The Method of the Kinematic Structure Reconfiguration of a Multifunctional Modular Robot Based on the Greedy Algorithm

Петренко, В. И.; Pavlov, A. S.; Тебуева, Фариза; Gurchinsky, M. M.

doi:10.1109/dese.2019.00018

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…For the first type of movement, the trajectory along which the manipulation object should move is often known. There are a large number of methods for planning this type of movement [12][13][14][15][16][17][18]. The disadvantage of analytical methods for solving this problem [19] is the specialization in a strictly defined kinematic scheme of the anthropomorphic manipulator, as well as low energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for Anthropomorphic Manipulator Control System

Петренко¹,

Тебуева²,

Svistunov³

et al. 2020

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

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Service robots are one of the relevant areas of modern robotics. Many service robots are equipped with a pair of anthropomorphic manipulators, so that they are able to perform complex operations. However, this approach leads to new challenges in development of the robot control systems. In this paper we propose an algorithm for training the control system of two anthropomorphic manipulators with 7 degrees of mobility having intersecting work areas. The algorithm is based on deep reinforcement learning approach applied to the artificial neural network (ANN). The paper also describes the practical implementation of the ANN-based manipulator control system that avoids collisions and achieves an average accuracy of reproducing target positions of manipulator end effector of 98.3%. The ANN training was carried out using Keras framework. The obtained results indicate the promise of applying the proposed method for the development of control systems for anthropomorphic manipulators based on deep reinforcement learning.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for Anthropomorphic Manipulator Control System

Петренко¹,

Тебуева²,

Svistunov³

et al. 2020

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Currently, the huge interest of developers of robotic systems is attracted by the intensively developing unmanned technologies, the concept of which is to use robots to perform routine, harmful and hazardous types of work without direct human participation, which is the key to ensuring safety and high efficiency in solving tasks [1]. Active research is being carried out in the field of artificial intelligence and the development of multifunctional modular robots (MMR) [2][3][4][5][6][7]. MMRs are robotic systems consisting of many homogeneous or heterogeneous modules that interact with each other and exchange information to perform the target operation.…”

Section: Introductionmentioning

confidence: 99%

“…The combination of modules of the same type allows you to build mechanisms completely different in their structure. This provides significant advantages in comparison with classical mobile robots: higher reliability and overcoming obstacles of various complexity [7]. Modular robotics combines all the latest advances in robotics, mechatronics and control theory.…”

Section: Introductionmentioning

confidence: 99%

“…The control tasks of modular robots associated with the formation and reconfiguration of the kinematic structure are given in [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The listed works propose a methodological approach to the development of methods and algorithms for reconfiguration the structure of robots with a mechatronicmodular design.…”

Section: Introductionmentioning

confidence: 99%

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Method for Reconfiguring the Kinematic Structure of a Mechatronic-Modular Robot in Non-Deterministic Conditions

Петренко¹,

Тебуева²,

Pavlov³

et al. 2020

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

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Modular robots, consisting of many identical modules, are one of the most difficult areas of robotics. Each newly added element changes the shape and capabilities of the end device, for example, adds functionality or allows the robot to move in new planes. The reconfiguration of the kinematic structure is a sequence of movements of each robot module from the initial position of the initial configuration to the final position of the desired configuration. The paper considers a method for reconfiguration the kinematic structure of a mechatronic-modular robot using reinforcement learning. The proposed method will be built on the basis of a learning algorithm, where the information for training will be the actions taken and the "reward" is a value characterizing the quality of the robot's completion of the target task. The purpose of the training is to build a control algorithm that maximizes the total reward for a certain period of time. The effectiveness of the learning algorithm was tested by computer simulation of a robot, consisting of 5, 10 and 15 modules, in the formation of the target configuration.

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The Method of the Kinematic Structure Reconfiguration of a Multifunctional Modular Robot Based on the Greedy Algorithm

Cited by 2 publications

References 16 publications

Machine Learning Algorithm for Anthropomorphic Manipulator Control System

Machine Learning Algorithm for Anthropomorphic Manipulator Control System

Method for Reconfiguring the Kinematic Structure of a Mechatronic-Modular Robot in Non-Deterministic Conditions

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