Skip to search boxSkip to navigationSkip to main content

Automation in handling uncertainty in semantic-knowledge based robotic task-planning by using Markov Logic Networks

  • Renxi Qiu
    ,
  • Ahmed Al-Moadhen
    ,
  • Michael Packianather
    ,
  • Rossi Setchi
  • University of Kerbala
    ,
  • Cardiff University
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

Generating plans in real world environments by mobile robot planner is a challenging task due to the uncertainty and environment dynamics. Therefore, task-planning should take in its consideration these issues when generating plans. Semantic knowledge domain has been proposed as a source of information for deriving implicit information and generating semantic plans. This paper extends the Semantic-Knowledge Based (SKB) plan generation to take into account the uncertainty in existing of objects, with their types and properties, and proposes a new approach to construct plans based on probabilistic values which are derived from Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inferencing together with semantic information to provide a basis for plausible learning and reasoning services in supporting of robot task-planning. In addition, an algorithm has been devised to construct MLN from semantic knowledge. By providing a means of modeling uncertainty in system architecture, task-planning serves as a supporting tool for robotic applications that can benefit from probabilistic inference within a semantic domain. This approach is illustrated using test scenarios run in a domestic environment using a mobile robot.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1023-1032

Journal (Volume, Issue Number)

Procedia Computer Science (Volume 35)

Publication milestones

  • Published - 13/09/2014

Publication status

Published - 13/09/2014

ISSN

1877-0509

External Publication IDs

  • handle.net: 10547/336080
  • Scopus: 84924198504

Access to documents

Publication metrics

Metrics

Download statistics
Download count
3