Modelling and Trajectory Tracking of Wheeled Mobile Robots

Leena, N; Saju, K. K.

doi:10.1016/j.protcy.2016.05.094

Cited by 41 publications

(16 citation statements)

References 1 publication

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“…The geometry and kinematic parameters of the differential mobile robot is defined. The position/ orientation vector of the mobile robot and its speed are, respectively [1][2][3][4][5][6][7][8][28][29][30][31][32][33]:…”

Section: The Geometry and Kinematic Of A Mobile Robotmentioning

confidence: 99%

“…Such an error can be corrected using the FGS-PD controller. The PD control system using fuzzy gain scheduling is that the method is determined by fuzzy logic with gain scheduling to decide the parameters of PD control [28][29][30][31].…”

Section: The Design Of Fuzzy Gain Scheduling Of the Pd Controllermentioning

confidence: 99%

“…Therefore, such problems can be solved through the selection of a suitable curvature and application of a gradual fuzzy gain scheduling of PD control (FGS-PD) control process based on the relationship between the current location of the mobile robot and the target points. Moreover, by controlling the velocities of each of the two wheels attached to the mobile robot through a FGS-PD controller, it is possible to compensate for the slip phenomenon based on the inertial and centrifugal forces acting on the mobile robot as it moves, while minimizing the estimation errors [26][27][28][29][30][31][32][33]. For validating the proposed method, the functions are evaluated by conducting an experiment on the estimation of the sound sources moving in real time in two different situations (straight trajectory and S-curved trajectory) [1][2][3][4][5][6][7][8][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Han

2019

Electronics

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This paper proposes fuzzy gain scheduling of proportional differential control (FGS-PD) system for tracking mobile robot to moving sound sources. Given that the target positions of the real-time moving sound sources are dynamic, the mobile robots should be able to estimate the target points continuously. In such a case, the robots tend to slip owing to abnormal velocities and abrupt changes in the tracking path. The selection of an appropriate curvature along which the robot follows a sound source makes it possible to ensure that the robot reaches the target sound source precisely. For enabling the robot to recognize the sound sources in real time, three microphones are arranged in a straight formation. In addition, by applying the cross correlation algorithm to the time delay of arrival base, the received signals can be analyzed for estimating the relative positions and velocities of the mobile robot and the sound source. Even if the mobile robot is navigating along a curved path for tracking the sound source, there could be errors due to the inertial and centrifugal forces resulting from the motion of the mobile robot. Velocities of both robot wheels are controlled using FGS-PD control to compensate for slippage and to minimize tracking errors. For experimentally verifying the efficacy of the proposed the control system with sound source estimation, two mobile robots were fabricated. It was demonstrated that the proposed control method effectively reduces the tracking error of a mobile robot following a sound source.

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Section: The Geometry and Kinematic Of A Mobile Robotmentioning

confidence: 99%

Section: The Design Of Fuzzy Gain Scheduling Of the Pd Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Han

2019

Electronics

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“…Using the inverse kinematics of a differential-drive mobile robot (DDMR), the kinematic control law proposed by Martins et al [6] has been used in many research works [6][7][8][9][10][11]. However, almost all authors have focused on analyzing the research results related to the dynamic controller only when the parameters of the kinematic controller are constant.…”

Section: Introductionmentioning

confidence: 99%

Design of Adaptive Kinematic Controller Using Radial Basis Function Neural Network for Trajectory Tracking Control of Differential-Drive Mobile Robot

Khai

Ryoo

2019

IJFIS

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In this study, we propose an adaptive kinematic controller using a radial basis function neural network (RBFNN) to guide a differential-drive mobile robot (DDMR) during trajectory tracking. The kinematic controller is responsible for generating the reference values of the linear and angular velocities delivered to the robot actuators. With the fixed parameters of the kinematic controller, it is difficult for the robots to obtain acceptable performance. By using RBFNN and gradient descent method, the parameters of the kinematic controller can be updated online, thus providing smaller errors and better performance in applications. In this paper, the experimental results are presented to show the effectiveness of the proposed kinematic controller.

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“…For example, [54] proposed a trajectory tracking mechanism where geometrical constraints, impulsive force constraints, torque constraints, maximum acceleration and velocity constraints are considered. Another interesting study in this regard [55] compares different frameworks of trajectory tracking controllers for unmanned vehicles, where nonlinearity of the dynamics, uncertainties, noise, disturbances and several constraints are considered. The actuating machines usually used for robotic manipulators are DC motors.…”

Section: Introductionmentioning

confidence: 99%

Minimum-time path planning for robot manipulators using path parameter optimization with external force and frictions

Asl

2019

Teh. glas. (Online)

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This paper presents a new minimum-time trajectory planning method which consists of a desired path in the Cartesian space to a manipulator under external forces subject to the input voltage of the actuators. Firstly, the path is parametrized with an unknown parameter called a path parameter. This parameter is considered a function of time and an unknown parameter vector for optimization. Secondly, the optimization problem is converted into a regular parameter optimization problem, subject to the equations of motion and limitations in angular velocity, angular acceleration, angular jerk, input torques of actuators’, input voltage and final time, respectively. In the presented algorithm, the final time of the task is divided into known partitions, and the final time is an additional unknown variable in the optimization problem. The algorithm attempts to minimize the final time by optimizing the path parameter, thus it is parametrized as a polynomial of time with some unknown parameters. The algorithm can have a smooth input voltage in an allowable range; then all motion parameters and the jerk will remain smooth. Finally, the simulation study shows that the presented approach is efficient in the trajectory planning for a manipulator that wants to follow a Cartesian path. In simulations, the constraints are respected, and all motion variables and path parameters remain smooth.

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Modelling and Trajectory Tracking of Wheeled Mobile Robots

Cited by 41 publications

References 1 publication

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Design of Adaptive Kinematic Controller Using Radial Basis Function Neural Network for Trajectory Tracking Control of Differential-Drive Mobile Robot

Minimum-time path planning for robot manipulators using path parameter optimization with external force and frictions

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