A Framework for Planning and Execution of Drone Swarm Missions in a Hostile Environment

Abstract

This article presents a framework for planning a drone swarm mission in a hostile environment. Elements of the planning framework are discussed in detail, including methods of planning routes for drone swarms using mixed integer linear programming (MILP) and methods of detecting potentially dangerous objects using EO/IR camera images and synthetic aperture radar (SAR). Methods of detecting objects in the field are used in the mission planning process to re-plan the swarm’s flight paths. The route planning model is discussed using the example of drone formations managed by one UAV that communicates through another UAV with the ground control station (GCS). This article presents practical examples of using algorithms for detecting dangerous objects for re-planning of swarm routes. A novelty in the work is the development of these algorithms in such a way that they can be implemented on mobile computers used by UAVs and integrated with MILP tasks. The methods of detection and classification of objects in real time by UAVs equipped with SAR and EO/IR are presented. Different sensors require different methods to detect objects. In the case of infrared or optoelectronic sensors, a convolutional neural network is used. For SAR images, a rule-based system is applied. The experimental results confirm that the stream of images can be analyzed in real-time.

Synchronous Control of a Group of Flying Robots Following a Leader UAV in an Unfamiliar Environment

“…Many works have indicated that solutions involving laser scanners, RGB-D sensors, or ultrasonic sensors mounted on the UAV board are fundamental and most effective. There are also solutions employing a synthetic aperture radar in addition to optical sensors [ 3 ].…”

Collaborative attack task assignment for distributed UAV swarm using competition cooperation mechanism

“…Because the location and size of obstacles are unknown before the UAV performs its mission, the obstacle avoidance problem are difficult to be considered when evaluating the distance advantage. It is considered that when the linear distance between the UAV and the task position is larger, the distance advantage is smaller [28], so the distance advantage function is constructed:…”

Energy Efficient UAV Flight Control Method in an Environment with Obstacles and Gusts of Wind

“…First, the MC may send a correction of the mission plan to the FCC when the MC's analyses show that, as a result of changes in weather conditions, the UAV is unable to perform the task within the time frame set. In such a case, MC modules set out a mission correction by solving VRPTW optimization tasks shown, for example, in [15] or [16]. The second type of integration is the impact on the current FCC control in order to run a certain behavior of the aerial vehicle.…”

“…Selected vertices i ∈ V V V and edges (i, j) ∈ E E E modeling the area of operations have defined parameters such as reconnaissance priority p i (analogically for the curve p ij ), payload working time in the vertex t i (analogically for the edge t ij ), and required time window for diagnosis (set in the form [e i , d i ], where e i -the earliest possible date of entry to the point modeled with the vertex i ∈ V V V (start of reconnaissance), d i -the latest date of entry to the point modeled with the vertex) (start of reconnaissance i ∈ V V V). A complete set of parameters and variable tasks is provided in [15,16]. The articles also underline the analogy with the tasks of VRPTW, which includes the tasks of determining UAV flight routes based on the MILP models presented.…”

“…From the perspective of mission planning, the following operational constraints indicated by [15], which belong to a group of limitations characteristic of grid flows, must be met.…”