Trajectory Optimization in a Cooperative Aerial Reconnaissance Model

Abstract

In recent years, the use of modern technology in military operations has become standard practice. Unmanned systems play an important role in operations such as reconnaissance and surveillance. This article examines a model for planning aerial reconnaissance using a fleet of mutually cooperating unmanned aerial vehicles to increase the effectiveness of the task. The model deploys a number of waypoints such that, when every waypoint is visited by any vehicle in the fleet, the area of interest is fully explored. The deployment of waypoints must meet the conditions arising from the technical parameters of the sensory systems used and tactical requirements of the task at hand. This paper proposes an improvement of the model by optimizing the number and position of waypoints deployed in the area of interest, the effect of which is to improve the trajectories of individual unmanned systems, and thus increase the efficiency of the operation. To achieve this optimization, a modified simulated annealing algorithm is proposed. The improvement of the model is verified by several experiments. Two sets of benchmark problems were designed: (a) benchmark problems for verifying the proposed algorithm for optimizing waypoints, and (b) benchmark problems based on typical reconnaissance scenarios in the real environment to prove the increased effectiveness of the reconnaissance operation. Moreover, an experiment in the SteelBeast simulation system was also conducted.

Cooperative Unmanned Aerial System Reconnaissance in a Complex Urban Environment and Uneven Terrain

“…In the first version, waypoints were deployed in the area of interest using a simple algorithm, which was fast but did not ensure the whole area to be covered completely. The improvement of the CAR model published in Reference [45] presents an algorithm, which reduces a number of waypoints and optimizes their locations so that the complete area of interest may be covered by at least one of the sensors. Another improvement [46] extended the model through smoothing the trajectories of unmanned systems in order to be able to use the model for Dubins vehicles that cannot change direction abruptly.…”

Cooperative Target Search of UAV Swarm with Communication Distance Constraint

“…To determine the area, grid [8,9], landmark [10,11], and potential field [12,13] methods are the main ones proposed. In [8], based on rasterizing the task area, real-time path planning was realized through an improved ant colony algorithm.…”

“…In [8], based on rasterizing the task area, real-time path planning was realized through an improved ant colony algorithm. In [11], the task area was divided by a Voronoi diagram, and waypoint allocation and track smoothing were used to realize the fast planning of a search track in a static environment. In [13], based on describing the task area using an artificial potential field, an improved logarithmic linear learning algorithm was proposed to reduce the risk that a UAV may wander into a zero-potential field area.…”

Collective Perception Using UAVs: Autonomous Aerial Reconnaissance in a Complex Urban Environment

“…, R K j j , R K j +1 j where K j ≥ 0 is the number of waypoints to be visited by UAV U j . Constraints in Equations ( 4)- (7) need to be satisfied: constraint in Equation (4) ensures that each UAV launches from its base and returns back at the end of the operation; and constraints in Equations ( 5)- (7) ensure that all the waypoints are visited just once: Figure 3a shows an example of the real situation from the top view. The polygon (blue line) encloses the area of interest; grey objects are obstacles (buildings in this case).…”

“…. , K p (7) Equation ( 8) defines the time, in which UAV U j performs its route R j . The term R k j − R k+1 j expresses the time needed to fly from waypoint/base R k j to the next waypoint/base R k+1 j on its route; it depends on the distance between both points and the flight parameters of UAV U j .…”