Emerging trends and regulations in UAV swarms

The units in a full-fledged, safe and autonomous swarm are comprised of drones and when the entire swarm is homogeneous, the adherence to Federal Aviation Administration (FAA)'s Small UAS Rule (Part 107) is sufficient to clear.  This regulation mandates the following from the drones:

• Drone Weight: Must weigh less than 55 pounds, including payload.
• Visual Line of Sight (VLOS): The drone must remain within the operator's unaided visual line of sight.
• Daylight Operations: Flights are allowed during daylight or twilight (with anti-collision lighting).
• Maximum Altitude: Cannot exceed 400 feet above ground level unless within 400 feet of a structure.
• Maximum Speed: Limited to 100 mph (87 knots).
• Airspace Restrictions: Operations in controlled airspace require prior FAA authorization.
• No Flying Over People: Unless they are directly involved in the operation.
• No Moving Vehicles: Cannot operate from a moving vehicle unless in a sparsely populated area.
• Weather Visibility: Minimum visibility of 3 miles from the control station.
• Pilot Certification: Operators must hold a Remote Pilot Certificate or be supervised by someone who does.
• Registration: All drones must be registered with the FAA.

For example, Amazon's drone delivery system, known as Prime Air, is designed to deliver packages weighing up to 5 pounds within 30 minutes. The drones are fully electric and incorporate advanced aerospace standards to ensure safety and reliability such as  Part 135 Air Carrier Certificate from the FAA as well as the FAA Part 107. The drones are equipped with a sophisticated sense-and-avoid system that enables them to detect and navigate around obstacles, both static (like chimneys) and dynamic (like other aircraft). This system uses proprietary algorithms for object detection and decision-making, ensuring safe operations even in unexpected situations. The algorithms leverage a diverse suite of object detection technologies to identify obstacles and adjust flight paths accordingly. During the delivery descent, the drones can detect and avoid smaller obstacles like trampolines or clotheslines that might not be visible in satellite imagery. An automated drone-management system is being developed to plan the flight paths and ensure there are safe distances between the aircraft and other aircraft in the area, and that all aviation regulations are complied with.

The autonomous drone delivery system features a deep learning autonomous drone model built using CNN-LSTM algorithms. It includes functionalities like online purchasing, drone delivery processing, and real-time location tracking. CNN-LSTM algorithms combine Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to handle tasks involving spatial and temporal data.CNNs are excellent for extracting spatial features from data, such as images or spectrograms. They use convolutional layers to identify patterns like edges, textures, or shapes. LSTMs are a type of Recurrent Neural Network (RNN) designed to capture temporal dependencies in sequential data. They excel at learning long-term relationships, making them ideal for tasks like time-series analysis or speech recognition.CNN layers process spatial data to extract features. These features are then passed to LSTM layers, which analyze the temporal relationships between them. This combination allows the model to understand both spatial and temporal aspects of the data, making it highly effective for tasks like video analysis, activity recognition, and speech emotion detection. This technique can help with generating textual descriptions of video sequences, identifying actions in a sequence of images and classifying emotions from audio spectrograms.

Amazon's CNN-LSTM predictor makes use of Gaussian and Edge detection preprocessing functions from image processing libraries for Steering Angle Dataset exploration.  Yolov3 bounding boxes architecture is used to find bounding boxes of cars, people, and trees in the image dataset. These bounding boxes were used by their probability model to calculate the probability of collision. Weight determination functions were used to calculate the probability of colliding into any given object. A pilot script is used to fly the drone.