Stroke is a serious medical condition that affects a large number of people worldwide. Patients who suffer from stroke often experience difficulty in movement, especially in their lower limbs. Rehabilitation is crucial to help stroke patients regain their mobility and quality of life. One promising solution is the use of exoskeleton robots that can assist patients in their recovery process. These robots are designed to mimic the human skeletal system and can help stroke patients regain their natural gait cycle. The proposed solution is an exoskeleton robot that can assist two joints, the hip and knee joints. However, controlling the exoskeleton robot is a challenging task. To address this issue, the feedback linearization control technique was implemented using MATLAB/Simulink. This control technique can ensure that the robot movements are smooth and precise, helping stroke patients regain their natural gait cycle. To validate our proposed control techniques, three different input reference trajectories were used. The simulation results showed the trajectory tracking performance of our controller, demonstrating the effectiveness of our approach. By successfully implementing the feedback linearization control technique, the exoskeleton robot can assist patients in regaining their mobility, leading to improved quality of life. In addition to the rehabilitation benefits, exoskeleton robots have the potential to assist patients in their daily activities, such as walking and running. This can significantly improve the patient’s independence and quality of life. The use of exoskeleton robots can also reduce the burden on healthcare providers by decreasing the need for physical assistance during rehabilitation.

Robotic assistive systems play an important role in enhancing quality of life of visually impaired individuals with greater independence. A significant amount of research has been conducted in assistive grasping systems in recent years, but certain challenges still exist including background noise during voice-based interaction, object occlusion, reliable and safe object handover human robot interaction (HRI), and multimodal interfaces. To address these challenges, this research presents an intelligent assistive robotic grasping framework that integrates the vision module, voice module and robot control module. The vision module includes the enhanced YOLOv11s-seg model that performs unified object segmentation with pose estimation and achieved mAP@50-95 of 0.933 for detection and 0.882 for segmentation with the least computational cost (32.8 GFLOPs) and fast inference time (4.2 ms/image) compared to YOLOv8s and YOLOv9c. The voice module includes a hybrid band-pass with spectral filter that efficiently removes the background noise (low, medium and higher background noise) to convert voice command into texts with accuracy of 93.5%. Additionally, an SVM-based machine learning text classification system is employed to enhance safety by rejecting voice commands unrelated to grasping or object picking, achieving 97.93% accuracy. Finally, the robot control module is introduced with object grasping and safe handover algorithms. The proposed intelligent assistive robotic grasping framework is validated through different experiments which demonstrate the overall grasp success rate of 94.7% across thirteen daily-life objects with minimal execution time of 42.67 s from the voice command to the system to handing over the grasp object to user by the robotic arm. The system ensures operational safety through workspace boundary constraints and built-in force sensing. The proposed approach provides a novel, efficient, and human-centric solution for voice-guided assistive robotic grasping for visually impaired users.

People affected by vision impairment experience significant challenges in mobility and daily life activities. In this paper, a smart assistive navigation system is proposed to address mobility challenges and to enhance the independence of visually impaired individuals. Three modules are integrated into the proposed system. The vision module detects obstacles and interactive objects such as doors, chairs, people, fire extinguishers, etc. The depth cam-based distance module provides the distance of detected objects and obstacles. The voice module provides auditory feedback to visually impaired individuals about the detected objects and obstacles that fall under the pre-defined threshold distance. Finally, the proposed system is optimized in terms of performance and user experience. Jetson Nano is used to reduce the cost of the overall system; however, it has compatibility issues with many of the latest object detection models. The YOLOv5n model is used considering compatibility for object detection, but it has low Mean Average Precision (mAP) and frame rate. To improve the performance of the vision module, various hyperparameters of YOLOv5n are fine-tuned along with transfer learning to enhance the mAP@50 from the original 0.457 to 0.845 and mAP@50-95 from 0.28 to 0.593. Tensor-RT optimization is employed to increase the frame rate to deploy the model in a real scenario. The real-time experimentation shows that the proposed system successfully alerts users to key objects, hazards, and obstacles which enables independent and confident navigation.

Nanoindentation, an advanced technique employed for characterizing materials, facilitates the precise determination of their hardness and Young's modulus by applying a specific, controlled force through an indenter, enabling highly localized deformation and measurement at nanometer scales. The nanoindentation gives us the view of the isotropic and anisotropic features of the materials by analyzing the zone beneath the indenter. The application of Bulk Metallic Glass (BMG) alloy, renowned for its unique combination of high strength, exceptional elasticity, and superior corrosion resistance, spans diverse industries including aerospace, biomedical, and consumer electronics. The study focuses on conducting nanoindentation analysis on the BMG alloy, aiming to characterize its deformation behavior. This involved utilizing Scanning Electron Microscopy (SEM) to discern deformation characteristics, followed by validation of the findings through simulations, ensuring robustness and reliability of the results. The modulus, determined to be 227GPa, provided insight into the material's structural rigidity, and the hardness 14.8GPa offered an indication of its resistance to localized plastic deformation. The results have been compared with the simulation results where the modulus was 242GPa and the hardness was 16.1GPa.

Rigid container walls invalidate the open-workspace assumptions of tabletop manipulation, leaving three coupled gaps: unconstrained trajectories can be wall-unsafe, single-point grasp checks reject accessible offset positions, and occlusion-removal actions can ignore wall proximity. This paper treats bin wall geometry as a first-class constraint. A multimodal framework couples YOLO11 instance segmentation with a radial-basis-function support vector machine (SVM) and geometric rules to select among five modes—direct grasping, height-based grasping, two-dimensional singulation, and partial and full occlusion removal based on target visibility, relative height, grasp feasibility and wall proximity. Every trajectory passes a common wall-constraint validator before reaching the controller. Experiments used a UR5e with an overhead RGB-D camera. YOLOv11m-Segmentation model reached 0.992 box and 0.991 mask mAP@0.5 with an average inference time of 0.4350 ± 0.11 s. Task success was 100.0%, 93.3%, and 86.66% at low, medium, and high clutter (30 trials per condition). At high clutter, the ablation increased TSR from 66.66% for the single-point wall-unaware baseline to 83.33% with multi-point grasp selection and 86.66% with wall-aware singulation. The proposed system achieved a 90% Task Success Rate (TSR), compared with Target-Oriented VPG, which achieved 60% TSR, and GraspNet with bin-wall constraints, which achieved 40% TSR with 10 trials in a high-clutter environment. Across the 90 proposed-system trials, six task failures were observed, but none involved wall contact. Because container awareness sits in the planning layer rather than the end-effector, the method retrofits to installed cells using standard two-finger hardware.

Lower limb exoskeletons assist individuals with mobility impairments by providing support and aiding rehabilitation. However, precise trajectory tracking remains a challenge due to variations in user movement and nonlinear gait dynamics. Traditional control methods, such as PID controllers, require continuous tuning and struggle with long-term adaptability. This study proposes a PID-based Iterative Learning Control (PID-ILC) approach for a 2-DOF lower limb exoskeleton, which refines control inputs over successive gait cycles to improve tracking accuracy. MATLAB simulations demonstrate that the PID-ILC strategy significantly reduces tracking errors over iterations, leading to smoother and more accurate joint movements. The results confirm that iterative learning enhances exoskeleton performance by improving motion precision and adaptability. This approach minimizes manual tuning efforts and provides a more effective solution for rehabilitation applications.