Mobile robot path planning using fuzzy enhanced improved Multi-Objective particle swarm optimization (FIMOPSO)

Sathiya, V.; Chinnadurai, M.; Ramabalan, S.

doi:10.1016/j.eswa.2022.116875

Cited by 33 publications

(16 citation statements)

References 22 publications

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“…Also, it is difficult to make adjustments to dynamic changes in the environment. With the development of the field of artificial intelligence, a path planning method based on artificial intelligence has attracted the attention and research of scholars, such as artificial neural network (Liu et al , 2022a, 2022b), evolutionary algorithm (Zhang et al , 2022), swarm intelligence algorithm, such as PSO (Sathiya et al , 2022), ACO (Liu et al , 2022a, 2022b), simulated annealing algorithm (Gao Shan, 2009), Artificial Fish Swarm Algorithm (Li et al , 2022), Whale Algorithm (Yan et al , 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

Cheng,

Meng

2024

RIA

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Purpose This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops. Design/methodology/approach In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions. Findings The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop. Originality/value In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

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Section: Literature Reviewmentioning

confidence: 99%

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

Cheng,

Meng

2024

RIA

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“…Suppose we want to optimize the operational performance of time, energy consumption, and smoothness. In that case, the required optimal solution can be obtained by minimizing the solution of equation (13).…”

Section: Multi-objective Optimization Function Constructionmentioning

confidence: 99%

“…Barnett et al 9 used a dynamic planning approach to solve the optimal time trajectory planning problem. In terms of trajectory time optimization, many researchers have studied optimization algorithms mainly including genetic algorithms, [10][11][12] particle swarm optimization algorithms, 13,14 artificial bee colony algorithms, 15,16 glowworm swarm optimization algorithms, 17 and so on.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning of tire laser engraving orthogonal robot based on an improved multi-objective grasshopper optimization algorithm

Liu,

Xu,

Zhang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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The traditional tire laser engraving robots have the low and stable running capability and discontinuous motion trajectory during operation, leading to long running time, high energy consumption, and uneven operation. In order to solve these problems, this paper proposes an orthogonal robot trajectory planning method based on an improved multi-objective grasshopper optimization algorithm (IMOGOA) with the orthogonal robot as the research object. Firstly, this paper used the quintic non-uniform rational b-splines (NURBS) curves to construct the trajectory. Secondly, the optimization objectives of the orthogonal robot’s running time, energy consumption, and smoothness were used to obtain the Pareto optimal solution by using the IMOGOA to establish the objective function. Then, the optimization method was simulated and analyzed, and the optimal solution was selected to compare with the rest of the non-dominated solution sets. The simulation results showed that the proposed method reduced the running time by 33.68%, improved the energy consumption and smoothness performance by 34.48% and 56.58%, and achieved multi-objective comprehensive optimization of the orthogonal robot. Thus, the effectiveness and feasibility of the proposed method were verified. Finally, a tire laser engraving orthogonal robot experimental platform was built for physical verification experiments. The experimental results showed that the experimental trajectory deviated from the simulated trajectory by only 5.63%, further proving the proposed method’s practicality.

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“…Therefore, the swarm-based algorithm improves the accuracy of the solution, and lots of scholars use the swarm-based algorithm to solve the MRPP. V. Sathiya [ 13 ] proposed a FIMOPSO to solve mobile robot path planning. A. Lazarowska [ 14 ] uses the Discrete Artificial Potential Field algorithm (DAPF) to solve the MRPP.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning

Tian,

Liang,

Wei

et al. 2024

Biomimetics

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With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA’s performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor.

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Mobile robot path planning using fuzzy enhanced improved Multi-Objective particle swarm optimization (FIMOPSO)

Cited by 33 publications

References 22 publications

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

Trajectory planning of tire laser engraving orthogonal robot based on an improved multi-objective grasshopper optimization algorithm

Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning

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