A New Approach for Controlling a Trajectory Tracking Using Intelligent Methods

Abdelhakim, Guessoum; Hassam, Abdelouahab

doi:10.1007/s42835-019-00112-1

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…The developments of sensors, microprocessors, and control technology have enabled mobile robots to perform very complex tasks. Today, the main challenge regarding mobile robots is the development of intelligent navigation systems [3]. Navigation is of great importance, since virtually every task requires the robot to travel between different positions by tracking a desired trajectory while being able to localize itself and plan its future movements without human assistance [4] in order to accomplish the defined task [5].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot

Kasmi

Hassam

2021

Eng. Technol. Appl. Sci. Res.

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In this study, Fuzzy Logic (FL) and Interval Type-2 FL (IT-2FL) controllers were applied to a mobile robot in order to determine which method facilitates navigation and enables the robot to overcome real-world uncertainties and track an optimal trajectory in a very short time. The robot under consideration is a non-holonomic unicycle mobile robot, represented by a kinematic model, evolving in two different environments. The first environment is barrier-free, and moving the robot from an initial to a target position requires the introduction of a single action module. Subsequently, the same problem was approached in an environment closer to reality, with objects hindering the robot's movement. This case requires another controller, called obstacle avoidance. This system allows the robot to reach autonomously a well-defined target by avoiding collision with obstacles. The robustness of the structures of the defined controllers is tested in Matlab simulations of the studied controllers. The results show that the IT-2FL controller performs better than the FL controller.

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Section: Introductionmentioning

confidence: 99%

“…, y] are the Cartesian coordinates of the mobile robot, θ is its orientation measured from the x-axis and , r l ϕ ϕ are the angular positions of the right and left wheel respectively[2,3].…”

mentioning

confidence: 99%

Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot

Kasmi

Hassam

2021

Eng. Technol. Appl. Sci. Res.

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“…In order to alleviate the latter problem, some researchers have proposed artificial intelligence methods for auto-tuning in the controller. The controller, however, still lacks proper stability in challenging driving scenarios [4,5].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Autonomous Electric Vehicle for Self-Driving Control under GNSS-Denied Environments

2021

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In this study, the hardware and software design and implementation of an autonomous electric vehicle are addressed. We aimed to develop an autonomous electric vehicle for path tracking. Control and navigation algorithms are developed and implemented. The vehicle is able to perform path-tracking maneuvers under environments in which the positioning signals from the Global Navigation Satellite System (GNSS) are not accessible. The proposed control approach uses a modified constrained input-output nonlinear model predictive controller (NMPC) for path-tracking control. The proposed localization algorithm used in this study guarantees almost accurate position estimation under GNSS-denied environments. We discuss the procedure for designing the vehicle hardware, electronic drivers, communication architecture, localization algorithm, and controller architecture. The system’s full state is estimated by fusing visual inertial odometry (VIO) measurements with wheel odometry data using an extended Kalman filter (EKF). Simulation and real-time experiments are performed. The obtained results demonstrate that our designed autonomous vehicle is capable of performing path-tracking maneuvers without using Global Navigation Satellite System positioning data. The designed vehicle can perform challenging path tracking maneuvers with a speed of up to 1 m per second.

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“…Robots follow a desired trajectory generated by a virtual mobile robot based on its kinematic model and initial posture, which can navigate the mobile robot to the desired position. This tracking problem has been studied by many researchers, and there are a lot of control strategies are proposed for mobile robots, such as model predict control (MPC) [20][21][22], sliding mode control (SMC) [23][24][25][26], fuzzy control [27][28][29], adaptive control [30][31][32], and intelligent control [33,34], etc. However, most of them studied the trajectory tracking problem under the assumption that the movement of the robot is in an obstacle-free environment.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Switching Tracking Control Scheme for Autonomous Mobile Robot in Unknown Obstacle Environments

Sun

Chen

2019

Electronics

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The obstacle avoidance control of mobile robots has been widely investigated for numerous practical applications. In this study, a control scheme is presented to deal with the problem of trajectory tracking while considering obstacle avoidance. The control scheme is simplified into two controllers. First, an existing trajectory tracking controller is used to track. Next, to avoid the possible obstacles in the environment, an obstacle avoidance controller, which is used to determine the fastest collision avoidance direction to follow the boundary of the obstacle at a constant distance, is proposed based on vector relationships between the robot and an obstacle. Two controllers combined via a switch strategy are switched to perform the task of trajectory tracking or obstacle avoidance. The stability of each controller in the control scheme is guaranteed by a Lyapunov function. Finally, several simulations are conducted to evaluate the proposed control scheme. The simulation results indicate that the proposed scheme can be applied to the mobile robot to ensure its safe movement in unknown obstacle environments.

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A New Approach for Controlling a Trajectory Tracking Using Intelligent Methods

Cited by 11 publications

References 8 publications

Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot

Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot

Design and Implementation of an Autonomous Electric Vehicle for Self-Driving Control under GNSS-Denied Environments

A Multi-Switching Tracking Control Scheme for Autonomous Mobile Robot in Unknown Obstacle Environments

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