## 1 Introduction
Moreover, image processing and computational prediction tools, distributed sensing and mobile robot technologies, and visual cognitive and environment mapping algorithms articulate Industry 5.0-driven sustainable technologies, operations, and tasks as regards digital twin-based product development. Cognitive and cloud robotics, machine learning and intelligent control algorithms, and digital twin simulation and predictive geospatial modeling tools shape the Industry 5.0 environment. Our research examines the internal obstacles that SMEs must overcome in order to incorporate big data-driven decision-making processes, computer vision algorithms, and imaging-based navigation technologies. The research analyzes the most relevant obstacles to SMEs implementing Industry 4.0, including a concise comparison of SME adoption of cyber-physical production networks, as well as a discussion of the possible benefits of overcoming SME implementation challenges. The EU's financial support for SMEs in relation to Industry 4.0 is reviewed alongside other potential sources of aid, as the study focuses primarily on European corporations [10]. Based on the present implementation of Industry 4.0, it may appear that only major companies have the resources necessary to configure cyber-physical system-based smart factories, since they can afford considerable financial and operational resources. Potential concerns associated with this topic include unfair competition and severe disadvantages for SMEs compared to large businesses [11]. The paper is divided into the sections listed below. First, this article explains the basic ideas necessary to comprehend Industry 4.0 and gives a summary of its principles in the literature review section. The subsequent section outlines the topic's essential sources and methodologies. Following the preceding section are the outcomes of the SME concentration on barriers and financial assistance. The research ends with a discussion. In conclusion, the most essential elements for a successful adoption of Industry 4.0 are enumerated, along with the greatest obstacles for SMEs. Key research contributions* Machine intelligence and autonomous robotic technologies in the corporate context of SMEs develop on distributed intelligence and real-time data simulation tools, visual perception and situational awareness algorithms, and cyber-physical production systems. * Industry 4.0-based manufacturing systems integrate sensor and actuator devices, cognitive data visualization and virtual simulation tools, and cloud and swarm robotics. * Image recognition and remote sensing technologies, signal processing and big data management tools, and virtual manufacturing systems are pivotal in smart robotic environments. * Sensor data fusion and remote intelligent object detection tools, cognitive decision-making algorithms, and digital twin modeling configure smart manufacturing execution and network robot systems. * Autonomous visual object detection and sensor data fusion tools, operational process simulation and robot motion control algorithms, and virtual machining systems shape smart manufacturing processes. * Deep learning and virtual simulation algorithms, data visualization functionalities, and digital twin technologies enable autonomous multi-robot systems in smart factories. * Object tracking and remote sensing algorithms, mobile autonomous robots, and remote intelligent image detection tools optimize smart process manufacturing and digital twin-based product development. * Smart connected objects, autonomous robotic and cyber-physical manufacturing systems, and virtual process simulation and machine data mining tools articulate Industry 4.0-based networked environments.