A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot.
Sensors (Basel)
; 21(24)2021 Dec 13.
Article
in English
| MEDLINE | ID: covidwho-1596846
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Robotics
Type of study:
Prognostic study
Language:
English
Year:
2021
Document Type:
Article
Affiliation country:
S21248331
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