Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)

Roy, Rohit; Tu, You-Peng; Sheu, Long-Jye; Chieng, Wei-Hua; Tang, Li‐Chuan; Ismail, Hasan

doi:10.3390/s23073606

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Neural network training is occurred once at the design step according to a performance index then the neural network model still the same during the operation. The neural network model architecture will be fixed before training [36,37].…”

Section: Self-tuning Neural Network Pid Controller (Stnn-pid)mentioning

confidence: 99%

Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator

Mohamed,

Oleiwi,

Abood

et al. 2023

Fractal Fract

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The robotic manipulator is considered one of the complex systems that include multi-input, multi-output, non-linearity, and highly coupled. The uncertainty in the parameters and external disturbances have a negative influence on the performance of the system. Therefore, the controllers that will be designed for these systems must be able to deal with these complexities and difficulties. The Proportional, Integral, and Derivative (PID) controller is known to be simple and well robust, while the neural network has a solid ability to map complex functions. In this paper, we propose six control structures by combining the benefits of PID controller with integer and fractional order and the benefits of neural networks to produce hybrid controllers for a 2-Link Rigid Robot Manipulator (2-LRRM) handling with the problem of trajectory tracking. The Gorilla Forces Troops Optimization algorithm (GTO) was used to tune the parameters of the proposed controller schemes to minimize the Integral of Time Square Error (ITSE). In addition, the robustness of the performance of the suggested control systems is tested by altering the initial position, external disturbances and parameters and carried out using MATLAB. The best performance of the proposed controllers was the Neural Network Fractional Order Proportional Integral Derivative Controller (NNFOPID).

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Section: Self-tuning Neural Network Pid Controller (Stnn-pid)mentioning

confidence: 99%

Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator

Mohamed,

Oleiwi,

Abood

et al. 2023

Fractal Fract

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show abstract

“…For example, in [ 1 ], navigation is based on vanishing points to guide a quadcopter through corridors without colliding with walls. Other researchers, such as [ 2 , 3 , 4 , 5 , 6 ], have used different types of sensors, including stereo, monocular, and RGB-D cameras, to obtain three-dimensional information about the environment. They have also implemented techniques such as simultaneous localization and mapping (SLAM) to reconstruct maps of the environment, which are then used for motion planning and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Collision-Free Image-Based Visual Servoing Method for Mobile Robot Navigation Based on the Path Planning in the Image Plan

Albekairi,

Mekki,

Kaaniche

et al. 2023

Sensors

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In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera’s field of view. A single monocular sensor’s sole visual data serves as the basis for our method. The fundamental idea is to manage and control the dynamics associated with any trajectory generated in the image plane. We show that the differential flatness of the system’s dynamics can be used to limit arbitrary paths based on the number of points on the object that need to be reached in the image plane. This creates a link between the current configuration and the desired configuration. The number of required points depends on the number of control inputs of the robot used and determines the dimension of the flat output of the system. For a two-wheeled mobile robot, for instance, the coordinates of a single point on the object in the image plane are sufficient, whereas, for a quadcopter with four rotating motors, the trajectory needs to be defined by the coordinates of two points in the image plane. By guaranteeing precise tracking of the chosen trajectory in the image plane, we ensure that problems of collision with obstacles and leaving the camera’s field of view are avoided. Our approach is based on the principle of the inverse problem, meaning that when any point on the object is selected in the image plane, it will not be occluded by obstacles or leave the camera’s field of view during movement. It is true that proposing any trajectory in the image plane can lead to non-intuitive movements (back and forth) in the Cartesian plane. In the case of backward motion, the robot may collide with obstacles as it navigates without direct vision. Therefore, it is essential to perform optimal trajectory planning that avoids backward movements. To assess the effectiveness of our method, our study focuses exclusively on the challenge of implementing the generated trajectory in the image plane within the specific context of a two-wheeled mobile robot. We use numerical simulations to illustrate the performance of the control strategy we have developed.

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Simulator for omnidirectional robots equipped with 2D and 3D LiDAR sensors

Clotet,

Rubies,

Bitriá

et al. 2023

2023 IEEE 2nd Industrial Electronics Society Annual on-Line Conference (ONCON)

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Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)

Cited by 4 publications

References 36 publications

Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator

Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator

An Innovative Collision-Free Image-Based Visual Servoing Method for Mobile Robot Navigation Based on the Path Planning in the Image Plan

Simulator for omnidirectional robots equipped with 2D and 3D LiDAR sensors

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