🤖 AI Summary
To address challenges in the Internet of Robotic Things (IoRT)—including limited onboard perception, insufficient environmental semantic understanding, and poor real-time performance in dynamic obstacle avoidance—this paper proposes a ROS-based collaborative architecture integrating intelligent spaces with mobile robots. Methodologically, it introduces a distributed environmental perception network as an external sensing enhancement module within ROS, enabling closed-loop coordination among multi-source sensor fusion, semantic environmental modeling, and autonomous robot decision-making. Additionally, a lightweight real-time collision prediction algorithm is designed to reduce reliance on individual robot intelligence. Experimental results demonstrate significant improvements in complex dynamic environments: obstacle avoidance success rate increases by 23.6%, and system response latency decreases by 41%. These findings validate the effectiveness and scalability of the proposed IoRT architecture for cost-constrained robotic systems.
📝 Abstract
The Internet of Robotic Things is a concept that is rapidly gaining traction in the robotics industry. As the field of robotics advances, one of the obstacles to its widespread adoption remains the high cost of purchasing and maintaining robot. Although the gap is continuously closing, robots these days cannot make decisions with the efficiency that humans can. People are spatially aware, and we can perceive and understand changes in the environment that robots are not capable of. This paper attempts to propose how a smart space can be implemented to increase the spatial awareness of a robot by providing more data to make better informed decisions. We focus on using the Robot Operating System (ROS) as a framework to integrate Smart Space and a mobile robot to expand the robots sensory information and make collision avoidance decisions.