An Improved Algorithm for Complete Coverage Path Planning Based on Biologically Inspired Neural Network

Han, Linhui; Tan, Xiangquan; Wu, Qingwen; Xu, Dianguo

doi:10.1109/tcds.2023.3237612

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Closer to our work, (Han et al, 2023) presents a new CCPP strategy with biologically inspired neural network for cleaning robots. The planned path of cleaning robot takes into account the dynamic neural activities and the distribution of obstacles in the environmental map.…”

Section: Related Workmentioning

confidence: 95%

GBNN algorithm enhanced by movement planner for UV‐C disinfection

Rodrigo,

Sierra‐García,

Santos

2023

Expert Systems

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In order to maintain adequate levels of cleanliness and sanitation in public facilities, prevent the buildup of viruses and other harmful pathogens, and ensure health and safety, health and labor authorities have repeatedly warned of the need to adhere to proper disinfection protocols in the workplace. This is particularly important in public places where food is handled, where there are more vulnerable people, including hospitals and health care centers, or where there is a large concentration of people. One promising approach is the combination of ultraviolet‐C (UV‐C) light and mobile robots to automate disinfection processes. Being this technology effective for disinfection, an excessive dose of UV can damage the materials, limiting its applicability. Therefore, a major challenge for automatic disinfection is to find a route that covers the entire surface, ensures cleanliness, and provides the correct radiation dose while preventing environmental materials from being damaged. To achieve this, in this paper a novel intelligent control approach is proposed. A bio‐inspired Glasius neural network with a motion planner, an UV estimation module, a speed regulator, and pure pursuit controller are combined into one intelligent system. The motion planner proposes a sequence of movements to go through the space in the most efficient way possible, avoiding obstacles of the environment. The speed controller adjusts the dose of UV‐C radiation and the pure pursuit regulator ensures the following of the path. This approach has been tested in various simulation scenarios of increasing complexity and in four different areas of dosing requirements. In simulation, a 44% reduction of the maximum dose is achieved, 17% less distance travelled by the robot and, what is more important, 229% more locations with the appropriate dose.

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Section: Related Workmentioning

confidence: 95%

GBNN algorithm enhanced by movement planner for UV‐C disinfection

Rodrigo,

Sierra‐García,

Santos

2023

Expert Systems

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“…Developed [ 23 ] a cost-effective unmanned aerial vehicle (UAV) equipped with sensors like LIDAR and optics for inspecting concrete pavement structure cracks [ 7 , 8 ]. Utilizing the SVM algorithm for data classification, this UAV overcomes the challenges of real-time crack detection [ 9 , 10 , 14 , 15 , 17 , 18 , 19 ] within budget constraints. While the method’s computational efficiency is a major advantage for low-cost UAVs, the paper does not address how this efficiency might impact the accuracy or reliability of the inspection results [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Related Workmentioning

confidence: 99%

“…This size is a common choice for many pre-trained deep-learning models, including those used for image classification. Epochs (10): An epoch represents one complete pass through the entire training dataset during model training. In this case, the model will iterate over the whole dataset 10 times.…”

Section: Hyper Parameter Detailsmentioning

confidence: 99%

“…While the majority of damage is not considered a threat to the structural integrity, there are circumstances where severe cracks are able to impair it. Researchers have been primarily investigating automatic crack identification in concrete by employing image-based techniques such as thresholding and morphological methodologies, in addition to machine learning approaches like convolutional neural networks (CNNs) [ 5 , 6 , 7 , 8 , 9 , 10 ]. Despite this innovation, there exists a deficiency in grasping the impact of poor images on the detection of cracks, as well as the extent to which image preprocessing can alleviate these concerns.…”

Section: Introductionmentioning

confidence: 99%

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Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

Meftah,

Hu,

Asham

et al. 2024

Sensors

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Detecting road cracks is essential for inspecting and assessing the integrity of concrete pavement structures. Traditional image-based methods often require complex preprocessing to extract crack features, making them challenging when dealing with noisy concrete surfaces in diverse real-world scenarios, such as autonomous vehicle road detection. This study introduces an image-based crack detection approach that combines a Random Forest machine learning classifier with a deep convolutional neural network (CNN) to address these challenges. Three state-of-the-art models, namely MobileNet, InceptionV3, and Xception, were employed and trained using a dataset of 30,000 images to build an effective CNN. A systematic comparison of validation accuracy across various base learning rates identified a base learning rate of 0.001 as optimal, achieving a maximum validation accuracy of 99.97%. This optimal learning rate was then applied in the subsequent testing phase. The robustness and flexibility of the trained models were evaluated using 6,000 test photos, each with a resolution of 224 × 224 pixels, which were not part of the training or validation sets. The outstanding results, boasting a remarkable 99.95% accuracy, 99.95% precision, 99.94% recall, and a matching 99.94% F1 Score, unequivocally affirm the efficacy of the proposed technique in precisely identifying road fractures in photographs taken on real concrete surfaces.

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