement of the heel and pelvis and the angular change of heel in one gait cycle. We then model the gait as a planar 3R serial chain and solve its inverse kinematics by using such information. The results are validated by comparing the estimated joint angles of lower limbs (i.e. hip, knee and ankle angles) with an optical motion capture system. This study can benefit the future research on conducting complete lower limbs kinematics analysis with minimal and unobtrusive wearable sensors.