Edge Computing Enabled Robot Navigation System

Somewhere deep learning techniques have made robots more efficient by augmenting robots with sensor feeds for them to process the data and make decisions in much more advanced ways than they previously could have. This project is concerned with the Edge Computing-Enabled Robot Navigation System that enables guiding a robot with the help of edge servers’ capabilities to operate in dynamic environments where the usual approaches of robotics are not effective. There are often problems related to delays and inefficiency in most robotic navigation systems due to the fact that processing is centralized on the cloud. In our architecture, heavy processing is shifted to the edge server which is very close to the robot, thus minimizing the response time and allowing real-time processing even in extreme conditions. A and Dynamic A* search algorithms are adopted as the most appropriate for the efficient path-finding function of the system so that the robots to steer themselves through new environments. The use of active search strategy in path planning and its distribution over a few nodes makes it possible to render high quality path searching with minimum energy on the robot computing subsystem. The results of the performance evaluation of the developed system conducted in static and dynamic environments showed increased forward movement speed and reduced latencies during the process and better performance than the one depending on the cloud resources.