A hybrid algorithm of particle swarm optimization, metropolis criterion and RTS smoother for path planning of UAVs

Wu, Xiande; Bai, Wei; Xie, Yaen; Sun, Xinzhu; Deng, Chao; Cui, Hongtao

doi:10.1016/j.asoc.2018.09.011

Cited by 75 publications

(33 citation statements)

References 17 publications

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“…Calculate the pbest d si of ith particle according to Algorithm 1; (14) / * Multilearning strategy * / (15) Calculate the gbestk d of ith particle according to the top k of gbest; (16) / * Update the velocity and position of ith particle * / (17) Calculate the velocity v d i of ith particle using equation (4); (18) Limit the velocity using…”

Section: (F5-f8) and Figures 4(e)-4(h)mentioning

confidence: 99%

“…It is characterized by simplistic implementation, fast convergence, and few parameters. As a result, the PSO algorithm is developing rapidly in recent years and has been successfully applied in various scientific and engineering fields, such as in water distribution network design [10], power systems [11], manipulator motion planning [12], optimal control [13], image processing [14], artificial neural networks [15], and other fields [16][17][18][19]. e velocity and position of each particle in the basic PSO algorithm are updated through its own personal history best position (pbest) and global best position (gbest).…”

Section: Introductionmentioning

confidence: 99%

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A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning

et al. 2019

Mathematical Problems in Engineering

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In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.

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Section: (F5-f8) and Figures 4(e)-4(h)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning

et al. 2019

Mathematical Problems in Engineering

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“…In [4], Dubins curve is employed to smoothly connect the waypoints which are generated by the ACO algorithm, so an intelligent self-organized algorithm (ISOA) is presented to cope with the distributed control architecture problem. Wu et al [5] proposed a hybrid particle swarm optimization (PSO) algorithm by introducing the metropolis criterion, and it can accept the suboptimal solution with the certain probability to guarantee that the algorithm can jump out of the local optimum. Moreover, the rauch tung striebel (RTS) smoother is improved to smooth the generated trajectory.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning for UAV Based on Improved ACO Algorithm

et al. 2020

IEEE Access

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Trajectory planning is an important subject in the field of unmanned aerial vehicles (UAVs). However, existing methods do not solve some problems well, such as slow convergence speed and low searching efficiency of related algorithms and collisions between UAVs and the obstacles. Therefore, a method is proposed to solve a trajectory planning problem for multi-UAV in a static environment, which includes three main phases: the initial trajectory generation, the trajectory correction, and the smooth trajectory planning. First, the improved algorithm called MACO which introduces the metropolis criterion into the node screening mechanism of the ant colony optimization (ACO) algorithm is presented to generate the initial trajectory that can effectively avoid falling into the local optimal solution and stagnation. Then, considering size constraint of UAVs, this paper gives three trajectory correction schemes to solve the collision avoidance problem and further to optimize the initial trajectory. Last, the discontinuity originated from the sharp turn which occurred in the trajectory planning is solved by the inscribed circle (IC) smooth method, and the produced trajectory has shown better performance in reducing fuel consumption and improving the trajectory safety. Experimental results demonstrate that the proposed method has the high feasibility and effectiveness from aspects of the optimal solution, the collision avoidance, and the smooth trajectory in the trajectory planning problem for UAVs.

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“…After years of research and exploration, many control algorithms have emerged. Currently, widely used path planning algorithms include ant colony algorithms [4][5][6], bee swarm algorithms [7][8], the virtual artificial potential field method [9][10], quasi-annealing algorithms [11], Neural network algorithms [12][13][14] and particle swarm optimization [15][16][17]. However, the most commonly used task allocation strategies are artificial self-organizing neural network algorithms (SOM) [18] and tree structure algorithms [19].…”

Section: Introductionmentioning

confidence: 99%

Multi-AUV Collaborative Operation Based on Time-Varying Navigation Map and Dynamic Grid Model

Chen

Bai

et al. 2020

IEEE Access

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In a dynamic and complex environment, to improve the cooperative operation efficiency of multiple AUV groups, a bionic neural wave network (BNWN) algorithm, and a velocity vector synthesis (VVS) algorithm are proposed. A strategy of space decomposition and node space recursion is adopted to provide dynamic navigation maps for AUV monomers and to modularize the tasks. A closed boundary function is introduced to construct a dynamic grid model to autonomously avoid obstacles with multiple moving forms. The results of three sets of simulation experiments show that the number of changes in direction, the total path length, and the collision rate of AUV individuals are greatly reduced. These results prove that the proposed algorithm has high autonomy and strong adaptability.

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A hybrid algorithm of particle swarm optimization, metropolis criterion and RTS smoother for path planning of UAVs

Cited by 75 publications

References 17 publications

A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning

A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning

Trajectory Planning for UAV Based on Improved ACO Algorithm

Multi-AUV Collaborative Operation Based on Time-Varying Navigation Map and Dynamic Grid Model

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