Goal seeking of mobile robot using fuzzy actor critic learning algorithm

Lachekhab, Fadhila; Tadjine, Mohamed

doi:10.1109/icmic.2015.7409370

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Muse and Wermter [34] applied actor-critical algorithms to platform-independent robot control for navigation tasks in complex environments. Lachekhab and Tadjine [35] fused fuzzy and actor-critical algorithms, added heuristic methods to improve the robustness of the system, and achieved the smooth navigation of a robot.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sampling Path Planning for a 3D Marine Observation Platform Based on Evolutionary Deep Reinforcement Learning

Zhang,

Liu,

Zhou

2023

JMSE

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Adaptive sampling of the marine environment may improve the accuracy of marine numerical prediction models. This study considered adaptive sampling path optimization for a three-dimensional (3D) marine observation platform, leading to a path-planning strategy based on evolutionary deep reinforcement learning. The low sampling efficiency of the reinforcement learning algorithm is improved by evolutionary learning. The combination of these two components as a new algorithm has become a current research trend. We first combined the evolutionary algorithm with different reinforcement learning algorithms to verify the effectiveness of the combination of algorithms with different strategies. Experimental results indicate that the fusion of the two algorithms based on a maximum-entropy strategy is more effective for adaptive sampling using a 3D marine observation platform. Data assimilation experiments indicate that adaptive sampling data from a 3D mobile observation platform based on evolutionary deep reinforcement learning improves the accuracy of marine environment numerical prediction systems.

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Section: Introductionmentioning

confidence: 99%

Adaptive Sampling Path Planning for a 3D Marine Observation Platform Based on Evolutionary Deep Reinforcement Learning

Zhang,

Liu,

Zhou

2023

JMSE

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“…Therefore, a certain controller is often used to overcome this issue. The most popular one is the Fuzzy Logic Controller (FLC) [2,3,4,6,9,10,11,12,13]. It can be designed and assigned as a closed-loop controller.…”

Section: Introductionmentioning

confidence: 99%

“…It transforms the attention of traditional controls focusing on robot sensor accuracy to the modern controls focusing on decision making based on the high uncertainty, complexity, and nonlinearity [7]. The popularity of FLC can be faced from several types of behaviors of a mobile robot, which are successfully improved, such as path-following [9,16,17,18,19], avoiding-obstacle [6,15,17,20,21] and goal-seeking [6], [13] robots.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of the Wall-Following Robot Based on FLC-Ga

Suwoyo

Tian²,

Hajar

2020

Sinergi

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Determination of the improper speed of the wall-following robot will produce a wavy motion. This common problem can be solved by adding a Fuzzy Logic Controller (FLC) to the system. The usage of FLC is very influential on the performance of the wall-following robot. Accuracy in the determination of speed is largely based on the setting of the membership function that becomes the value of its input. So manual setting on membership function can still be enhanced by approaching the certain optimization method. This paper describes an optimization method based on Genetic Algorithm (GA). It is used to improving the ability of FLC to control the wall-following robot controlled by FLC. To provide clarity, the wall-following robot that controlled using an FLC with manual settings will be simulated and compared with the performance of wall-following robots controlled by a fuzzy logic controller optimized by a Genetic Algorithm (FLC-GA). According to comparative results, the proposed method has been showing effectiveness in terms of stability indicated by a small error.

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