Trajectory tracking of wheeled mobile robots using only Cartesian position measurements

Pliego‐Jiménez, Javier; Martínez-Clark, Rigoberto; Cruz-Hernández, C.; Arellano-Delgado, Adrian

doi:10.1016/j.automatica.2021.109756

Cited by 28 publications

(17 citation statements)

References 17 publications

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“…From the perspective of using network structural parameters, the training process of BPNN is composed of the sum of the mean square error of each output node, and the neural network obtained in this way can be regarded as a compromise performance of different tasks 12 . It is considered that the sequential circuit can remember information briefly because it stores data through various self-feedback loops, thus generating the sequential function.…”

Section: Methodology 21 Environmental Modeling and Information Inputmentioning

confidence: 99%

Robot automatic path-finding algorithm based on computer vision and neural network model

Yang

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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Section: Methodology 21 Environmental Modeling and Information Inputmentioning

confidence: 99%

Robot automatic path-finding algorithm based on computer vision and neural network model

Yang

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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“…Te parameters and formulas for the proposed models, as shown in Figures 3 and 4, are summarized in Table 2 and labelled from equations ( 14) to (22).…”

Section: Te Agv Kinematic Modelmentioning

confidence: 99%

“…Te controller controls the tracking trajectory by minimizing the position and angle errors. Te report shows that the proposed controller can reduce the error faster than the traditional Computational Intelligence and Neuroscience PID controller [22]. Zhou et al and colleagues designed a 16wheel hard AGV system equipped with magnetic tape navigation to track the line.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms

Moshayedi

Sina

et al. 2022

Computational Intelligence and Neuroscience

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Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group’s use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model’s ability for the researchers in robotic research.

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“…The tracking and stabilization problems of the nonholonomic mobile robot systems for most admissible reference trajectories without switching were addressed by the Lyapunov method in Wang et al [21]. A dynamic feedback linearization controller in combination with attitude and velocity observers was proposed to solve the tracking control problem of the wheeled mobile robot system in Pliego-Jimnez et al [22]. The tracking problem for a class of non-holonomic systems in chained form was addressed with the aid of a recursive technique in [23].…”

Section: Introductionmentioning

confidence: 99%

A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties

Yu,

Wu,

Yang

et al. 2023

Asian Journal of Control

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The tracking control problem of non‐holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed‐point tracking. This technical note focuses on the region tracking control for a non‐holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non‐holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non‐holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed‐point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed‐point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.

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Trajectory tracking of wheeled mobile robots using only Cartesian position measurements

Cited by 28 publications

References 17 publications

Robot automatic path-finding algorithm based on computer vision and neural network model

Robot automatic path-finding algorithm based on computer vision and neural network model

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms

A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties

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