Intelligent Technologies Integration in the Task of Unaccented Trajectories Search in Robotics

Shakhmametova, G. R.; Yusupova, Nafisa

doi:10.1016/j.ifacol.2018.11.270

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…CPS issues also affect related information technologies: management systems, data analysis, design, the Internet of things (IoT) and network connectivity. The development of CPS stimulates innovation in a number of areas with high reliability requirements: aeronautics, energy, healthcare, transport and others [4][5][6][7].…”

Section: State Of the Artmentioning

confidence: 99%

Cyber-Physical Systems and Reliability Issues

Yusupova¹,

Rizvanov²,

Andrushko³

2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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Anomaly detection is a well researched concept used in many areas, including engineering systems design, where it helps detect errors and prevent failures. Traditional anomaly detection methods, based either on comparing the behavior of the real system with its model or on different signal processing methods, have been successfully applied for Fault Detection and Isolation (FDI) in mechatronic systems. Cyber-Physical Systems (CPS) are complex in both structural and behavioral terms. They consist of numerous heterogeneous components that generate large volumes of data, exchange information and form extremely complex patterns of behaviour. This makes it almost impossible to effectively set up and apply classical reliability assessment methods. The article discusses the basic models of machine learning and the possibility of their application to solve the problem of CPS reliability. It is proposed to use neural networks with long short-term memory (LSTM) to detect anomalies in the CPS. The neural network architecture with 3 hidden, input and output layers was designed. The experiment with testing data (Tennessee Eastman Process dataset) was conducted and analysis of the results was carried out.

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Section: State Of the Artmentioning

confidence: 99%

Cyber-Physical Systems and Reliability Issues

Yusupova¹,

Rizvanov²,

Andrushko³

2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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“…For the effective implementation of the assigned tasks, such RS must have a high degree of autonomy, which, in turn, leads to the emergence of a number of specific management problems. Based on the analysis of research works [1][2][3][4][5], one of the key problems arising during the operation of mobile robots (MR) in an unknown environment is the planning of safe trajectories of movement, allowing to build accident-free routes close to optimal (shortest).…”

Section: Introductionmentioning

confidence: 99%

A method for planning the trajectory of a mobile robot in an unknown environment with obstacles

2021

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The known algorithms for planning the trajectory of movement of mobile robots in an unknown environment have high computational complexity or do not allow finding the trajectory that is optimal along the length of the path, while maintaining a safe distance from obstacles. The aim of the work is to increase the efficiency of solving the problem of planning the trajectory of movement of mobile robots from the initial position to the final position in an unknown environment with obstacles, taking into account the limited capabilities (sensory and computational) of mobile robots. The solution to this problem was carried out on the basis of step-by-step optimization of the current position of the robot relative to a given target. The proposed method analyzes the possibility of a robot moving in directions determined by means of analytical geometry based on measurements of on-board distance sensors. An element of scientific novelty is the procedure for calculating trajectory segments based on the choice of an intermediate state and correcting the trajectory taking into account the measurements of the on-board distance sensors of the mobile robot. The proposed method makes it possible to search for the trajectory of a mobile robot in an unknown environment while ensuring a given distance to obstacles. The use of the presented algorithm allows the robot to maintain a high efficiency of the task while functioning in conditions of information deficiency. The reliability of the results was confirmed in the course of software simulation. The solution of the problem, taking into account these features, made it possible to reduce the computational complexity of the method, as well as to remove restrictions on the use of trajectory planning algorithms for mobile robots with low-performance on-board sensors and computing devices. The presented algorithm is implemented in the form of software in the Python programming language, which can be used to simulate autonomous control systems for mobile robots.

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“…Mobile manipulation robotic complexes belong to the category of service robots that can be used in the domestic sphere, when performing emergency rescue operations and research missions. Examples of the use of service robots are harvesting in agriculture, delivering food and reception of orders at catering establishments, caring for sick and disabled people, cleaning rooms, performing emergency and rescue operations (to monitor the emergency zone and eliminate the consequences of emergency situations), moon exploration and Mars, demining, military operations (fire support, delivery of supplies and medicines, transportation of the wounded, reconnaissance), diagnostics and maintenance of pipelines [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Robotic Complex Control Method Based on Deep Reinforcement Learning of Recurrent Neural Networks for Automatic Harvesting of Greenhouse Crops

Петренко¹,

Тебуева²,

Antonov³

et al. 2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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The modern development of technology determines the feasibility of the transition in agriculture from manual labor to automatic production. One of the promising areas is the automation of growing vegetable crops in greenhouse complexes. Necessary factors for intensive plant growth and unfavorable for human health, such as high temperature and humidity, as well as an atmosphere saturated with chemicals, make the task of robotizing agricultural operations urgent in this area. The method for controlling a robotic complex for automatic fruit collection in greenhouse complexes is proposed. Work in greenhouse complexes is characterized as non-deterministic and with partial observability of the environment; therefore, the deep recurrent neural network DRQN was used as the basis for the method of controlling the robotic complex. Deep learning with reinforcement was used for optimizing its weights. The presented simulation results demonstrate the efficiency of the proposed method and the need for its further development.

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Intelligent Technologies Integration in the Task of Unaccented Trajectories Search in Robotics

Cited by 6 publications

References 16 publications

Cyber-Physical Systems and Reliability Issues

Cyber-Physical Systems and Reliability Issues

A method for planning the trajectory of a mobile robot in an unknown environment with obstacles

A Robotic Complex Control Method Based on Deep Reinforcement Learning of Recurrent Neural Networks for Automatic Harvesting of Greenhouse Crops

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