Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications.

Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost‐effective human‐machine interface to enhance the immersion during the long‐term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG‐based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower‐limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real‐time robotic manipulation and virtual game for immersion‐enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower‐limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming‐aided training, showing its potential in IoT‐based smart healthcare applications.

Origami has been proved as a valuable tool to develop deployable, multifunctional, and tunable devices for diverse engineering applications. Inspired by the Kresling origami, this article presents a soft pneumatic actuator with vacuum control to realize the compound motion of twist and contraction. The twist direction varies as the crease angle changes and it offers pure contraction after combining two actuators with reverse creases. Our actuator can lift 180 folds of its weight and achieve the contraction ratio of 47%, while the response time is below 0.3 s. The origami-based actuator can realize an adjustable bistable property, leading to the possibility for external stimulus detection and mechanical memory devices, where the sensitivity can be regulated by the initial vacuum pressure. Through several actuators, we demonstrate the reconfigurable crawling robots capable of worm-like creep, alternating-step locomotion as well as obstacle detection. After activating all the actuators, the four-actuator robot with two working modes is validated to perform agilely for confined space navigation, showing our actuator has the potential in soft robotics applications.

Automatic identification of weld seam types by welding robot is a key link in intelligent welding as some adjustment scheme (e.g., welding trajectory planning, initial welding position, welding parameters) vary with the weld seam types. However, the variable welding environment and various weld seam profile omnifarious affect the robustness of weld seam types identification. To overcome the challenges derived from the weld seam diversity, in this paper, the silhouette-mapping was selected as the weld seam intermedium and a multi-type weld seam automatic identification system based on vision sensor was introduced. Two different laser sources were adopted to obtain robust silhouette-mapping features in proper gestures. Based on the silhouette-mapping data (stripe-mapping and spot-mapping), the related image processing algorithms were carried out to achieve automatic identification. Specifically, the bidirectional deviation search method was proposed to locate the spot-mapping area based on the stripe-mapping image. Aiming at the characteristics of the spot-mapping image, a carefully designed CNN (convolutional neural network) model was used to classify types. Experimental results prove that the silhouette-mapping and CNN are an effective combination for the multi-type weld seam identification, and a total of 97.6% of weld seam types were correctly predicted. Some weld-related studies include welding features extraction, and welding quality detection may improve its accuracy on the basis of determining weld seam types.