Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

The utilization of edge-enabled cloud computing in unmanned aerial vehicles has facilitated advances in autonomous control by employing computationally intensive algorithms frequently related to traversal among different locations in an environment. A significant problem remains in designing an effective strategy to offload tasks from the edge to the cloud. This work focuses on creating such a strategy by employing a network evaluation method built on the mean opinion score metrics in concoction with machine learning algorithms for path length prediction to assess computational complexity and classification models to perform an offloading decision on the data provided by both network metrics and solution depth prediction. The proposed system is applied to the A* path planning algorithm, and the presented results demonstrate up to 94% accuracy in offloading decisions.

Automated summary

This study explores the use of edge-enabled cloud computing in unmanned aerial vehicles for autonomous control, focusing on developing an effective strategy for task offloading. By utilizing a network evaluation method based on mean opinion score metrics and machine learning algorithms for path length prediction, the system evaluates computational complexity and makes offloading decisions based on network metrics and solution depth prediction. The proposed system, applied to the A* path planning algorithm, demonstrates up to 94% accuracy in offloading decisions.