Autonomous Mobile Robot Obstacle Avoidance Using Fuzzy-PID Controller in Robot’s Varying Dynamics

Aqeel-ur-Rehman, .; Cai, Chenxiao

doi:10.23919/ccc50068.2020.9188467

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…The PID control is considered as Method 4 (Aqeel‐Ur‐Rehman & Cai, 2020; MohandSaidi & Mellah, 2022). The fundamental idea behind PID is to modify the feedback signal, particularly the error between the desired and measured states.…”

Section: Resultsmentioning

confidence: 99%

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Zhang,

Yang,

Wang

et al. 2023

Computer aided Civil Eng

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Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross‐entropy‐based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT‐UMbot) for road cracks. Specifically, the CEAFC method uses cross‐entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT‐UMbot based on a four‐wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state‐of‐the‐art performance with high efficiency.

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Section: Resultsmentioning

confidence: 99%

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Zhang,

Yang,

Wang

et al. 2023

Computer aided Civil Eng

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“…Gharajeh & Jond presented the adaptive neuro-fuzzy inference system (ANFIS) method and shortened the planned path by 30 percent compared to other algorithms [30]. Besides, the most common integrated use is FL and PID (proportional-integral-derivative) algorithms [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot

Top,

Gökbulut

2024

Turkish Journal of Science and Technology

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This paper uses a cascade-connected fuzzy-PI controller to control the position and speed of a differential drive and four-wheel drive of an autonomous mobile robot for optimal path planning. The angular speed information obtained from the encoder of each motor and the instantaneous position and angle information of the robot were calculated. The angle and position error between the reference points and these values is applied to the fuzzy logic controller as an input signal. The robot angular and linear speed data obtained from the fuzzy logic output were converted into reference speed values with kinematic equations to be applied to the motors. The speed controls of the motors were carried out with a PI controller based on these reference values. The study was performed both as a simulation in the MATLAB program and experimentally in the laboratory environment for one and more reference coordinates. In the experimental study, reference values were sent to the robot via Bluetooth with the Android application designed. At the same time, the instant data of the robot was also collected on the Android device through the same application. These data collected in Excel format were transferred to the computer via e-mail and the graphics were drawn in the MATLAB program. When the results were examined, it was seen that both speed and position control were successfully implemented with the fuzzy-PI controller for optimum path planning of the robot.

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Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Tolossa,

Gunasekaran,

Halder

et al. 2024

Robotica

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This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

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Autonomous Mobile Robot Obstacle Avoidance Using Fuzzy-PID Controller in Robot’s Varying Dynamics

Cited by 7 publications

References 16 publications

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

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