Floor Cleaning Robot Research Articles

Recent developments in autonomous floor-cleaning robots: a review

Purpose Autonomous floor-cleaning robots (AFCRs) have become increasingly popular due to their ability to provide efficient and effective cleaning without the need for human intervention. These robots can perform various cleaning tasks, such as vacuum cleaning, mopping, scrubbing or sweeping, in domestic or industrial setups. As the use of floor-cleaning robots continues to grow, this paper aims to document key technological advancements. Design/methodology/approach The structure of the present work relies on published research articles excavated from general online research databases such as Google Scholar, Web of Science and Scopus. The authors use a variety of keywords and titles to search for research papers. Finally, 93 research articles are selected for review based on abstracts and key results that match AFCRs. Findings According to market trends, floor-cleaning robots dominate other cleaning areas. This review mainly focuses on five attributes of floor-cleaning robots: design and development of AFCR, complete coverage path planning, the application of machine learning (ML)/deep learning (DL), optimisation strategies for qualitative output and ethnographic studies. It also consists of discussions based on the results of reported technical works. Hence, AFCRs have dominated the market in the past decade and are likely to be more aggressive in the coming years. Originality/value To the best of the authors’ knowledge, only a survey article based on US-granted patents published in 2013 constitutes a review work in the research domain on AFCRs. In 2021, another review conducted a survey on the latest technological advancements in window-cleaning robots. It reviewed in detail the locomotion aspects, control mechanisms, adhesion mechanisms, sensors and actuators required for window-cleaning robots. In 2019, a comprehensive review was published on cleaning robots from a control strategy perspective for domestic applications. Therefore, the authors have crafted this review to understand the evolution of floor-cleaning robots in the past decade.

Solar Powered Floor Cleaning Robot

Solar-powered floor cleaning robots are an innovative combination of robotics and renewable energy technology designed to provide long-term and efficient solutions for automated floor cleaning activities. These robots use solar panels to collect solar energy, which is utilized to power their operation and charge onboard batteries, allowing for autonomous and ecologically responsible cleaning tasks. Solar-powered floor cleaning robots are outfitted with cleaning mechanisms, sensors, navigation systems, and complex control algorithms to roam indoor and outdoor settings independently, completing cleaning chores while reducing energy consumption and environmental effect. These solar-powered robots provide longer operating times, less dependency on grid electricity, and increased sustainability, making them appropriate for a variety of home, commercial, and industrial applications. This abstract investigates the design, functioning, advantages, and prospective applications of solar-powered flooring.

Solar Based Floor Cleaning Robot Using IOT

This project introduces a new type of robot, Solar-Powered Autonomous Floor Cleaning Robot in response to the increasing demand of sustainable and energy-efficient solutions in robotics. The robot is designed to move on its own indoors, outdoors and clean different types of floors while getting power from solar panels that are integrated into its body. By using this method, it tries to reduce the impact on the environment that comes with conventional cleaning methods as well as increase the efficiency of floor maintenance generally.The primary objective of the proposed robot is to address the challenge of maintaining optimal efficiency in solar energy harvesting by ensuring the cleanliness of solar panels in industrial environments. The robot incorporates autonomous navigation and cleaning capabilities, employing advanced sensors and artificial intelligence algorithms to detect and navigate around obstacles while efficiently cleaning the solar panel surfaces.The robot's design emphasizes modularity and scalability, allowing it to adapt to diverse industrial environments and solar panel configurations. Equipped with cleaning brushes and a water-efficient system, the robot ensures thorough cleaning without causing damage to the solar panels. Additionally, the integration of real-time monitoring capabilities enables remote tracking of cleaning operations and assessment of overall system performance.

Creating a Multi-Purpose Floor Cleaning Robot

Abstract: Traditional floor cleaners which rely on electricity; find widespread use in areas such as airports, railway platforms, hospitals, bus shelters, shopping malls, and various commercial spaces. These devices rely on electricity for operation, posing limitations on their usage. In regions like India, where power crises are prevalent, especially during summer, the effectiveness of floor cleaners is significantly hindered, particularly in bus stands. Therefore, there arises a need to develop an affordable and user-friendly battery-operated floor cleaner. This paper tries to create a multifunctional floor cleaning machine as an alternative to conventional electric models. The work includes modeling and analyzing the floor cleaner using relevant software.

Design and Development of Multifunction Floor Cleaning Robot

Abstract— In response to the pressing challenges posed by the high cost of labour, time, and effort in developing countries, there has been a longstanding reliance on Multifunction Floor Cleaning Robot. Recognizing the need for more affordable and sustainable solutions, our innovative project focuses on the design, development, and manufacturing of a multifunction floor cleaning robot. This robot operates efficiently on battery, or electricity, providing flexibility and accessibility in diverse environments. The key objectives of this paper revolve around minimizing energy consumption, reducing both robot and operational costs, alleviating human effort, promoting environmental sustainability, and ensuring ease of handling. This not only contributes to an eco-friendlier approach but also facilitates the feasibility of constructing the robot on a commercial scale in the future. Keywords— regenerative braking, BLDC motor, inverter, battery.

Staircase Recognition and Localization Using Convolutional Neural Network (CNN) for Cleaning Robot Application

Floor-cleaning robots are primarily designed to clean on a single floor, while multi-floor environments are usually not considered target applications. However, it is more efficient to have an autonomous floor-cleaning robot that can climb stairs and reach the next floors in a multi-floor building. To operate in such environments, the ability of a mobile robot to autonomously traverse staircases is very important. For this operation, staircase detection and localization are essential components for planning the traversal route on staircases. This article describes a deep learning approach using a convolutional neural network (CNN)-based robot operation system (ROS) framework for staircase detection, localization, and maneuvering of the robot to the detected stair. We present a real-time object detection framework to detect staircases in incoming images. We also localize these staircases using a contour detection algorithm to detect the target point: a point close to the center of the first step, and an angle of approach to the target point with respect to the current location of the robot. Experiments are performed with data from images captured on different types of staircases at different viewpoints/angles. The experimental results show that the presented approach can achieve an accuracy of 95% and a recall of 86.81%. A total runtime of 155 ms is taken to identify the presence of a staircase and the detection of the first step in the working environment, as well as being able to locate the target point with an accuracy of ±2 cm, ±1 degree.

Vision-based dirt distribution mapping using deep learning

Cleaning is a fundamental routine task in human life that is now handed over to leading-edge technologies such as robotics and artificial intelligence. Various floor-cleaning robots have been developed with different cleaning functionalities, such as vacuuming and scrubbing. However, failures can occur when a robot tries to clean an incompatible dirt type. These situations will not only reduce the efficiency of the robot but also impose severe damage to the robots. Therefore, developing effective methods to classify the cleaning tasks performed in different regions and assign them to the respective cleaning agent has become a trending research domain. This article proposes a vision-based system that employs YOLOv5 and DeepSORT algorithms to detect and classify dirt to create a dirt distribution map that indicates the regions to be assigned for different cleaning requirements. This map would be useful for a collaborative cleaning framework for deploying each cleaning robot to its respective region to achieve an uninterrupted and energy-efficient operation. The proposed method can be executed with any mobile robot and on any surface and dirt, achieving high accuracy of 81.0%, for dirt indication in the dirt distribution map.

Fuzzy Floor Dust Cleaning Robot Prototype Based On Arduino

The design of a Floor Cleaning Robot Based on Arduino Uno R3 aims to make it easier for humans to clean the dust on the floor efficiently. This robot consists of several series of components including Arduino Uno r3: Arduino Uno r3, sensor ultrasonic, motor servo, This robot consists of several components including Arduino Uno R3, ultrasonic sensor, servo motor, motor shield driver, dc motor, and vacuum blower motor. The entire system synergizes in the process of cleaning the dust on the floor and all of them are connected to a power source in the form of a dc battery, which supplies a voltage of 7 volts for the Arduino circuit and 12 volts for the vacuum blower. The working principle of this robot starts when the ultrasonic sensor can measure and distinguish the closest distance between the robot and an obstacle, at the same time the servo motor will move up to 180° to assist the ultrasonic sensor in detecting obstacles, both the front, right, and left sides of the robot. This Floor Cleaning Robot is programmed by adapting fuzzy logic artificial intelligence, the fuzzy rules used in this robot aim to control the speed of the robot based on the obstacle distance detected by the ultrasonic sensor. Fuzzy logic executes the data and continues the command to drive the motor so that the robot can work efficiently cleaning dust on the floor by minimizing the occurrence of collisions against obstacles. Dust suction carried out by the vacuum blower motor is not included in the Arduino circuit, because the vacuum blower motor requires an input power of 12 volts. It is not possible to unite it to the Arduino circuit because the power it has is only 7 volts, but these two separate circuits can still synergize well in the process of cleaning the dust on the floor. The results of observations and experiments show that the fuzzy logic embedded in the Arduino as the brain of the dust-cleaning robot on the floor works quite well.

Microphone-Based Context Awareness and Coverage Planner for a Service Robot Using Deep Learning Techniques

Floor-cleaning robots are becoming popular and operating in public places to ensure the places are clean and tidy. These robots are often operated in a dynamic environment that is less safe and has a high probability of ending up in accidents. Sound event-based context detection is expected to overcome drawbacks in a robot’s visual sensing to avoid a hazardous environment, especially in improper illumination and occlusion situations. Even though numerous studies in the literature discuss the benefits of sound-based context detection, there is no work reported related to context avoidance for cleaning robots. To this end, we propose a novel context avoidance framework based on a deep-learning method that can detect and classify a specific sound and localize the source from a robot’s frame to avoid that environment. The proposed model receives the spectrogram from the array of microphones as the input and produces two parallel outputs. The first output provides information about the spectrum class after running the classification task. The second output contains the localization message of the identified sound source. With the identity of the location that needs to be avoided, the proposed module will generate an alternative trajectory. The proposed model is evaluated in two real-world scenarios, wherein the model is trained to detect the escalator sound in the robot’s surroundings and avoid its location. In all considered scenarios, the developed system accomplished a significantly higher success rate in detecting and avoiding the escalator.

Leveraging Expert Demonstration Features for Deep Reinforcement Learning in Floor Cleaning Robot Navigation.

In this paper, a Deep Reinforcement Learning (DRL)-based approach for learning mobile cleaning robot navigation commands that leverage experience from expert demonstrations is presented. First, expert demonstrations of robot motion trajectories in simulation in the cleaning robot domain are collected. The relevant motion features with regard to the distance to obstacles and the heading difference towards the navigation goal are extracted. Each feature weight is optimized with respect to the collected data, and the obtained values are assumed as representing the optimal motion of the expert navigation. A reward function is created based on the feature values to train a policy with semi-supervised DRL, where an immediate reward is calculated based on the closeness to the expert navigation. The presented results show the viability of this approach with regard to robot navigation as well as the reduced training time.

Development of Autonomous Indoor Floor Cleaning Robot

- Cleanliness plays an important role in everyday life. It is the most popular method of maintaining our surroundings free of contaminants and illnesses, as well as for social and academic well-being. Recently, a few cleaning-arrangements have become available to maintain your home dust-free. Machine-Controlled For the betterment of humanity, floor cleaners were given. In recent years, cleanliness has been a critical aspect in improving one's own health, so we are developing a robotic Automated floor cleaner that would safely clean the premises. Expert support robots, such as client care and indoor robots, account for a large portion of these robots. One of these robots' most basic requirements is the ability to explore in environments where a GPS system cannot be used. The robot should also be able to detect barriers and quickly reroute to reach the desired destination. While getting at the goal, the robot will also be cleaning the floor simultaneously and independently. The robot depends on Robot Operating System (ROS) structure. The SLAM (Simultaneous Localization and Mapping) would aid in creating a crash-free path for the robot from start to finish.

CLEAN SWEEP: The Floor Cleaning Robot

Abstract: Modern households are getting increasingly automated and intelligent. Home automation makes life more convenient and gives individuals more free time. Domestic robots are becoming more prevalent in homes and in people's daily lives, yet the market is still young and in its infancy. But a rise is anticipated, and the use of household robots is changing. There are many robotic vacuum cleaners on the market, but very few of them wet-clean floors. This project's goal is to create and use a vacuum robot with two cleaning modes. Through a phone application, autonomous and manual modes are available. Instead of using a manual vacuum, a robot vacuum cleaner is designed to make cleaning easier. This project's goal is to build and put into action a vacuum robot with two cleaning modes: autonomous and manual, the latter of which may be accessed via a phone application.

Design and Development of Floor Cleaning Robot

Abstract: Robots are being extensively used in various fields of work. They have taken over dangerous, boring, and monotonous jobs. While cleaning is still done with the help of maids and janitors in institutes and big building complexes which strains and tires them. Hence the need for an autonomous floor cleaning robot. The existing floor cleaning robots are bulky and are not very user-friendly. They tend to be on the expensive end. Our project aims at providing a solution at lower cost, better integration with the help of IoT and better overall user-friendliness. Keywords: robots, Autonomous, IoT, user-friendliness, integration

IoT Based Floor Cleaning Robot

Abstract: Cleaning the dust from the floor is one of the daily tasks that must be completed. This is a common practice not only at home, but also at companies and shopping malls. Due to the fact that dust-cleaning operations take a long time, other activities are sometimes disregarded [6]. To eradicate this problem, we came up with this project based on cleaning mechanism, our robot can reach out to places where human access is not possible. Taking the advantage of advancements achieved in mechanical technology innovation have made human life much easier and more pleasant. Today's clinics are becoming more sophisticated and automated. Homegrown robots are making their way into people's homes and daily lives, although the sector is still in its infancy. Regardless, a shift is expected, and the adoption of indigenous robots is moving forward. There are a few mechanical vacuum cleaners on the market, but only a handful of them can clean wet floors. Using a remote mechanical cleaning framework, this robot makes floor cleaning simple and rapid. This remote framework includes a transmitter app that sees a surge in demand for an android portable app that allows the robot to follow directions supplied by the client via the transmitter app. The floor cleaner robot is designed to make cleaning interactions easier than they would be if done manually. The main purpose of this project is to design and build a cleaning robot model using NodeMCU. Keywords: NodeMCU, Moping, Servo, Robot, Automation.

Design and fabrication of a novel concept-based autonomous controlled solar powered four-wheeled Floor Cleaning Robot for wet and dry surfaces

Design and fabrication of a novel concept-based autonomous controlled solar powered four-wheeled Floor Cleaning Robot for wet and dry surfaces

A Deep Learning-Based Dirt Detection Computer Vision System for Floor-Cleaning Robots with Improved Data Collection

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.

Floor Cleaning Robot

The cleaning robot takes significant place in automated examination. Floor cleaning robot is utilized in indoor spots like rooms, enormous workplaces etc. Cleaning physically is troublesome and dreary work and there is chance that the person cleaning leaves some part messy. Furthermore, it is tedious and bothering. The cleaning activity is more mechanized and therefore, the cleaning robot helps people. The robot is in small in size and is lightweight, so it can be used in a packed spot. Enormous workplaces have huge cleaning requirements, and physically it is not productive. Robots are advantageous for this reason.

Autonomous Floor Cleaning Robot

Autonomous Floor Cleaning Robot

Automation of Floor Cleaning Robot

Automation of Floor Cleaning Robot

Floor Cleaning Robot Control System with Android Based Voice Command

To clean the floor, it can be done manually by using a floor mop along with floor cleaning fluid, but this will result in a floor that is already clean but the implementation time is long enough to interfere with the time of mothers who are office workers or manufacturers to help their husband’s income. . In the process of cleaning the floor, cleaning the floor can be done until the floor is clean so that the children are comfortable playing on the floor. The process of cleaning floors can be done in various ways, either manually or automatically. To clean the floor with a large floor size more quickly, the floor is cleaned with machine power. But the range and limitations of floor cleaning machines exist. So it is necessary to make a robot that works automatically without human assistance to do the cleaning so that the job is done quickly and the area limit of the cleaning results is greater. The system will work using pengerak technology remotely using bluetooth. This system aims to connect communication between wheeled robots and smartphones via Bluetooth communication media. From the test results, the author has successfully applied bluetooth technology to a wheeled robot by providing instructions such as forward, backward, turn right, turn left, and stop. This robot is used to clean floors quickly and practically so that it can be done by itself.