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Spatial Context Identification for an Autonomous Mobile Robot

Published: 25 August 2020 Publication History

Abstract

Autonomous mobile robots must be equipped with knowledge representation, and reasoning to solve decision problems and evolve in dynamic complex environments to ensure a natural interaction in the human environment. In a robotic interaction system, information has to be represented and processed at various levels of abstraction: from sensor up to actions and plans. Thus, knowledge representation provides the means to describe the environment with different abstraction that allows performing appropriate decisions. In this paper, we demonstrate a methodology for multimodal interaction between a robot and its environment. The framework manages interaction by representing knowledge and reasoning on it to draw inference. The interaction process involves fusion of actual values from different sensors in the environment, and the fission process that suggests a detailed set of actions to be implemented. Reasoning abilities are necessary to guarantee a global execution of the scenario. Reasoning techniques involving deterministic inferences and probabilistic inferences are used to manage uncertain knowledge by combining statistical relational models using Markov logic Networks. We apply these techniques to deal with partially observable environments and proposed a solution to solve the problem of incomplete knowledge in decision making. We validated it through a use-case scenario.

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cover image ACM Other conferences
CompSysTech '20: Proceedings of the 21st International Conference on Computer Systems and Technologies
June 2020
343 pages
ISBN:9781450377683
DOI:10.1145/3407982
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • UORB: University of Ruse, Bulgaria

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2020

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Author Tags

  1. Markov logic network
  2. Mobile autonomous robot
  3. first order logic
  4. fission
  5. fusion
  6. knowledge representation
  7. reasoning

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CompSysTech '20

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CompSysTech '20 Paper Acceptance Rate 46 of 72 submissions, 64%;
Overall Acceptance Rate 241 of 492 submissions, 49%

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