Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

Ma, Jianwei; Liu, Yang; Zang, Shaofei; Wang, Lin

doi:10.1155/2020/9813040

Cited by 45 publications

(31 citation statements)

References 32 publications

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“…Here, the fitness function is equivalent to the objective function of the problem [34]. In order to speed up the convergence of the genetic algorithm while ensuring low complexity of the algorithm, and to find the optimal path that can smoothly avoid obstacles and quickly reach the target point [35], a multiobjective fitness function based on path length, path safety, and path energy consumption is designed in this paper, which is specifically expressed as follows:…”

Section: Population Initializationmentioning

confidence: 99%

Path Planning of Mobile Robot Based on Improved Multiobjective Genetic Algorithm

Liu

2021

Wireless Communications and Mobile Computing

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Path planning is the core technology of mobile robot decision-making and control and is also a research hotspot in the field of artificial intelligence. Aiming at the problems of slow response speed, long planning path, unsafe factors, and a large number of turns in the conventional path planning algorithm, an improved multiobjective genetic algorithm (IMGA) is proposed to solve static global path planning in this paper. The algorithm uses a heuristic median insertion method to establish the initial population, which improves the feasibility of the initial path and generates a multiobjective fitness function based on three indicators: path length, path security, and path energy consumption, to ensure the quality of the planned path. Then, the selection, crossover, and mutation operators are designed by using the layered method, the single-point crossover method, and the eight-neighborhood-domain single-point mutation method, respectively. Finally, the delete operation is added, to further ensure the efficient operation of the mobile robot. Simulation experiments in the grid environment show that the algorithm can improve the defects of the traditional genetic algorithm (GA), such as slow convergence speed and easy to fall into local optimum. Compared with GA, the optimal path length obtained by planning is shortened by 17%.

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Section: Population Initializationmentioning

confidence: 99%

Path Planning of Mobile Robot Based on Improved Multiobjective Genetic Algorithm

Liu

2021

Wireless Communications and Mobile Computing

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“…Hao et al [ 16 ] proposed an adaptive genetic algorithm based on collision detection, which solves the problem of low quality of genetic algorithm paths and low convergence iterations by optimizing genetic operators and adding collision detection methods. Ma et al [ 17 ] proposed a genetic algorithm based on Bezier curves, which can effectively generate shorter and smoother paths. Li et al [ 18 ] proposed an improved ACO, which improved the convergence speed by adaptively changing the wave coefficient and updating the state transition rules.…”

Section: Related Workmentioning

confidence: 99%

“…Path planning is a multiobjective optimization problem [33], which needs to consider the safety, smoothness, and total length of the path. Its mathematical model is as follows, where formula (16) represents the safety of the minimized path, formula (17) represents the smoothness of the minimized path, and formula (19) represents the length of the minimized path:…”

Section: Problem Definitionmentioning

confidence: 99%

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Song

Yang

et al. 2021

Computational Intelligence and Neuroscience

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The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

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“…To ensure the journey of the mobile robot from point to point is complete, specific data is given to the mobile robot. Since the environment is a 2-D space, a grid-based space (where several obstacles are normal, known and stable) is used in this proposed algorithm to represent the environmental space, and decimal encoding is used according to [32]. In this article, the model's multi-layered representation is used to define the 1378 environmental space.…”

Section: Genetic Algorithm For 5g Mobile Robot Schemementioning

confidence: 99%

Industry 4.0 with intelligent manufacturing 5G mobile robot based on genetic algorithm

Hawari

Apandi²

2021

IJEECS

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A manufacturing fifth-generation (5G) mobile robot is a new development of industry 4.0 application, deploying an unmanned system. This study aims to implement a robot system for industrial applications in real-time with remote sensors to enable humans. Moreover, there is still some obstacle to cope with the better optimization solution for manufacturing 5G robot. This paper proposed a latency network algorithm for the manufacturing 5G mobile robot. An improved genetic algorithm (GA) by restructuring the genes is applied to plan a mobile robot path. The process of the robot path in a complex workspace is proposed, considering the node's collision-free constraint in the moving phase of a robot. The proposed scheme improves the robot path and delivery efficiency of the robot on average at 68% by moving on the industrial environment's shortest path and time average of the mobile robot reach its destination.

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Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

Cited by 45 publications

References 32 publications

Path Planning of Mobile Robot Based on Improved Multiobjective Genetic Algorithm

Path Planning of Mobile Robot Based on Improved Multiobjective Genetic Algorithm

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Industry 4.0 with intelligent manufacturing 5G mobile robot based on genetic algorithm

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