Model of Optimal Cooperative Reconnaissance and its Solution using Metaheuristic Methods

Stodola, Petr; Mazal, Jan

doi:10.14429/dsj.67.10530

Cited by 17 publications

(7 citation statements)

References 12 publications

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“…The algorithm is based on the simulated annealing principle, which is a generic probabilistic method. It has proven to be successful in similar position optimization problems; see the publications of the authors [47,48].…”

Section: Problem Formulation and Solutionmentioning

confidence: 99%

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

Stodola

Drozd

Mazal

et al. 2019

Sensors

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Using unmanned robotic systems in military operations such as reconnaissance or surveillance, as well as in many civil applications, is common practice. In this article, the problem of monitoring the specified area of interest by a fleet of unmanned aerial systems is examined. The monitoring is planned via the Cooperative Aerial Model, which deploys a number of waypoints in the area; these waypoints are visited successively by unmanned systems. The original model proposed in the past assumed that the area to be explored is perfectly flat. A new formulation of this model is introduced in this article so that the model can be used in a complex environment with uneven terrain and/or with many obstacles, which may occlude some parts of the area of interest. The optimization algorithm based on the simulated annealing principles is proposed for positioning of waypoints to cover as large an area as possible. A set of scenarios has been designed to verify and evaluate the proposed approach. The key experiments are aimed at finding the minimum number of waypoints needed to explore at least the minimum requested portion of the area. Furthermore, the results are compared to the algorithm based on the lawnmower pattern.

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Section: Problem Formulation and Solutionmentioning

confidence: 99%

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

Stodola

Drozd

Mazal

et al. 2019

Sensors

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“…For a solution to this problem, an adapted simulation annealing algorithm is used. This algorithm is a generic probabilistic method and has proven to be very successful when used by the authors in similar position optimization problems; see the works of [31,32].…”

Section: Trajectory Optimizationmentioning

confidence: 99%

Trajectory Optimization in a Cooperative Aerial Reconnaissance Model

Stodola

Drozd

Nohel

et al. 2019

Sensors

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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.

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“…Simulated Annealing (SA) is a global search method that find its inspiration in the physical annealing process studied in statistical mechanics 9 . Since SA is characterised by ease of implementation and fast convergence, it is usually employed as an optimisation method to find an optimal solution for military problem 10,11 . Basically, SA calculates the object function at a point, and then finds a new point with a higher value of object function with the probability.…”

Section: Simulated Annealing For the Optimal Depthmentioning

confidence: 99%

Optimal Operating Depth Search for Active Towed Array Sonar using Simulated Annealing

Lee

Seong

2019

Def. Sc. Jl.

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In an active towed array sonar, it is important to find the optimal operation depth. Generally, the optimal depth can be chosen via numerical simulations for all sonar depths and this imposes great burdens of time and cost.In this paper, an efficient approach is proposed to find the optimal depth using the optimisation technique. First, the sonar performance function is newly defined as a measure of how well the active sonar might perform. This function depends on the properties of the ocean environment and the positions of sonar and underwater target. Then, the simulated annealing to find an optimal solution for maximising sonar performance is used. The optimised depth agrees well with the depth obtained from direct searching for all depths of source and receiver combinations, but its computational time is largely reduced.

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Model of Optimal Cooperative Reconnaissance and its Solution using Metaheuristic Methods

Cited by 17 publications

References 12 publications

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

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

Trajectory Optimization in a Cooperative Aerial Reconnaissance Model

Optimal Operating Depth Search for Active Towed Array Sonar using Simulated Annealing

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