Modeling and Trajectory Tracking Control for Magnetic Wheeled Mobile Robots Based on Improved Dual-Heuristic Dynamic Programming

Dian, Songyi; Fang, Hongwei; Zhao, Tao; Wu, Qing; Hu, Yi; Guo, Rui; Li, Shengchuan

doi:10.1109/tii.2020.2983841

Cited by 35 publications

(21 citation statements)

References 28 publications

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“…However, similar to other data forms, the three-dimensional contour obtained by depth information is also affected by factors such as flicker noise and object occlusion, which are likely to have a negative impact on the effect of motion recognition. erefore, to solve these problems, many robust feature extraction methods based on depth maps have been proposed [19,20].…”

Section: Human Motion Recognition Methods Based On Depth Mapmentioning

confidence: 99%

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Zuo¹

2021

Mathematical Problems in Engineering

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Human motion recognition has an important application value in scenarios such as intelligent monitoring and advanced human-computer interaction, and it is an important research direction in the field of computer vision. Traditional human motion recognition algorithms based on two-dimensional cameras are susceptible to changes in light intensity and texture. The advent of depth sensors, especially the Kinect series with good performance and low price released by Microsoft, enables extensive research based on depth information. However, to a large extent, the depth information has not overcome these problems based on two-dimensional images. This article introduces the research background and significance of human motion recognition technology based on depth information, introduces in detail the research methods of human motion recognition algorithms based on depth information at home and abroad, and analyzes their advantages and disadvantages. The public dataset is introduced. Then, based on the depth information, a method of human motion recognition is proposed and optimized. A moving human body image segmentation method based on an improved two-dimensional Otsu method is proposed to solve the problem of inaccurate and slow segmentation of moving human body images using the two-dimensional Otsu method. In the process of constructing the threshold recognition function, this algorithm not only uses the cohesion of the pixels within the class but also considers the maximum variance between the target class and the background class. Then, the quantum particle swarm algorithm is used to find the optimal threshold solution of the threshold recognition function. Finally, the optimal solution is used to achieve accurate and fast image segmentation, which increases the accuracy of human body motion tracking by more than 30%.

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Section: Human Motion Recognition Methods Based On Depth Mapmentioning

confidence: 99%

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Zuo¹

2021

Mathematical Problems in Engineering

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“…Dian et al [17] established a mathematical model of a magnetic wheeled mobile robot (MWMR) with an intelligent discrete algorithm for trajectory tracking control to realize precise motion control of the robot. Simulations results were compared with those of neural network (NN) tracking control algorithm.…”

Section: Related Research Workmentioning

confidence: 99%

“…This will improved the training process in terms of speeding up the training phase, and decreasing the probability of falling into local minimum during it. The normalization of the measurements of the sensors are described by (17).…”

mentioning

confidence: 99%

Smart element aware gate controller for intelligent wheeled robot navigation

Al-Jamali

Abdullah

2021

IJECE

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<p>The directing of a wheeled robot in an unknown moving environment with physical barriers is a difficult proposition. In particular, having an optimal or near-optimal path that avoids obstacles is a major challenge. In this paper, a modified neuro-controller mechanism is proposed for controlling the movement of an indoor mobile robot. The proposed mechanism is based on the design of a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. This controller is updated to overcome the rigid and dynamic barriers in the indoor area. The proposed controller is implemented with a mobile robot known as Khepera IV in a practical manner. The practical results demonstrate that the proposed mechanism is very efficient in terms of providing shortest distance to reach the goal with maximum velocity as compared with the MENN. Specifically, the MEEG is better than MENN in minimizing the error rate by 58.33%.</p>

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“…Sorour et al designed complementary route-based ICR control for steerable wheeled mobile robots [32]. However, the research on the trajectory tracking problems of wall-climbing mobile robots is still insufficient [33].…”

Section: Introductionmentioning

confidence: 99%

Design and Control of an Omnidirectional Mobile Wall-Climbing Robot

Zhong

Xiao

et al. 2021

Applied Sciences

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Omnidirectional mobile wall-climbing robots have better motion performance than traditional wall-climbing robots. However, there are still challenges in designing and controlling omnidirectional mobile wall-climbing robots, which can attach to non-ferromagnetic surfaces. In this paper, we design a novel wall-climbing robot, establish the robot’s dynamics model, and propose a nonlinear model predictive control (NMPC)-based trajectory tracking control algorithm. Compared against state-of-the-art, the contribution is threefold: First, the combination of three-wheeled omnidirectional locomotion and non-contact negative pressure air chamber adhesion achieves omnidirectional locomotion on non-ferromagnetic vertical surfaces. Second, the critical slip state has been employed as an acceleration constraint condition, which could improve the maximum linear acceleration and the angular acceleration by 164.71% and 22.07% on average, respectively. Last, an NMPC-based trajectory tracking control algorithm is proposed. According to the simulation experiment results, the tracking accuracy is higher than the traditional PID controller.

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Modeling and Trajectory Tracking Control for Magnetic Wheeled Mobile Robots Based on Improved Dual-Heuristic Dynamic Programming

Cited by 35 publications

References 28 publications

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Smart element aware gate controller for intelligent wheeled robot navigation

Design and Control of an Omnidirectional Mobile Wall-Climbing Robot

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