Skip to main content
AIP Conference ProceedingsVolume 2293, 24 November 2020, Article number 420032International Conference on Numerical Analysis and Applied Mathematics 2019, ICNAAM 2019; Sheraton Rhodes ResortRhodes; Greece; 23 September 2019 through 28 September 2019; Code 165330

Deceptive actions and demonstrations of intentions for robot collision avoidance(Conference Paper)(Open Access)

  Save all to author list
  • Department of Intelligent Systems and Robotics, Institute of Natural Sciences and Mathematics, Ural Federal University, Lenin st, 51, Ekaterinburg, 620083, Russian Federation

Abstract

Efficiency and effectiveness of many robotics tasks depend crucially on the quality of the solution of the problem of collision avoidance. In many cases, traditional path planning and obstacle avoiding approaches and algorithms can not guarantee a sufficiently high quality of the solution of the problem of collision avoidance for multi-robot and human-robot interactions. In this paper, we consider demonstrations of true intentions and demonstrations of deceptions for the problem of collision avoidance. © 2020 American Institute of Physics Inc.. All rights reserved.

Funding details

Funding sponsor Funding number Acronym
Ministry of Education and Science of the Russian FederationMinobrnauka
Government Council on Grants, Russian Federation
  • 1

    This work is partially supported by the Ministry of Education and Science of the Russian Federation project "Combinatorial models in computer science and their applications". The work was supported by Act 211 Government of the Russian Federation, contract N 02. A03.21.0006.

  • ISSN: 0094243X
  • ISBN: 978-073544025-8
  • Source Type: Conference Proceeding
  • Original language: English
  • DOI: 10.1063/5.0027660
  • Document Type: Conference Paper
  • Volume Editors: Simos T.E.,Simos T.E.,Simos T.E.,Simos T.E.,Simos T.E.,Tsitouras C.
  • Publisher: American Institute of Physics Inc.

  Popov, V.; Department of Intelligent Systems and Robotics, Institute of Natural Sciences and Mathematics, Ural Federal University, Lenin st, 51, Ekaterinburg, Russian Federation;
© Copyright 2020 Elsevier B.V., All rights reserved.

Cited by 0 documents

{"topic":{"name":"Robot; Human-Robot Interaction; Reinforcement Learning","id":7126,"uri":"Topic/7126","prominencePercentile":98.04735,"prominencePercentileString":"98.047","overallScholarlyOutput":0},"dig":"a071ee721e431c2c5c8d846c93489524fac4852034d147d848e3048bef6473f2"}

SciVal Topic Prominence

Topic:
Prominence percentile: