The Concept of Robotics Complex for Transporting Special Equipment to Emergency Zones and Evacuating Wounded People

Mamchenko, Mark; Ananyev, Pavel; Kontsevoy, Alexander; Plotnikova, Anna; Gromov, Yuri

doi:10.1007/978-981-15-5580-0_17

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Russia is going to use advanced robotics in its emergency operations like bringing medicine immediately to the affected area, shifting of the wounded person from the site, provision of tools and equipment in disaster situations, etc. [83]. A training data set for automated surgical operations by robots and methods for its propagation and sharing to other robotic platforms were discussed in [84] in order to develop the capability to perform emergency medical/surgical procedures using AI to tackle the highly emergent situations where availability of healthcare professionals risks lives.…”

Section: Cps Componentsmentioning

confidence: 99%

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Javed

Shahzad

Rehman

et al. 2022

Cities

314

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Section: Cps Componentsmentioning

confidence: 99%

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Javed

Shahzad

Rehman

et al. 2022

Cities

314

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“…Mobile robotics is an ever-growing field with a multitude of research focuses related to them [1]. Many applications of mobile robots can be seen today, such as transporting equipment [2], search and rescue [3], security [4], agriculture [5] and many others. One of the main issues of mobile robotics is the performance of its internal systems.…”

Section: Introductionmentioning

confidence: 99%

Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier

et al. 2023

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The navigation of mobile robots throughout the surrounding environment without collisions is one of the mandatory behaviors in the field of mobile robotics. The movement of the robot through its surrounding environment is achieved using sensors and a control system. The application of artificial intelligence could potentially predict the possible movement of a mobile robot if a robot encounters potential obstacles. The data used in this paper is obtained from a wall-following robot that navigates through the room following the wall in a clockwise direction with the use of 24 ultrasound sensors. The idea of this paper is to apply genetic programming symbolic classifier (GPSC) with random hyperparameter search and 5-fold cross-validation to investigate if these methods could classify the movement in the correct category (move forward, slight right turn, sharp right turn, and slight left turn) with high accuracy. Since the original dataset is imbalanced, oversampling methods (ADASYN, SMOTE, and BorderlineSMOTE) were applied to achieve the balance between class samples. These over-sampled dataset variations were used to train the GPSC algorithm with a random hyperparameter search and 5-fold cross-validation. The mean and standard deviation of accuracy (ACC), the area under the receiver operating characteristic (AUC), precision, recall, and F1−score values were used to measure the classification performance of the obtained symbolic expressions. The investigation showed that the best symbolic expressions were obtained on a dataset balanced with the BorderlineSMOTE method with ACC¯±SD(ACC), AUC¯macro±SD(AUC), Precision¯macro±SD(Precision), Recall¯macro±SD(Recall), and F1−score¯macro±SD(F1−score) equal to 0.975×1.81×10−3, 0.997±6.37×10−4, 0.975±1.82×10−3, 0.976±1.59×10−3, and 0.9785±1.74×10−3, respectively. The final test was to use the set of best symbolic expressions and apply them to the original dataset. In this case the ACC¯±SD(ACC), AUC¯±SD(AUC), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1−score¯±SD(F1−Score) are equal to 0.956±0.05, 0.9536±0.057, 0.9507±0.0275, 0.9809±0.01, 0.9698±0.00725, respectively. The results of the investigation showed that this simple, non-linearly separable classification task could be solved using the GPSC algorithm with high accuracy.

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Service robotics development trends in the Russian Federation

Kazak

Oleinikov

Тимиргалеева

et al. 2021

AIP Conference Proceedings

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The Concept of Robotics Complex for Transporting Special Equipment to Emergency Zones and Evacuating Wounded People

Cited by 4 publications

References 15 publications

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier

Service robotics development trends in the Russian Federation

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