Intelligent Deep Neuro-Fuzzy System of Abnormal Situation Recognition for Transport Systems

Ivanov, Yuriy; Zhiganov, S.V.; Ivanova, Tatyana

doi:10.1007/978-3-030-69421-0_24

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…In order to be able to estimate the energy consumption of the robot associated with the trajectory of movement, it is necessary to have the function E f(arguments). It is proposed to restore (identify) a non-linear dependence using well-established tools of neural networks (Martínez and Velásquez, 2011;Gordin et al, 2020;Ivanov et al, 2021), fuzzy logic (Voskoglou, 2022) or hybrid (neuro-fuzzy systems) (Marakhimov et al, 2018) in the automatic synthesis mode based on the training sample. To obtain a training sample, it was decided to perform a limited but sufficient number of movement trajectories, representing the set W and the loading zone (Figure 2B), having carried out all the necessary measurements.…”

Section: Methodsmentioning

confidence: 99%

“…At the same time, due to the constant correction of the nomenclature of stored objects towards its expansion, the issues of increasing the «productivity» of specialized warehouses are becoming more urgent. Qualitative indicators can be achieved by solving logistical problems inside a specialized room, as shown in (Ivanov et al, 2021), which can be achieved through the development of adequate mathematical models of processes and the use of modern automation tools, in particular, industrial robots (IR), sensor systems and intelligent control algorithms. Thus, the performance indicator of the removal, loading, reassembly subsystems, location changes, confirmation of the quality of objects and storage classes will be determined, among other things, at the stage of technological design processes at manufacturing enterprises, for example, the weight and size indicators of the combined cargo on a pallet, options for laying, packaging and fastening.…”

Section: Introductionmentioning

confidence: 99%

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Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Gorkavyy,

Egorova

et al. 2023

Front. Energy Res.

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Purpose of the work: Researching possibility of creating a method for improving the energy efficiency of differentiated robotic technological process (RTP) in the food industry. The high rates of development of production processes robotization, including in the food industry, leading to increase the cost of electrical energy, determine the research tasks relevance in finding energy-efficient methods for controlling industrial robots.Methodology: The proposed approach is based on principles of object-oriented design and possibility of classifying robotic technological process: Applying specific model sets and methods to improve energy efficiency to individual classes. The existing possibility of energy consumption synthesizing models of robots inside differentiated robotic technological process, such as stacking loads, in reduced form, made it possible to use neural network methods to identify non-linear dependencies. At the same time, training sample for intelligent modules was formed on the basis of classical experiment planning algorithms. The synthesis of methods, models and procedures was implemented on the basis of high-level programming languages C++, MATLAB.Results: A mathematical model and automated algorithms for its synthesis are proposed, which make it possible to adjust robotic technological process simulation model taking into account its specifics and implement a method for finding the optimal parameters of its functioning. To confirm the effectiveness of proposed solution, the obtained neural network model and optimization method were tested on real robotic technological process, and the calculation of economic efficiency of proposed solution was also given.Conclusion/recommendations: The application of this approach will significantly reduce energy costs for robotic operations in the food industry.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Gorkavyy,

Egorova

et al. 2023

Front. Energy Res.

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show abstract

“…Some other researchers have evaluated the generated collision avoidance trajectories from the perspective of risk assessment [ 11 ], using multiple parameters such as navigation risk [ 12 ], navigation smoothness [ 13 ], and other metrics. In [ 14 , 15 ], the authors discussed ASV(Autonomous Surface Vehicle) developments in depth, while conflict detection and obstacle avoidance received a lesser amount of attention. Only a few studies related to reacting collision avoidance for unmanned ships were included in the paper [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Risk-Sensitive Markov Decision Processes of USV Trajectory Planning with Time-Limited Budget

Ding,

Zhu

2023

Sensors

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Trajectory planning plays a crucial role in ensuring the safe navigation of ships, as it involves complex decision making influenced by various factors. This paper presents a heuristic algorithm, named the Markov decision process Heuristic Algorithm (MHA), for time-optimized avoidance of Unmanned Surface Vehicles (USVs) based on a Risk-Sensitive Markov decision process model. The proposed method utilizes the Risk-Sensitive Markov decision process model to generate a set of states within the USV collision avoidance search space. These states are determined based on the reachable locations and directions considering the time cost associated with the set of actions. By incorporating an enhanced reward function and a constraint time-dependent cost function, the USV can effectively plan practical motion paths that align with its actual time constraints. Experimental results demonstrate that the MHA algorithm enables decision makers to evaluate the trade-off between the budget and the probability of achieving the goal within the given budget. Moreover, the local stochastic optimization criterion assists the agent in selecting collision avoidance paths without significantly increasing the risk of collision.

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Intelligent Deep Neuro-Fuzzy System of Abnormal Situation Recognition for Transport Systems

Cited by 2 publications

References 8 publications

Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Risk-Sensitive Markov Decision Processes of USV Trajectory Planning with Time-Limited Budget

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