One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries.

Ever since its discovery, the applications of Shape Memory Alloys (SMA) can be found across a range of application domains, from structural design to medical technology. This is based upon the unique and inherent characteristics such as thermal Shape Memory Effect (SME) and Superelasticity (or Pseudoelasticity). While thermal SME is used for shape morphing applications wherein temperature change can govern the shape and dimension of the SMA, Superelasticity allows the alloy to withstand a comparatively very high magnitude of loads without undergoing plastic deformation at higher temperatures. These unique properties in wearables have revolutionized the field, and from fabrics to exoskeletons, SMA has found its place in robotics and cobotics. This review article focuses on the most recent research work in the field of SMA-based smart wearables paired with robotic applications for human-robot interaction. The literature is categorized based on SMA property incorporated and on actuator or sensor-based concept. Further, use-cases or conceptual frameworks for SMA fiber in fabric for ‘Smart Jacket’ and SMA springs in the shoe soles for ‘Smart Shoes’ are proposed. The conceptual frameworks are built upon existing technologies; however, their utility in a smart factory concept is emphasized, and algorithms to achieve the same are proposed. The integration of the two concepts with the Industrial Internet of Things (IIoT) is discussed, specifically regarding minimizing hazards for the worker/user in Industry 5.0. The article aims to propel a discussion regarding the multi-faceted applications of SMAs in human-robot interaction and Industry 5.0. Furthermore, the challenges and the limitations of the smart alloy and the technological barriers restricting the growth of SMA applications in the field of smart wearables are observed and elaborated.

Path planning for autonomous mobile robots remains a critical challenge, particularly in environments with obstacles and constraints. Efficient navigation requires a robust algorithm capable of generating smooth, collision-free trajectories while ensuring computational efficiency. This study addresses the problem of indoor mobile robot navigation by leveraging harmonic potential fields, a solution derived from Laplace’s equation, to formulate an effective path-planning strategy. Conventional numerical methods for solving Laplace’s equation, such as Successive Over-Relaxation and Accelerated Over-Relaxation, often require extensive computational resources, especially in large-scale environments. To overcome this limitation, this research introduces an improved iterative approach, the Explicit Decoupled Group Modified Accelerated Over-Relaxation (EDGMAOR) method, which enhances computational efficiency and convergence speed. The EDGMAOR method incorporates a half-sweep block approach, significantly reducing the number of computations required per iteration while maintaining accuracy. To validate the effectiveness of the proposed method, simulations were conducted in a static, enclosed environment with various configurations of obstacles. Different starting and goal positions were tested to assess the efficiency, accuracy, and computational cost of the generated paths. The results indicate that the EDGMAOR method outperforms conventional approaches by achieving faster convergence rates and reduced computational time, demonstrating its suitability for real-time robot pathfinding applications. Furthermore, the study highlights that with greater obstacles proliferation, the EDGMAOR method maintains its efficiency, as obstacle regions are automatically excluded from unnecessary computations. This characteristic makes the method particularly useful for complex indoor environments where real-time processing is crucial. In conclusion, this research establishes EDGMAOR as a practical and effective solution for solving mobile robot path-planning problems, providing a balance between computational speed, accuracy, and robustness. The findings contribute to the ongoing advancements in autonomous robotics and artificial intelligence-driven navigation systems, with potential applications in industrial automation, smart transportation, and defence sectors.