skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

Sensors (Basel, Switzerland), 2021-12, Vol.21 (24), p.8331 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21248331 ;PMID: 34960425

Full text available

Citations Cited by
  • Title:
    A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot
  • Author: Pathmakumar, Thejus ; Elara, Mohan Rajesh ; Gómez, Braulio Félix ; Ramalingam, Balakrishnan
  • Subjects: Algorithms ; audit robot ; Auditing procedures ; Audits ; Automation ; Cleaning ; cleaning-auditing ; COVID-19 ; Deep learning ; Dirt ; Evacuations & rescues ; Neural networks ; path planning ; Performance evaluation ; Pharmaceutical industry ; Planning ; Quality assessment ; reinforcement learning ; Robotics ; Robots ; Sensors ; Teaching methods ; Waypoints
  • Is Part Of: Sensors (Basel, Switzerland), 2021-12, Vol.21 (24), p.8331
  • Description: Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of "How clean is clean" is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s21248331
    PMID: 34960425
  • Source: GFMER Free Medical Journals
    MEDLINE
    PubMed Central
    Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait