A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

Liu, Xiaohuan; Zhang, Degan; Zhang, Ting; Zhang, Jie; Wang, Jiaxu

doi:10.1108/ec-09-2020-0500

Cited by 22 publications

(16 citation statements)

References 47 publications

(26 reference statements)

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“…Step 1: Define any graphic as a set 1 S containing 1 m points, and define any point as 11 S in 1 S to be the starting point of the processing at coordinates 11 11 ( , ) xy . Then delete 1 S from the point set S , and define the rest of the sets in a set S as the first point set Step 2: Calculate the distances ,…”

Section: B Mathematical Descriptionmentioning

confidence: 99%

“…Hilli et al [9] employed particle swarm optimization to find the best path, and Islam et al [10] proposed a new hybrid metaheuristic algorithm that combined particle swarm optimization with variable neighborhood search to solve the clustered vehicle routing problem. Liu et al [11] designed a hybrid path-planning algorithm based on optimized reinforcement learning and improved particle swarm optimization to solve the pathplanning problem of intelligent driving vehicles. Halassi et al [12] presented a new multi-objective discrete particle swarm algorithm for the Capacitated vehicle routing problem, and Wisittipanich et al [13] applied two metaheuristic methods with particular solution representation, i.e., particle swarm optimization and differential evolution to find delivery routings with minimum travel distances.…”

Section: Introductionmentioning

confidence: 99%

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Improved Particle Swarm Optimization for Laser Cutting Path Planning

2023

IEEE Access

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This research focuses on the long empty cutting path problem during the laser cutting process by employing an improved proximity method to establish the starting point set in complex closed graphics. Specifically, this work improves the particle swarm algorithm and proposes the Levy Flight, power function, and Singer map employed particle swarm optimization (LPSPSO) to avoid the disadvantages of the standard particle swarm optimization (PSO) algorithm. Specifically, the comprehensive prospect-regret theoretical model evaluation value is used as the fitness value to guide the algorithm's evolution and adaptively adjust the parameters in the LPSPSO algorithm, including the inertia weight power function, the learning factors, and the chaotic random number based on the Singer chaotic map. Additionally, the Levy flight is introduced to disturb the particles and prevent local optimization. This is achieved by adjusting the Levy flight threshold based on the distance between the particles to prevent the Levy flight from starting prematurely and increasing the calculation burden. To verify the performance of the LPSPSO algorithm, it was challenged against three state-of-the-art algorithms on 22 benchmark test instances and a laser cutting problem, with the results revealing that the LPSPSO algorithm has a better performance and can be used to solve the empty length of the laser cutting path problem.INDEX TERMS laser cutting path planning, improved particle swarm optimization, improved proximity method, Levy flight threshold, comprehensive prospect-regret theory, chaotic random number

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Section: B Mathematical Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization for Laser Cutting Path Planning

2023

IEEE Access

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“…Mohammed Hussein et al proposed a modified Particle Swarm Optimization (PSO), which is named MPSO [25]. Liu et al proposed a hybrid path planning algorithm based on optimized reinforcement learning (RL) and improved particle swarm optimization (PSO) [32]. However, the improved algorithm still has some limitations, such as low convergence accuracy, easy precocity, and so on.…”

Section: Related Workmentioning

confidence: 99%

Path Planning of Mobile Robots Based on An Improved Particle Swarm Optimization Algorithm

Yuan

Sun

2022

Preprint

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Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a "high-intensity training" mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving mobile robot path planning problem.

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“…Optimal path selection needs to be determined based on the flight performance constraints, the specific mission requirements, and the flight environment constraints [5,6]. Scholars have conducted a lot of research on the UAV path planning problem and proposed a series of algorithms, such as graph-based optimization methods, including the visibility graph (VG) algorithm [7] and Voronoi diagrams [8]; the searching-based methods, including the Dijkstra [9] algorithm, A* algorithm [10] and D* algorithm [11]; the sampling-based methods, such as PRM algorithm [12] and RRT algorithm [13]; the nature-inspired methods, such as genetic algorithm (GA) [14], ant colony optimization (ACO) [15], artificial potential field algorithm [16], particle swarm optimization (PSO) [17] and fluid-based algorithm [18]; and other methods, such as control theory-based methods [19].…”

Section: Introductionmentioning

confidence: 99%

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

Guo

Liu

et al. 2022

IJGI

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In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.

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A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

Cited by 22 publications

References 47 publications

Improved Particle Swarm Optimization for Laser Cutting Path Planning

Improved Particle Swarm Optimization for Laser Cutting Path Planning

Path Planning of Mobile Robots Based on An Improved Particle Swarm Optimization Algorithm

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

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