In Infrastructure Engineering, control plane and data plane both serve different purposes and often engineers want to manage only those that are finite, bounded and are open to management and monitoring. If the number far exceeds those that can be managed, it is better to separate resources and data. For example, when there are several drones to be inventoried and managed for interaction with cloud services, it is not necessary to create a pseudo-resource representing each of the drones. Instead, a composite cloud resource set representing a management object can be created for the fleet and almost all of the drones can be kept as data in a corresponding database maintained by that object. Let us go deep into this example for controlling UAV swarm movement via cloud resources.

First an overview of the control methods is necessary.There are several such as leader-follower, virtual structure, behavior-based, consensus-based, and artificial potential field and advanced AI-based methods (like artificial neural networks and deep reinforcement learning).

There are advantages and limitations of each approach, showcasing how conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities.

There is a critical need for innovative solutions and interdisciplinary approaches combining conventional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications, so the infrastructure and solution accelerator stacks must enable switching from one AI model to another or even change direction from one control strategy to another.

Real-world applications for UAV swarms are not only present, they are the future. So this case study is justified by wide-ranging applications across fields such as military affairs, agriculture, search and rescue operations, environmental monitoring, and delivery services.

Now, for a little more detail on the control methods to select one that we can leverage for a cloud representation. These control methods include:

Leader-Follower Method: This method involves a designated leader UAV guiding the formation, with other UAVs following its path. It's simple and effective but can be limited by the leader's capabilities.

Virtual Structure Method: UAVs maintain relative positions to a virtual structure, which moves according to the desired formation. This method is flexible but requires precise control algorithms.

Behavior-Based Method: UAVs follow simple rules based on their interactions with neighboring UAVs, mimicking natural swarm behaviors. This method is robust but can be unpredictable in complex scenarios.

Consensus-Based Method: UAVs communicate and reach a consensus on their positions to form the desired shape. This method is reliable and scalable but can be slow in large swarms.

Artificial Potential Field Method: UAVs are guided by virtual forces that attract them to the desired formation and repel them from obstacles. This method is intuitive but can suffer from local minima issues.

Artificial Neural Networks (ANN): ANN-based methods use machine learning to adaptively control UAV formations. These methods are highly adaptable but require significant computational resources.

Deep Reinforcement Learning (DRL): DRL-based methods use advanced AI techniques to optimize UAV swarm control. These methods are highly sophisticated and can handle complex environments but are computationally intensive.

Out of these, the virtual structure method inherently leverages both the drones capabilities to find appropriate positions on the virtual structure as well as their ability to limit the movements to reach their final position and orientation.

Some specific examples and details include:

Example 1: Circular Formation

Scenario: UAVs need to form a circular pattern.

Method: A virtual structure in the shape of a circle is defined. Each UAV maintains a fixed distance from this virtual circle, effectively forming a circular formation around it.

Advantages: This method is simple and intuitive, making it easy to implement and control.

Example 2: Line Formation

Scenario: UAVs need to form a straight line.

Method: A virtual structure in the shape of a line is defined. Each UAV maintains a fixed distance from this virtual line, forming a straight line formation.

Advantages: This method is effective for tasks requiring linear arrangements, such as search and rescue operations.

Example 3: Complex Shapes

Scenario: UAVs need to form complex shapes like a star or polygon.

Method: A virtual structure in the desired complex shape is defined. Each UAV maintains a fixed distance from this virtual structure, forming the complex shape.

Advantages: This method allows for the creation of intricate formations, useful in tasks requiring precise positioning.

Example 4: Dynamic Formation Changes

Scenario: UAVs need to change formations dynamically during a mission.

Method: The virtual structure is updated in real-time according to the mission requirements, and UAVs adjust their positions accordingly.

Advantages: This method provides flexibility and adaptability, essential for dynamic and unpredictable environments.