Citation:

Vishnu Lakkamraju, 2025. "Edge AI for Real-Time Decision Making in Autonomous Systems", ESP Journal of Engineering & Technology Advancements  5(2): 130-140.

Abstract:

Edge Artificial Intelligence (Edge AI) marks a revolutionary development in the field of autonomous systems. Real-time responsiveness is even more important as autonomous systems run in dynamic and unpredictable surroundings. By integrating intelligence directly into edge devices, Edge AI solves the latency and bandwidth constraints of conventional cloud-based AI models by enabling instantaneous responses free from depending on far-off servers. In high-stakes fields as autonomous vehicles, industrial automation, unmanned aerial vehicles (UAVs), and remote medical diagnostics—where milliseconds define safety and efficiency—this is especially transforming. Edge AI systems accomplish fast situational awareness and adaptable behaviour by using lightweight deep learning models, on-device inferencing, and specialised hardware accelerators. Edge AI also limits sensitive data to the local device therefore improving data privacy and lowering the hazards connected with continuous data transmission. Important for operations in settings with low connectivity, such rural areas, battlefields, or disaster zones, it also enables offline capability. Improved Edge AI performance under hardware constraints has come from developments in federated learning and model compression methods. Edge AI's broad promise is still shown by developing application cases in smart cities, environmental monitoring, and precision agriculture. Still, integration difficulties abound, including the requirement for consistent protocols, effective model deployment systems, and strong security architectures. Transparency, justice, and responsibility will also become more vital ethical issues as artificial intelligence spreads out at the periphery. The architecture, uses, difficulties, and future course of Edge AI in autonomous systems are investigated in this work. It provides a thorough study of how localised intelligence is changing machine autonomy and supporting contextsally aware, safer, more secure, faster and more efficient systems. Edge artificial intelligence (Edge AI) marks a radical change in computational intelligence, bringing data processing closer to the source—at the edge of the network. This marks a major change in AI deployment strategy as Edge AI marks intelligence where it is most needed: at the edge. The important contribution Edge AI makes in allowing real-time decision making in autonomous systems like smart infrastructure, industrial robots, unmanned aerial vehicles (UAVs), and self-driving cars is examined in this work. Conventional cloud-based models can fall short of the latency, bandwidth, and dependability required of real-time autonomous operations. Edge AI provides, on the other hand, ultra-low latency, enhanced privacy, energy economy, and network failure resistance. This paper addresses fundamental architectures, important applications, technological developments, and the urgent issues in Edge AI deployment as well as suggests future paths to handle scalability, security, and standardising based on developing trends. This thorough analysis emphasises the critical part Edge artificial intelligence plays in the development of autonomous systems, therefore guiding a future in which machines run with more intelligence and autonomy.

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Keywords:

Edge AI, Autonomous Systems, Real-Time Decision Making, Iot, Latency, Machine Learning, Embedded Systems, Smart Infrastructure, Robotics.