High-Efficiency Automatic Recharging Mechanism for Cleaning Robot Using Multi-Sensor

Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset’s quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results.

Section: Introductionmentioning

confidence: 99%

Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications

Pathmakumar

Elara

Soundararajan

et al. 2022

“…Robot aided cleaning is a field that is advancing by leveraging cutting-edge technologies like artificial intelligence, sensing, energy-efficient systems, etc. [ 19 , 20 , 21 , 22 ]. An effort to bridge the research gap in estimating the quality of cleaning and formulating a strategy to execute it using robots can bring a solution to the paradox—“how clean is clean”.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

Pathmakumar

Kalimuthu

Elara

et al. 2021

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.

“…With the advent of the 21st century, autonomous robots have become an integral part of intelligent machines for performing various tasks including inspection, manufacturing, warehouse handling, and maintenance (cleaning, dusting and painting etc.). Among them, autonomous cleaning robots play a vital role in many day to day activities and also in industrial automation applications [ 1 , 2 ]. These robots have become the need of the day due to the large demand in household works, hospitals and industries.…”

Section: Introductionmentioning

confidence: 99%

sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot

Ramalingam

Mohan

Balakrishnan

et al. 2021

Staircase cleaning is a crucial and time-consuming task for maintenance of multistory apartments and commercial buildings. There are many commercially available autonomous cleaning robots in the market for building maintenance, but few of them are designed for staircase cleaning. A key challenge for automating staircase cleaning robots involves the design of Environmental Perception Systems (EPS), which assist the robot in determining and navigating staircases. This system also recognizes obstacles and debris for safe navigation and efficient cleaning while climbing the staircase. This work proposes an operational framework leveraging the vision based EPS for the modular re-configurable maintenance robot, called sTetro. The proposed system uses an SSD MobileNet real-time object detection model to recognize staircases, obstacles and debris. Furthermore, the model filters out false detection of staircases by fusion of depth information through the use of a MobileNet and SVM. The system uses a contour detection algorithm to localize the first step of the staircase and depth clustering scheme for obstacle and debris localization. The framework has been deployed on the sTetro robot using the Jetson Nano hardware from NVIDIA and tested with multistory staircases. The experimental results show that the entire framework takes an average of 310 ms to run and achieves an accuracy of 94.32% for staircase recognition tasks and 93.81% accuracy for obstacle and debris detection tasks during real operation of the robot.