Safe control of SEMS in group interaction

Author:

Gorodetskiy A. E.1ORCID,Kurbanov V. G.2ORCID,Tarasova I. L.3ORCID

Affiliation:

1. Institute of Problems of Mechanical Engineering of RAS

2. Institute of Problems of Mechanical Engineering of RAS; Saint-Petersburg State University of Aerospace Instrumentation

3. Institute of Problems of Mechanical Engineering of RAS; Peter the Great St. Petersburg Polytechnic University

Abstract

Introduction:In control over a group of interacting smart electromechanical systems (SEMS), situations may arise when the operator’s instructions and/or the automatic control system at a higher level contradict the internal state of the controlled SEMS and/or the environment of choice. Such situations can be prevented by algorithms which check the fulfillment of conditions for the admissibility of movements. These algorithms can be based on modeling the SEMS behavior using logical-probabilistic or logicallinguistic descriptions of situations, and on non-scalar quality criteria when making decisions.Purpose:The development of algorithms for safe control over robots based on SEMS modules with phase constraints, under incomplete certainty of the environment.Results:Algorithms have been developed for safe control over three robots, using a mathematical description of situational control over a group of SEMS and the methodology of organizing the situational control over a group of mobile SEMS. The algorithms move the robots from certain current positions to specified terminal positions, avoiding their collisions with each other. In order to avoid collisions, the decision-making system in a robot’s central nervous system uses robot’s priorities based on the distance between the robots. An approach has been proposed to overcome uncertainty on the way (trajectory) of the robots. Uncertainties in the form of logical-probabilistic and logical-linguistic type constraints are considered. It is shown that these restrictions can be translated into a logical-interval form. This allows you to use standard mathematical programming procedures when searching for the optimal solution.Practical relevance:The obtained algorithms can be used for decision-making in the central nervous system and when controlling robots.

Publisher

State University of Aerospace Instrumentation (SUAI)

Subject

Control and Optimization,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Reference23 articles.

1. Smart electromechanical systems/Studies in systems, decision and control. Vol. 49. Ed. Andrey E. Gorodetskiy. Springer International Publishing, 2016. 277 p. doi:10.1007/978-3-319-27547-5

2. Smart electromechanical systems: The central nervous system / Studies in systems, decision and control. Vol. 95. Eds. Andrey E. Gorodetskiy, Vugar G. Kurbanov. Springer International Publishing, 2017. 270 p. doi:10.1007/978-3-31953327-8

3. Gorodetskiy A. E., Kurbanov V. G., Tarasova I. L. Decision-making in central nervous system of a robot. Informatsionno-upravliaiushchie sistemy [Information and Control Systems], 2018, no. 1, pp. 21–30 (In Russian). doi:org/10.15217/issnl684-8853.2018.1.21

4. Sakharov V. V., Chertkov A. A., Tormashev D. S. Algorithm for optimal planning of group interaction of robots. Morskoy vestnik, 2014, no. 4(52), pp. 119–122 (In Russian).

5. Vasilyev I. A., Polovko S. A., Smirnova E. Yu. Organization of group control of mobile robots for the tasks of special robotics. Nauchno-tekhnicheskie vedomosti SPbGPU, 2013, no. 1, pp. 119–123 (In Russian).

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