ArticleSensors (Basel, Switzerland)2021
An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation.Sensors (Basel, Switzerland) · 2023Article
- A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms.Sensors (Basel, Switzerland) · 2022Article
- Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications.Sensors (Basel, Switzerland) · 2022Article
- A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot.Sensors (Basel, Switzerland) · 2021Article
- Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor.Scientific reports · 2021Article
- Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework.Sensors (Basel, Switzerland) · 2021Article
- sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of "How clean is clean" by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the "touch and inspect" analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.