Fixed-time trajectory tracking control for nonholonomic mobile robot based on visual servoing

Ou, Meiying; Sun, Haibin; Zhang, Zhenxing; Gu, Shengwei

doi:10.1007/s11071-021-07191-8

Cited by 25 publications

(11 citation statements)

References 35 publications

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“…A mobile robot is a kind of robot with high intelligence, and it is also the focus and frontier field of intelligent robot research. As an important group in the robot family, mobile robots have made many remarkable achievements in the past decades [5]. Developed countries such as Europe, America, and Japan have studied mobile robot technology earlier, and the mobile robot technology of these countries represents the highest level in the world to a certain extent [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Fan

2022

Journal of Sensors

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In order to improve the efficiency of a logistics cycle robot picking up goods, a path planning algorithm based on artificial intelligence was proposed. After analyzing the particle swarm optimization algorithm, the particle swarm optimization algorithm is optimized and improved and the path planning of a single robot is obtained. On this basis, a multipopulation particle swarm optimization (CMMPPSO) algorithm is proposed. The results show that the JMPOPSO algorithm is more accurate than the BPSO algorithm and the maximum fitness optimized by the BPSO algorithm is 1.59, while the maximum fitness optimized by the JMPOPSO algorithm is 1.98. The path optimized by the CMMPPSO algorithm based on JMPOPSO is better than that optimized by the CMMPPSO algorithm based on BPSO, shortening by about 25% and shortening the time by about 30. Simulation experiments verify the effectiveness of the CMMPPSO algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Fan

2022

Journal of Sensors

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“…Combining ( 14) and the kinematic model described in (4), one can obtain the error model for the trajectory tracking control as ṗe = F + Gz (18) in which…”

Section: Construction Of the Afntsmdcmentioning

confidence: 99%

“…Meanwhile, attempts have been made to apply SMC law in Cartesian coordinates [12][13][14][15][16][17][18]. Among those studies, cascaded control systems, namely inner-outer loop control structures [13][14][15], are adopted.…”

Section: Introductionmentioning

confidence: 99%

Finite-time tracking control for nonholonomic wheeled mobile robot using adaptive fast nonsingular terminal sliding mode

et al. 2022

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System uncertainties and external disturbances are the major causes of the trajectory tracking performance degradation in nonholonomic wheeled mobile robots (NWMRs). In this article, an adaptive fast nonsingular terminal sliding mode dynamic control (AFNTSMDC) method is proposed to provide enhanced robust and finite-time tracking performance for the NWMR. The proposed AFNTSMDC is a systematic design method based upon both the kinematic and dynamic model of the NWMR. The proposed controller has a simple form without singularity issue in the control input, which makes it practically implementable. The finite-time stability of the proposed tracking-error function is also proved using the Lyapunov function. Finally, circular trajectory tracking experiments are conducted to validate the robustness and convergence rate of the proposed AFNTSMDC scheme in comparison with the existing methods including classic kinematic control, robust sliding mode kinematic control, and conventional sliding mode dynamic control in the presence of uncertainties and external disturbances.

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“…They proposed a tracking error system between the mobile robot and the desired trajectory, enabling the robot to track the reference trajectory in a fixed time. However, this process is complicated and may result in inaccurate results [1]. Ma et al introduced the U-model method to relieve the requirement of dynamic mathematical model and simplify the design of the trajectory tracking controller of the manipulator.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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With the development and leap of artificial intelligence technology, more and more robots have penetrated into all walks of life. Today’s agriculture is changing in the direction of modernization and automation. On the one hand, because of rising labor costs, it cannot afford to consume a large amount of labor for agricultural operations. On the other hand, the population is growing rapidly, and traditional agricultural production, picking, and other links have been unable to keep pace with the development of the times. Therefore, it is very necessary to use artificial intelligence technology to transform traditional agriculture. The purpose of this paper is to use artificial intelligence technology to plan, track, and optimize the displacement trajectory of the agricultural picking robot, so as to improve the working efficiency of the picking robot. In this paper, the neural network, the D-H modeling method of the manipulator, and the forward and reverse motion of the manipulator are explained and analyzed, and based on the relevant algorithms of neural network, the manipulator is modeled, and then the, forward and reverse motion of the manipulator is analyzed in detail, and the digital model of the picking robot is constructed. Then, the angle and motion speed of each joint of the robot are analyzed to reduce the motion trajectory error caused by friction and other factors. Then, the simulation experiment of the displacement trajectory tracking control is carried out, and the linear trajectory motion and the arc trajectory motion are deeply analyzed, and the axis error is greatly reduced after 6 iterations. Finally, the displacement trajectory is optimized. The optimized total movement time is shortened by 6.84 seconds, which enables the picking robot to not only ensure work efficiency but also accurately complete the planned displacement trajectory. After continuous experiments on the algorithm model and the picking robot, the actual trajectory of the picking robot at 0.7 seconds can be expected. The trajectories are completely coincident, indicating that the neural network plays a very important role in the trajectory research of picking robots.

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Fixed-time trajectory tracking control for nonholonomic mobile robot based on visual servoing

Cited by 25 publications

References 35 publications

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Finite-time tracking control for nonholonomic wheeled mobile robot using adaptive fast nonsingular terminal sliding mode

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

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