An artificial intelligence based novel approach for real-time allocation of armament to hostile targets

Shahzad, Atif; Ur-Rehman, Raza

doi:10.1109/ibcast.2013.6512147

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…The revenue function can represent the mission accomplishment degree of rescue mission. Equation (13) gives out its computational method. Finally, the definition of cost-revenue function is shown in Equations ( 14) and (15).…”

Section: The Cost-revenue Functionmentioning

confidence: 99%

“…Table 19 shows the results. In Table 19, methods of GA+ABC, GA+IABC, AGA+ABC, and AGA+IABC are investigated; the results of group assignment, mission assignment, total revenue (see (13)), total cost (see (10)), processing time, and path length are simulated. From Table 19, the AGA+IABC can get the best performance except for the processing time.…”

Section: The Evaluation Experiments Of Disaster Rescue Simulationmentioning

confidence: 99%

“…Table 1 presents an algorithm review of multi-UAV mission assignment and path planning. Regarding mission assignment, the heuristic algorithm [13][14][15] uses the intuitive experiences to build its computational strategy; the mathematical programming [16,17] employs the classic algebra or matrix theory to solve optimal computation with various of constraints; and the stochastic intelligent optimization [18][19][20] considers the typical theories, such as biological or physical rules, to accomplish optimal computation. As for path planning, the mathematical programming [21,22] can solve path generation with the consideration of environment and kinematics factors; the artificial potential field method [23,24] considers the balance between the attractive and repulsive forces to implement path searching; the graph-based method [25][26][27][28] uses the modeling and computation of graph theory to accomplish path planning; and the intelligent optimization [29,30] can carry out path creation using typical biological imitation algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Liu

Wang

et al. 2021

Actuators

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An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

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Section: The Cost-revenue Functionmentioning

confidence: 99%

Section: The Evaluation Experiments Of Disaster Rescue Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Liu

Wang

et al. 2021

Actuators

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“…Although these methods have the advantages of computational efficiency and are simple to implement, they oversimplify the drone model and can only be applied to simple mission scenarios. Heuristic algorithms iteratively optimize mission assignment schemes through strategies based on experience or rules, including genetic algorithm (GA) [18], evolutionary algorithm (EA) [19], tabu search (TS) algorithm [20], simulated annealing (SA) algorithm [21], etc. Path planning methods are divided into five categories: graph-based methods accomplish path planning by constructing a robust path graph, such as the Voronoi diagram method [22].…”

Section: Introductionmentioning

confidence: 99%

Multi-Drone Optimal Mission Assignment and 3D Path Planning for Disaster Rescue

Xiong

Liu

et al. 2023

Drones

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In a three-dimensional (3D) disaster rescue mission environment, multi-drone mission assignments and path planning are challenging. Aiming at this problem, a mission assignment method based on adaptive genetic algorithms (AGA) and a path planning method using sine–cosine particle swarm optimization (SCPSO) are proposed. First, an original 3D digital terrain model is constructed. Second, common threat sources in disaster rescue environments are modeled, including mountains, transmission towers, and severe weather. Third, a cost–revenue function that considers factors such as drone performance, demand for mission points, elevation cost, and threat sources, is formulated to assign missions to multiple drones. Fourth, an AGA is employed to realize the multi-drone mission assignment. To enhance convergence speed and optimize performance in finding the optimal solution, an AGA using both the roulette method and the elite retention method is proposed. Additionally, the parameters of the AGA are adjusted according to the changes in the fitness function. Furthermore, the improved circle algorithm is also used to preprocess the mission sequence for AGA. Finally, based on the sine–cosine function model, a SCPSO is proposed for planning the optimal flight path between adjacent task points. In addition, the inertia and acceleration coefficients of linear weights are designed for SCPSO so as to enhance its performance to escape the local minimum, explore the search space more thoroughly, and achieve the purpose of global optimization. A multitude of simulation experiments have demonstrated the validity of our method.

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Joint task management of sensor and weapon based on distributed management system

Zhang

Chen

2017

2017 36th Chinese Control Conference (CCC)

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An artificial intelligence based novel approach for real-time allocation of armament to hostile targets

Cited by 3 publications

References 13 publications

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Multi-Drone Optimal Mission Assignment and 3D Path Planning for Disaster Rescue

Joint task management of sensor and weapon based on distributed management system

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