Compliant mechanisms are gaining popularity in many different fields, such as in microelectromechanical systems (MEMS), medical applications and health care, opto-mechatronic technology, aerospace engineering, and semiconductor equipment. One of the areas for utilizing compliant mechanisms is building models of human hand counterparts. These models are often used as grasping and rehabilitation devices. Because of their properties, creating a human hand counterpart with compliant mechanisms is a much better choice compared with the models with traditional mechanisms; it looks more realistic, and its movements are much more natural compared with models with a traditional mechanism. A method of modeling and designing such a bio-inspired mechanism, as well as its experimental analysis with various forces applied, is presented in this paper. Two prototypes of the compliant fingers were obtained by 3D printing, and the calculation of the bending angle values was achieved by applying image processing to camera images of the compliant fingers’ prototypes. Image processing was conducted on images taken for both loaded and unloaded 3D-printed compliant finger prototype positions. Finally, these bending angle results are compared with the results obtained by Finite Element Method (FEM) analysis and experimental results acquired by a digital protractor.

Innovative lightweight smart structures incorporating piezoelectric material-based active elements, both as sensors and actuators, have been identified to present manifold advantages over traditional passive systems. Such structures have become intrinsically integrated into smart mechatronic systems, necessitating advanced design, testing, and control techniques. Real-time simulation of shell-type deformable objects, especially when employing the finite element method for non-linear analysis and control, has been challenging due to the extensive computational demand. Presented herein is an efficacious implementation leveraging machine learning with the isogeometric finite element formulation. This implementation focuses on shell-like smart mechatronic structures crafted from composite laminates comprising piezoelectric layers, which are characterised by electro-mechanical coupling. The foundation for the shell kinematics is derived from the Mindlin-Reissner assumptions, effectively incorporating transverse shear effects. While the inclusion of machine learning facilitates real-time efficient operations, the isogeometric finite element analysis (FEA) introduces pronounced advantages over conventional finite element method (FEM), also serving as a valuable source of offline data crucial for the training phases of machine learning algorithms. A piezo-laminated semicircular arch has been analysed to exemplify the effectiveness and performance of the presented methodology. Explorations into further machine learning techniques and intelligent control schemes are also contemplated.

This paper presents an edge-deployable vision-based framework for human–robot interaction using a xArm collaborative robot and a single RGB camera mounted on the robot wrist, and lightweight AI-based perception modules. The system enables intuitive, contact-free control by combining hand understanding and object detection within a unified perception–decision–control pipeline. Hand landmarks are extracted using MediaPipe Hands, from which continuous hand trajectories, static gestures, and dynamic gestures are derived. Task objects are detected using a YOLO-based model, and both hand and object observations are mapped into the robot workspace using ArUco-based planar calibration. To ensure stable robot motion, the hand control signal is smoothed using low-pass and Kalman filtering, while dynamic gestures such as waving are recognized using a lightweight LSTM classifier. The complete pipeline runs locally on edge hardware, specifically NVIDIA Jetson Orin Nano and Raspberry Pi 5 with a Hailo AI accelerator. Experimental evaluation includes trajectory stability, gesture recognition reliability, and runtime performance on both platforms. Results show that filtering significantly reduces hand-tracking jitter, gesture recognition provides stable command states for control, and both edge devices support real-time operation, with Jetson achieving consistently lower runtime than Raspberry Pi. The proposed system demonstrates the feasibility of low-cost edge AI solutions for responsive and practical human–robot interaction in collaborative industrial environments.