Mobile Robot Path Planning based on Fuzzy Logic Algorithm in Dynamic Environment

Wang, Shanxia

doi:10.1109/aie57029.2022.00027

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Fuzzy logic algorithms are adept at handling data that are imprecise, uncertain, or not clearly defined [23]. They are suitable for decision-making and control systems in cases where exact binary logic may not be applicable.…”

Section: B Fuzzy Logic-based Algorithmmentioning

confidence: 99%

Evaluation of popular path planning algorithms

Kara

2024

International Journal of Electronics and Telecommunications

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The navigation of mobile robots is a key element of autonomous systems, which allows robots to move effectively and securely in changing environments with greater autonomy and precision. This study aims to provide researchers with a comprehensive guide for selecting the best path-planning methods for their particular projects. We evaluate some popular algorithms that are regularly used in mobile robot navigation, in order to demonstrate their specifications and determine where they are most effective. For example, one algorithm is used to model the problem as a standard graph, and another algorithm is found to be the most suitable for highly dynamic and highly dimensional environments, due to its robust path-planning capabilities and efficient route construction. We also filter high-performance algorithms in terms of computational complexity, accuracy, and robustness. In conclusion, this study provides valuable information on its individual strengths and weaknesses, helping robotics and engineers make informed decisions when selecting the most appropriate algorithm for their specific applications.

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Section: B Fuzzy Logic-based Algorithmmentioning

confidence: 99%

Evaluation of popular path planning algorithms

Kara

2024

International Journal of Electronics and Telecommunications

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“…The Taylor series is extended to convert a nonlinear function to a linear function in equation (39). are defined as equation ( 42) and (43).…”

Section: 𝑠̇(𝑒(𝑡)) = 𝐺 𝑛 [𝑍 ̃𝑇𝔽 * + 𝑍 ̂𝑇𝐹 ̃+ 𝜀 − 𝑢 𝑅𝐶 ] − 𝜚𝑠𝑔𝑛 [𝑠 (𝑒(𝑡...mentioning

confidence: 99%

“…To deal with the uncertain components, recent studies on intelligent control have proposed directly incorporating human expertise into neural networks [28][29][30][31]. Fuzzy inference systems have been used as adaptive controllers for robots [32][33][34][35][36], showing one of the most successful applications of fuzzy logic systems [37][38][39][40]. Naturally, neural networks have been developed in various ways to deal with the uncertainty components [41][42][43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Ngo,

Tran,

et al. 2023

Journal of Robotics and Control

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Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems.

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“…Therefore, selecting and optimizing local path planning is crucial for achieving robot motion in complex and dynamic inspection spaces. Common local path planners include artificial potential fields [20], reactive replanning methods [21,22], and fuzzy algorithm-based methods [23]. Artificial potential fields are relatively simple and exhibit good real-time planning performance.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning

Zhang,

Li,

et al. 2024

Sensors

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High-precision positioning and multi-target detection have been proposed as key technologies for robotic path planning and obstacle avoidance. First, the Cartographer algorithm was used to generate high-quality maps. Then, the iterative nearest point (ICP) and the occupation probability algorithms were combined to scan and match the local point cloud, and the positions and attitudes of the robot were obtained. Furthermore, Sparse Matrix Pose Optimization was carried out to improve the positioning accuracy. The positioning accuracy of the robot in x and y directions was kept within 5 cm, the angle error was controlled within 2°, and the positioning time was reduced by 40%. An improved timing elastic band (TEB) algorithm was proposed to guide the robot to move safely and smoothly. A critical factor was introduced to adjust the distance between the waypoints and the obstacle, generating a safer trajectory, and increasing the constraint of acceleration and end speed; thus, smooth navigation of the robot to the target point was achieved. The experimental results showed that, in the case of multiple obstacles being present, the robot could choose the path with fewer obstacles, and the robot moved smoothly when facing turns and approaching the target point by reducing its overshoot. The proposed mapping, positioning, and improved TEB algorithms were effective for high-precision positioning and efficient multi-target detection.

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Mobile Robot Path Planning based on Fuzzy Logic Algorithm in Dynamic Environment

Cited by 5 publications

References 5 publications

Evaluation of popular path planning algorithms

Evaluation of popular path planning algorithms

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning

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