Remote control system based on the Internet and machine vision for tracked vehicles

Wang, Shuai; Zhang, Shubing; Ma, Ruoding; Erdong, Jin; Liu, Xinhui; Tian, He; Yang, Ruipeng

doi:10.1007/s12206-018-0236-3

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…The process is mainly divided into two parts, namely, forward propagation and back propagation. Forward propagation means that after the input layer receives the signal, it calculates the unit value and assigns it to the hidden layer [ 10 ]. If it matches, the learning is complete, if it does not match, then backpropagation is performed.…”

Section: Methods Of Machine Vision and Intelligent Algorithms Based O...mentioning

confidence: 99%

“…Computational Intelligence and Neuroscience to the hidden layer [10]. If it matches, the learning is complete, if it does not match, then backpropagation is performed.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

“…For the feature map whose input size is a × b and the pooling area is m × m, and the step size is d, the output feature map size is a′ × b′. Among them, the values of a′ and b' are determined by formulas (10) and (11).…”

Section: Cnnmentioning

confidence: 99%

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Machine Vision and Intelligent Algorithm Based on Neural Network

Sun

2022

Computational Intelligence and Neuroscience

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Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm.

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Section: Methods Of Machine Vision and Intelligent Algorithms Based O...mentioning

confidence: 99%

“…Computational Intelligence and Neuroscience to the hidden layer [10]. If it matches, the learning is complete, if it does not match, then backpropagation is performed.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

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Machine Vision and Intelligent Algorithm Based on Neural Network

Sun

2022

Computational Intelligence and Neuroscience

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“…Based on the skid dynamics model, Hong et al [ 11 ] proposed a path-tracking control algorithm for underwater tracked vehicles that takes into account the soil–track interaction. Wang et al [ 12 ] designed a path-tracking controller tracked vehicle based on visual navigation and a fuzzy Proportional–Integral–Derivative (PID) algorithm. In this work, the accuracy of tracking control is not very satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Research on Trajectory-Tracking Control System of Tracked Wall-Climbing Robots

Zhang,

Wu,

et al. 2023

Sensors

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Different from the vehicles and robots that move on the ground, complex and nonlinear track–wall interactions bring considerable difficulties to the accurate control of tracked wall-climbing robots due to the effect of gravity and adsorption. In this article, the authors propose a trajectory-tracking control system for tracked wall-climbing robots based on the fuzzy logic computed-torque control (FLCT) method. A key element in the proposed control strategy is to consider the adsorption force and gravity compensation based on the dynamic model. Validated via numerical simulations and experiments, the results show that the proposed controller can track the reference trajectory quickly, accurately and stably.

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“…rough the device's radio frequency identi cation, sensors, two-dimensional code, etc., it is connected to the wireless network through the interface, thereby giving the object "intelligence" [11]. To enable communication and dialogue between people and things, as well as between organizations, the network that links organizations is known as the " IoT."…”

Section: Introductionmentioning

confidence: 99%

Image Self-Coding Algorithm Based on IoT Perception Layer

2022

Mobile Information Systems

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In fact, with the quick growth of IoT-related industries in recent years, multimedia contents such as digital image videos have also shown explosive growth. In the sensing layer of the three-layer IoT architecture, sensors are the most critical part, which mainly sense the state of the environment. In this paper, an image self-coding algorithm based on the IoT perception layer is proposed. There is no specific encoding algorithm for the pictures collected by the current Internet of Things network perception layer. This results in poor search results for the network images collected by the sensor at the perception layer. A deep convolutional neural network image self-coding algorithm based on the IoT perception layer combines prior knowledge with deep involutional aural networks to increase the discriminative ability of images while preserving the message of the images themselves and improving the goal of image search accuracy. The experimental results show that the block search algorithm is used for image registration to reduce energy consumption, the absolute difference sum algorithm is improved to improve the accuracy of image registration, and the progressively out weighted average algorithm is used to stitch the images. After image stitching, the communication volume with the base station is reduced, which can effectively reduce the network load by 60%.

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Remote control system based on the Internet and machine vision for tracked vehicles

Cited by 13 publications

References 19 publications

Machine Vision and Intelligent Algorithm Based on Neural Network

Machine Vision and Intelligent Algorithm Based on Neural Network

Research on Trajectory-Tracking Control System of Tracked Wall-Climbing Robots

Image Self-Coding Algorithm Based on IoT Perception Layer

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