Real-time UAV Resource Monitoring and Alerts with Automated Control Mechanism.

Autonomous navigation in Unmanned Aerial Vehicles (UAVs) is a crucial and rapidly advancing field with a wide range of applications. It utilizes various deep learning (DL) algorithms that enable UAVs to operate and navigate autonomously, reducing the need for direct human intervention. However, the use of DL algorithms demands significant computational resources, which can lead to resource constraints, potentially disrupting ongoing operations and compromising UAV safety. This work focuses on establishing a real-time monitoring, alerting, and automated control framework for UAV system resources. The proposed framework alerts the user/operator if system resource utilization exceeds the threshold. Furthermore, the automated controlling mechanism guarantees that resource consumption is adequately monitored and kept below predefined boundaries, lowering the danger of resource depletion and UAV damage. The suggested framework provides a powerful and efficient solution for optimal utilization of the available UAV system resources. By tackling the critical issue of system resource consumption, the suggested framework helps to achieve the larger goal of enabling the widespread adoption of autonomous navigation,