A Novel Step Optimal Path Planning Algorithm for the Spherical Mobile Robot Based on Fuzzy Control

Guo, Jian; Li, Chunying; Guo, Shuxiang

doi:10.1109/access.2019.2962074

Cited by 53 publications

(23 citation statements)

References 18 publications

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“…There are many vulnerability factors in the portrayal of the environment in the self-ruling mobile robot path arranging, it cannot easily group the situation to a particular situation, and a fuzzy logic concept is embraced in this type of situation, [66]. J. Guo, C. Li, and S. Guo [67] used fuzzy logic in a novel path planning technique. Firstly, the configuration and rectify of the ultrasonic sensors (HC-SR04) were done by analyzing the motion model of the spherical mobile robot.…”

Section:  the Fuzzy Logic (Fl)mentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

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Mobile robots use is rising every day. Path planning algorithms are needed to make a traveler of robots with the least cost and without collisions. Many techniques have been developed in path planning for mobile robot worldwide, however, the most commonly used techniques are presented here for further study. This essay aims to review various path planning strategies for mobile robots using different optimization methods taken recent publisher’s paper in last five year.

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Section:  the Fuzzy Logic (Fl)mentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

View full text Add to dashboard Cite

show abstract

“…The current path planning algorithms mainly include the colony algorithms (Liu et al, 2019;Ye et al, 2020;Zhang et al, 2020;Zhu et al, 2020), PSO (Krell et al, 2019;Wang Y. B. et al, 2019;Liu X. H. et al, 2021;Song et al, 2021), A * algorithms (Xiong et al, 2020;Tang et al, 2021;Tullu et al, 2021), artificial potential field methods Azmi and Ito, 2020;Song et al, 2020;Yao et al, 2020), genetic algorithms (Hao et al, 2020;Li K. R. et al, 2021;Wen et al, 2021), fuzzy control algorithms (Guo et al, 2020;Zhi and Jiang, 2020), fast marching algorithms (Sun et al, 2021;Wang et al, 2021;Xu et al, 2021), and deep reinforcement learning algorithms (Li L. Y. et al, 2021;Lin et al, 2021;Xie et al, 2021). PSO is an evolutionary computation algorithm that can be used to find the optimal solution through collaboration and information sharing between individuals in the group, as in path planning, the optimal solution is to find the shortest path.…”

Section: Introductionmentioning

confidence: 99%

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Cheng

Zheng³

et al. 2021

Front. Neurorobot.

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The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.

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“…e current research trend is to combine the above methods to improve the shortcomings of each algorithm, and some researchers have done a lot of research on these methods. Guo et al proposed a novel step optimal path planning method based on fuzzy control [26]. A fuzzy controller with two inputs and two outputs is designed, which can complete the path planning in the common environment and reach the target point successfully.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

Guo

Gao

et al. 2021

Mathematical Problems in Engineering

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Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.

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A Novel Step Optimal Path Planning Algorithm for the Spherical Mobile Robot Based on Fuzzy Control

Cited by 53 publications

References 18 publications

A Review on Path Planning Algorithms for Mobile Robots

A Review on Path Planning Algorithms for Mobile Robots

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

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