In continuation of the previous article, the following is prior research demonstrating onboard UAV control enhanced by feedback from cloud analytics, where the cloud not only processes vision tasks but strategically directs drone movement, tours, or repeated flight missions using buffered, selectively analyzed imagery.

1. DeepBrain: Cloud Offloading with Feedback

• In the “DeepBrain” project, drones stream aerial video to the cloud which runs deep learning vision models (e.g., for car or object detection). The cloud then provides real-time feedback to end-users and mission controllers about detected events or objects.

• Although initial computation is shifted offboard, decision outputs from the cloud are used to determine subsequent drone tasks (e.g., new search zones, retargeting, or repeated tours). The system architecture is designed to close the loop between cloud analytics and onboard navigation, so drones may adjust their flight plans or repeat coverage based on what was found or missed during prior data uploads.

2. Vision-Based Learning for Drones: Survey

• Recent surveys note that, beyond offloading vision processing, modern cloud-integrated UAV frameworks buffer images/video and use selective analysis for mission control.

• The cloud may process large batches selectively, then transmit instructions advising drones on where to revisit, what trajectory to update, or which areas require higher-resolution coverage. This approach reduces bandwidth and computation for each detection, relying on cloud-based aggregation and planning to cue onboard action; drones can repeat tours with maximized efficiency based on priority feedback.

3. Co-scheduling and Mission Assignment

• In mission-assignment studies, cloud analytics aggregate detections and schedule drone movements by issuing optimized flight paths and tour instructions, factoring in data from prior image uploads.

• The cloud essentially acts as a "central planner," buffering input from multiple drones and then sending tailored feedback on where individual drones must go next to fill gaps, observe changes, or minimize redundant coverage.

These works show the cloud not only offloads vision computing but actively shapes drone behavior using feedback from batch/buffered analytics. This closes the loop between data collection and physical navigation:

• Selective image processing triggers action, not just reporting.

• Dynamic, repeated tours (based on cloud synthesis) optimize resource use and target survey priorities.

#codingexercise: CodingExercise-09-25-2025.docx