Edge AI based on Real-time Robotic Decision-Making in Resource-constrained Environment.

Abstract

The development of Edge Artificial Intelligence (Edge AI) is a transformational breakthrough in the area of robotics, especially when it comes to situations where timely decision-making under limited computational and energy capacities is necessary. Legacy cloud based AI solutions, despite their strength, have been known to be latent, bandwidth-dependent and vulnerable to security, hence not suitable to time-sensitive robotic use in remote or dynamic sites. Edge AI is a technology that can facilitate overcoming these weaknesses by shifting computation and inference to embedded devices, or near edge servers, to allow robots to interpret sensory data, learn the world around them and autonomously make decisions at low latency. Essentially, it's what enables the robots to function effectively despite low connectivity or power even amid the presence of the more sophisticated AI algorithms such as progressive deep learning with minimal memory usage, reinforcement learning and optimizing adaptive algorithms and edge computing systems. Combining AI with edge computing does not only improve autonomy but also promotes scalability, resilience and privacy protection. This paper aims to discuss the design principles, enabling technologies and usages of Edge AI in real-time robotic decision-making under resource constrained environments. Through examining the latest developments and the system architecture along with real-world applications, the paper clarifies how Edge AI can make robotic systems faster, smarter, and more efficient autonomous agents that will be able to accomplish a complex task in real-world operational environments.