At present, the computer is in the stage of rapid growth, and designers can make full use of high technology to visualize their inspiration.Due to the high growth of embedded system and the emergence of intelligent theory and microprocessor, robot technology has gained greater development opportunities.This text mainly studies the application of embedded system in robot vision, constructs a robot fuzzy control system combined with deep learning (DL), and applies it to robot-based collaborative interactive artistic design to realize intelligent innovation of collaborative interactive artistic design based on intelligent robot aided decision.The results of robot simulation show that the proposed collaborative interactive artistic design method for robots is flexible in design, fast and accurate in modeling and strong in real-time simulation, which provides reliable guidance for the innovation of artistic design mode based on artificial intelligence (AI).On this basis, the extended application of AI in artistic design is discussed.Compared with the traditional artistic creation, it has high working speed, completes the creation quickly, and can also carry out batch production with the help of molds.
At present, the computer is in the stage of rapid growth, and designers can make full use of high technology to visualize their inspiration.Due to the high growth of embedded system and the emergence of intelligent theory and microprocessor, robot technology has gained greater development opportunities.This text mainly studies the application of embedded system in robot vision, constructs a robot fuzzy control system combined with deep learning (DL), and applies it to robot-based collaborative interactive artistic design to realize intelligent innovation of collaborative interactive artistic design based on intelligent robot aided decision.The results of robot simulation show that the proposed collaborative interactive artistic design method for robots is flexible in design, fast and accurate in modeling and strong in real-time simulation, which provides reliable guidance for the innovation of artistic design mode based on artificial intelligence (AI).On this basis, the extended application of AI in artistic design is discussed.Compared with the traditional artistic creation, it has high working speed, completes the creation quickly, and can also carry out batch production with the help of molds.
Computational methods that surpass the capabilities of traditional embedded intelligence are required due to the quick development of intelligent electronic devices. In order to improve system performance and decision-making efficiency, this study describes the development and execution of a smart electronic system that combines quantum computing and embedded artificial intelligence. The suggested framework uses quantum computer techniques for optimization and difficult problem solving, while integrating resource-constrained embedded devices with predictive models deployed at the edge. A hybrid traditional and quantum architecture is shown, in which quantum-assisted computation facilitates data processing and model optimisation while embedded AI conducts real-time inference. An embedded processor platform is used to implement the system, and representative smart electronics applications are used to assess it. When compared to conventional embedded AI techniques, experimental results show increased scalability, decreased computational delay, and higher accuracy. The suggested concept offers a scalable route to next-generation intelligent electronics and demonstrates the viability of combining quantum-assisted cognition with embedded electronic systems. For upcoming embedded systems that need high-performance intelligence with limited resources, this study provides a useful paradigm.
Utilizing the capabilities of both human-centred artificial intelligence and quantum computing, quantum artificial intelligence transforms how firms operate in Industry 5.0. This enables you to perform complex simulations, optimize fast, and make smarter decisions for robust, customized, and long-lasting systems. This is particularly evident in fields where quantum computing can manage massive information and complex patterns that traditional computer cannot, such as drug development, logistics, and new materials. It assists Industry 5.0 in achieving its objectives, which go beyond automation, which was the main focus of Industry 4.0. It does this via fostering sustainability, developing stress-tolerant systems, and facilitating human-machine collaboration (Augmented Intelligence). The following are some of Industry 5.0's most significant effects: optimization Logistics, supply chain optimization, and industrial optimization may all be greatly accelerated with the use of quantum machine learning (QML). Over time, this improves productivity and reduces waste. More precise predictive maintenance and complex analysis are made possible by quantum artificial intelligence, freeing up workers to focus on more worthwhile and innovative projects. This facilitates genuine collaboration between humans and AI. Researchers can more easily concentrate on potential compounds when they model the interactions between molecules. This facilitates the development of novel medications and antibiotics. Drug development and healthcare are two industries that employ this technology. Complex quantum simulations are used in advanced materials and simulation to expedite the discovery and development of novel materials. Real-time threat detection and quantum-resistant encryption can improve the security of factory networks. By using a lot of data to determine what each individual needs, personalization enables you to create experiences and products that are quite distinct for each person. Quantum algorithms allow you to do more in less time. Additionally, quantum-inspired techniques can improve the performance of standard algorithms to provide superior outcomes. Large, extremely complicated datasets can be processed by quantum computers. This enables them to identify subtle patterns that conventional AI is unable to detect and train AI models far more quickly. Keeping up with the latest algorithms and hardware limitations is never easy. It is crucial to consider ethical concerns and ensure that technology is accessible to all people worldwide. By fusing human creativity with machine intelligence, the integration might result in the creation of more intelligent and independent systems. This could contribute to a more stable future.
In the case of multiple intelligent robots working together, such as the robot soccer game, each robot is required to complete the common task with efficient, safe and fast cooperative communication. Compared with the traditional wireless communication, intelligent robots have the advantages of information security, large capacity, high efficiency and high speed if they interact with each other through quantum communication. In this paper, quantum communication and intelligent robot are combined, and a kind of quantum self-organizing network for robot soccer is proposed. In this network, each robot is both the sender and receiver of quantum information, and all players are communication nodes in the quantum self-organizing network. With the support of certain routing protocols, the network can collect, judge and make decisions on soccer position information, player movement information and soccer goal information. The simulation results show that compared with the traditional wireless method, this scheme not only has higher information security, but also ensures the effectiveness of information transmission. Therefore, this study provides a new idea for the design and development of communication network between soccer robots in the future.
Over the last 65 years, Artificial Intelligence has successfully transformed robots from simple machines into intelligent autonomous agents. However, as robotics enters high-stakes fields like "Quick Commerce" and complex engineering, classical computing relying on binary bits is hitting a massive "computational ceiling." These traditional systems are simply too slow to process the colossal, high-dimensional data required for instant decision-making. This paper reviews the evolution of AI and identifies the specific hardware bottlenecks limiting modern autonomy. To bridge this gap, we propose "Quantum Power" leveraging qubits, superposition, and entanglement as the essential engine for the next generation of robotics. By synthesizing the interplay between AI and Quantum Computing, this research demonstrates how a hybrid approach can overcome current logistical barriers, moving us toward a future of faster, smarter, and truly symbiotic quantum-powered robots. Despite its promising advantages, the implementation of quantum-AI integration faces several challenges, including limited availability of quantum hardware, noise sensitivity, and the need for hybrid quantum-classical frameworks. The study highlights ongoing research efforts and future directions aimed at overcoming these limitations. Overall, the fusion of quantum computing and AI offers a novel and transformative approach to developing intelligent robotic systems with superior learning and decision-making capabilities.
The convergence of quantum computing and artificial intelligence (AI) is redefining the boundaries of computational engineering, offering an unprecedented capacity for optimization, simulation, and design automation. This research introduces Quantum-Driven Computer-Aided Engineering (Q-CAE), a hybrid AI–quantum framework developed to accelerate design space exploration and enhance the precision of optimization in complex engineering systems. Q-CAE combines the analytical rigor of quantum algorithms with the adaptability of AI-driven optimization models to address the challenges of high-dimensional, nonlinear, and interdependent design parameters that traditionally strain classical computing resources. Within this framework, quantum subroutines such as Quantum Approximate Optimization Algorithms (QAOA) and Variational Quantum Circuits (VQC) are employed to efficiently search large solution spaces, while AI models leverage reinforcement learning and neural networks to predict optimal configurations and refine performance iteratively. This hybrid approach enables dynamic learning from multi-objective data sets, optimizing energy efficiency, material properties, and system reliability. Participants engaged with Q-CAE applications gained first-hand experience in algorithmic modeling, quantum heuristics, and AI-driven engineering design, cultivating interdisciplinary skills essential for future computational innovation. Results indicate that hybrid Q-CAE systems can significantly reduce computational time and enhance accuracy compared to traditional CAE workflows. The study demonstrates how integrating quantum principles into engineering design pipelines can transform modern computational methodologies, setting the foundation for autonomous, intelligent, and quantum-enhanced engineering systems.
Over the last 65 years, Artificial Intelligence has successfully transformed robots from simple machines into intelligent autonomous agents. However, as robotics enters high-stakes fields like "Quick Commerce" and complex engineering, classical computing relying on binary bits is hitting a massive "computational ceiling." These traditional systems are simply too slow to process the colossal, high-dimensional data required for instant decision-making. This paper reviews the evolution of AI and identifies the specific hardware bottlenecks limiting modern autonomy. To bridge this gap, we propose "Quantum Power" leveraging qubits, superposition, and entanglement as the essential engine for the next generation of robotics. By synthesizing the interplay between AI and Quantum Computing, this research demonstrates how a hybrid approach can overcome current logistical barriers, moving us toward a future of faster, smarter, and truly symbiotic quantum-powered robots. Despite its promising advantages, the implementation of quantum-AI integration faces several challenges, including limited availability of quantum hardware, noise sensitivity, and the need for hybrid quantum-classical frameworks. The study highlights ongoing research efforts and future directions aimed at overcoming these limitations. Overall, the fusion of quantum computing and AI offers a novel and transformative approach to developing intelligent robotic systems with superior learning and decision-making capabilities.