Hybrid Form of Particle Swarm Optimization and Genetic Algorithm For Optimal Path Planning in Coverage Mission by Cooperated Unmanned Aerial Vehicles

Haghighi, Hassan; Sadati, Seyed Hossein; Dehghan, Seyyed Mohammad Mehdi; Karimi, Jalal

doi:10.5028/jatm.v12.1169

Cited by 13 publications

(13 citation statements)

References 20 publications

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“…The simulation results show that the algorithm performs well in finding a short and collision free path in different environment conditions. Haghighi et al [25] the authors proposed method by the hybridization of the Grey Wolf optimizer-particle swarm optimization algorithm. The method was depended on collision avoidance and detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

Hybrid metaheuristic approach for robot path planning in dynamic environment

Amar

Jasim

2021

Bulletin EEI

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Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.

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Section: Related Workmentioning

confidence: 99%

Hybrid metaheuristic approach for robot path planning in dynamic environment

Amar

Jasim

2021

Bulletin EEI

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“…Combining global and local planning approaches enhances work efficiency, accelerates target task completion, reduces system consumption, and bolsters UAV flight robustness [13]. Algorithm fusion planning methods will be the trend for current and future UAV path planning [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

An Improved Artificial Potential Field UAV Path Planning Algorithm Guided by RRT Under Environment-Aware Modeling: Theory and Simulation

Liu,

Yan,

Yang

et al. 2024

IEEE Access

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Unmanned Aerial Vehicles (UAVs) have been extensively researched and used in civil and military applications due to their effectiveness and flexibility. However, when identifying obstacles and avoiding them, most of the existing path planning methods fail to accurately perceive the environment, such as without considering the differences between obstacles, which leads to low timeliness and easy fall into a local minimum. In this work, an improved artificial potential field UAV path planning algorithm (G-APF) guided by the rapidly-exploring random tree (RRT) based on an environment-aware model is designed to overcome the limitations of traditional methods. The model can perceive different objects in the environment through the addition of supervised environment modeling to traditional unsupervised path planning. Specifically, an environment-aware model based on YOLOv8 is used to establish the UAV flight environment model, and an adaptive optimal threat distance calculation module is used to construct the repulsive potential field. Secondly, to improve the timeliness of path planning and the global awareness of the model, we first use the G-APF algorithm to plan the rough flight path based on the UAV flight environment. Then, the initially generated trajectory is replanned by building an attractive potential field and combining it with a repulsive potential field. Finally, the problems of local minimum and target unreachability and local trajectory oscillation generated by the artificial potential field (APF) algorithm are solved by G-APF. Experiments with generated regions are performed to demonstrate the efficiency and effectiveness of the proposed approach.

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“…The incredible problem-solving attributes pertaining to hybridized optimization techniques could be verified by investigating the below mentioned citations where Xiaoya Ma [33] introduced an optimal control technique for a Whole Network Control System (WNCS) and utilized the eclectic ensemble of GA, Neural Network (NN), and fuzzy control, incorporating the benefits of excellent self-learning capacity of NN with powerful global search capability of GA. B. H. Haghighi et al [34] proposed a hybrid architecture by integrating GA and PSO for optimal path planning problems of diversified UAVs amid coverage missions. S. A. Malik et al [35] recommended a heuristic scheme based on GA for numerically solving the nonlinear dynamical system of the generalized Burgers'-Fisher equation.…”

Section: Introductionmentioning

confidence: 99%

Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems

et al. 2023

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This paper presents a pragmatic approach established on the hybridization of nature-inspired optimization algorithms and Bernstein Polynomials (BPs), achieving the optimum numeric solution for Nonlinear Optimal Control Problems (NOCPs) of dynamical systems. The approximated solution for NOCPs is obtained by the linear combination of BPs with unknown parameters. The unknown parameters are evaluated by the conversion of NOCP to an error minimization problem and the formulation of an objective function. The Fitness Dependent Optimizer (FDO) and Genetic Algorithm (GA) are used to solve the objective function, and subsequently the optimal values of unknown parameters and the optimum solution of NOCP are attained. The efficacy of the proposed technique is assessed on three real-world NOCPs, including Van der Pol (VDP) oscillator problem, Chemical Reactor Problem (CRP), and Continuous Stirred-Tank Chemical Reactor Problem (CSTCRP). The final results and statistical outcomes suggest that the proposed technique generates a better solution and surpasses the recently represented methods in the literature, which eventually verifies the efficiency and credibility of the recommended approach.INDEX TERMS bernstein polynomials, dynamical systems, fitness dependent optimizer, genetic algorithm, nonlinear optimal control problems, optimization problem, optimization techniques.

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Hybrid Form of Particle Swarm Optimization and Genetic Algorithm For Optimal Path Planning in Coverage Mission by Cooperated Unmanned Aerial Vehicles

Cited by 13 publications

References 20 publications

Hybrid metaheuristic approach for robot path planning in dynamic environment

Hybrid metaheuristic approach for robot path planning in dynamic environment

An Improved Artificial Potential Field UAV Path Planning Algorithm Guided by RRT Under Environment-Aware Modeling: Theory and Simulation

Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems

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