Smart antenna for emerging 5G and application

Fog Cloud Computing and the Internet of Things are transforming robotics by empowering AI-enabled autonomous systems. This study analyzes the benefits, drawbacks, and uses of this integration. AI-enabled autonomous robots can use edge computing and cloud resources for real-time data processing and decision-making, improving their performance and adaptability. Communication protocols, data management, security, and scalability are examined in the ecosystem. Case studies reveal how this confluence affects robotics applications. This research shows how FCC, IoT, and AI may improve robotic systems' efficiency, intelligence, and autonomy. The article covers AI-enabled autonomous systems in transportation, manufacturing, healthcare, agriculture, and smart cities. These technologies can improve productivity and safety in many fields, from self-driving automobiles to surgical robots. Integrating these technologies raises safety, ethical decision-making, data privacy, and security concerns. The report emphasizes transparent and ethical AI algorithms, unbiased decision-making, and regulatory frameworks to enable responsible integration and mitigate dangers. In the future, AI-enabled autonomous systems will be shaped by improved AI algorithms, multi-modal sensing, human-robot collaboration, and edge intelligence. It emphasizes the necessity of interdisciplinary collaboration and ethical considerations in responsible technology development. This study concludes with a detailed analysis of fog/cloud computing, IoT, and AI in robotics, revealing the immense promise and problems of AI-enabled autonomous systems. Responsible development and collaboration can help us negotiate this transformational frontier and create a safer, more efficient, and innovative society with AI-driven autonomous systems.

Section: Fog/cloud Computing and Iotmentioning

confidence: 99%

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Kiran Deep Singh,

Singh

2024

“…The features of fog computing are shown in Figure 1. Fog computing is appropriate for time-critical and bandwidth-sensitive applications because it distributes computing activities and decreases data transfer to distant cloud data centers [14].…”

Section: Fog Computingmentioning

confidence: 99%

Interdisciplinary Approaches: Fog/Cloud Computing and IoT for AI and Robotics Integration

Singh,

Singh

2024

Fog/Cloud Computing and the Internet of Things have created intriguing opportunities for AI and robotics integration. This study examines interdisciplinary approaches that combine FCC, IoT, AI, and Robotics to construct sophisticated autonomous systems. These integrated systems may efficiently and intelligently conduct complicated tasks by using edge devices and cloud resources. Communication protocols, data management, security, and interoperability are studied in this interdisciplinary environment. Real-world case studies demonstrate the practicality and benefits of this integration. This study shows how interdisciplinary approaches will change AI and robotics integration. In conclusion, the intersection of Fog/Cloud Computing, IoT, AI, and Robotics is influencing autonomous systems. Edge devices and the cloud enable robots to become intelligent, adaptable, and essential parts of many industries. This research encourages researchers, practitioners, and policymakers to collaborate on innovation and widespread adoption of disruptive technologies. Interdisciplinary techniques are essential to maximizing AI and robotics integration and launching a new era of intelligent automation

“…Edge AI algorithms can evaluate data locally and relay only essential insights to the cloud or other fog nodes. This reduces bandwidth utilization, network load, and data transmission costs [26].…”

Section: Edge Ai Data Filtering and Compressionmentioning

confidence: 99%

Fog-based Edge AI for Robotics: Cutting-edge Research and Future Directions

Singh,

Singh

2023

The fusion of Fog-based Edge Artificial Intelligence (AI) is an emerging and transformative research area within robotics. This research examines the significant potential of augmenting robotic systems by integrating Edge AI and Fog Computing, aiming to enhance their cognitive abilities, independence, and operational effectiveness. The feasibility of real-time data analysis and decision-making is enhanced by deploying AI algorithms at the network edge, near the robots, and by leveraging fog computing capabilities. This study investigates the diverse implementations of Fog-based artificial intelligence (AI) in robotics. These applications encompass autonomous navigation, object detection, and human-robot interaction. By showcasing these examples, the research demonstrates the potential for a transformative impact on the capabilities of robotic systems through the integration of Fog-based AI. Additionally, this study explores the obstacles and potential advantages within this interdisciplinary field, providing valuable perspectives on the promising avenues that can facilitate advancements in robotics by leveraging the combined power of Fog-based Edge Artificial Intelligence. This study elucidates how the amalgamation of Fog Computing and Edge AI confers enhanced capabilities upon intelligent robotic systems, enabling them to operate autonomously in real time. This integration effectively addresses the obstacles commonly encountered in conventional cloud-based AI systems, such as latency, internet connectivity, and data security concerns. The study highlights the importance of architecture, security, and ethical factors in utilizing robotic intelligence. It emphasizes the need for data protection standards and transparency to ensure responsible and reliable utilization of this technology in a rapidly changing environment.