Robotic Vacuum Cleaner Research Articles

Understanding User Experience with Smart Home Products

ABSTRACT There has been a surge in the popularity of smart home products. Existing literature primarily focuses on the initial adoption of these products, with less attention given to the post-adoption stage. This has created a research gap in understanding user experience (UX) and the key dimensions of user satisfaction and dissatisfaction. This study aims to address this gap by examining both positive and negative user experiences with smart home products. Using robotic vacuum cleaners as a case study, we analyze key UX dimensions derived from their positive and negative reviews on Amazon.com that influence user satisfaction and dissatisfaction, respectively. Dimensions contributing to satisfaction include “functionality,” “smartness,” and “enhanced capacity (mopping feature).” In contrast, “insufficient smartness,” “poor customer service,” and “connectivity problems” are critical dimensions for user dissatisfaction. Notably, “smartness” serves as a double-edged sword, potentially leading to high satisfaction but also significant dissatisfaction if poorly implemented. Our analysis also includes a comparative examination of robotic vacuums with traditional vacuums, highlighting the different UX dimensions associated with each product type. Using fuzzy-set qualitative comparative analysis (fsQCA), we further examine the configurations of these UX dimensions that contribute to satisfaction and dissatisfaction. To generalize our findings, we applied our framework to smart thermostats and proposed a UX model for smart home products. Our findings not only fill the research gap in smart home literature but also offer valuable insights for practitioners in designing smart home products and developing marketing strategies. Our research method can be applied to understand user experience with various types of smart home products and other innovative technologies.

Design of a sweeping robot based on fuzzy QFD and ARIZ algorithms

The design of a multifunctional intelligent robotic vacuum cleaner is essential for meeting user needs and replacing human cleaning tasks, thereby improving the effectiveness and convenience of human-robot interaction. Existing robotic vacuum cleaners can address the issues of small dust and hair accumulation; however, a comprehensive and integrated solution for sweeping, mopping, sanitizing, and general waste cleaning remains necessary. This paper employs the Fuzzy Quality Function Deployment (FQFD) tool and the Algorithm for Inventive Problem Solving (ARIZ) algorithm to address these challenges. The research framework integrates FQFD with the Fuzzy Analytic Hierarchy Process (FAHP) methodology to evaluate the weights of user requirements for a robotic vacuum cleaner. Subsequently, these requirements were translated into technical specifications, thus establishing a link between user needs and technical requirements. The ARIZ algorithm is subsequently employed to resolve conflicts and inconsistencies within the FQFD model. Finally, an innovative design for a robotic vacuum cleaner was proposed by utilizing the ARIZ algorithm's problem-solving principles and qualitative and quantitative insights derived from the FQFD. The design is evaluated using the Fuzzy Order Preference Technique (FTOPSIS) method, which resembles the ideal solution. Furthermore, the operational dynamics of the critical components of the design solution that navigate obstacles are analyzed to ensure their effectiveness. The final product can effectively replace humans in complex cleaning tasks while navigating obstacles during sweeping, mopping, and sanitizing for comprehensive space cleaning. The results of this study provide valuable insights for designers to enhance user experience, improve customer emotional satisfaction, and guide future product designs in related product development initiatives.

Path Planning for Floor Cleaning Robots Based on MATLAB Simulink

The increasing popularity of robotic vacuum cleaners in households in recent years has established them as indispensable devices in many homes and has also demonstrated their potential in various other domains. Nevertheless, numerous robotic sweepers available in the market exhibit certain constraints, including repetitive sweeping of the same area, collisions, or entrapment by obstacles. These limitations restrict their effectiveness in specific settings, such as hospitals, where comprehensive cleaning is essential. In an effort to enhance the efficiency and effectiveness of robotic sweepers, a research project was undertaken to investigate the feasibility of navigating a two-dimensional environment utilizing plannerAStar, a path planning algorithm in MATLAB. The study findings indicated a direct correlation between the quantity of goals established through the A-planner and the percentage of independent obstacles effectively circumvented, as well as the area traversed in a distinct "side-to-side" configuration. The enhanced pattern variant exhibited greater consistency and coverage in an environment characterized by walls and corridors. Improved path planning algorithms have been developed utilizing the plannerAStar function in MATLAB Simulink to address issues related to collisions and obstructions encountered by swinging robots in real-world scenarios. Simulation results demonstrate that the path planning algorithm utilizing plannerAStar significantly decreases the path length by 34%, reduces the number of collisions by 75%, and lowers the collision rate by 10% in comparison to the fundamental randomized algorithm. The findings indicate that utilizing the plannerAStar algorithm for path planning is anticipated to notably decrease both redundant collisions and obstacle collisions for the robot, consequently enhancing the robot's operational efficiency.

Developing an intuitionistic fuzzy rough new correlation coefficient approach for enhancing robotic vacuum cleaner.

An intuitionistic fuzzy rough model is a powerful tool for dealing with complex uncertainty and imprecision in graph-based models, combining the strengths of intuitionistic fuzzy sets and rough sets. In this research, a correlation coefficient is an established tool for finding the strength of the relationship between two intuitionistic fuzzy rough graphs since correlation coefficients are very capable of processing and interpreting data. Furthermore, an intuitionistic fuzzy rough environment is integrated with attribute decision-making based on correlation coefficients. In order to measure the correlation between two intuitionistic fuzzy rough graphs, this suggests utilising the correlation coefficient concept and weighted correlation coefficient. In order to identify decision-making issues that are supported by intuitionistic fuzzy rough preference relations, the Laplacian energy and new correlation coefficient of intuitionistic fuzzy rough graphs are calculated in this study. We propose a new approach to computing the relative position loads of establishments by adjusting the correlation coefficient between one personality's intuitionistic fuzzy rough preference relation and the other items, as well as the undecided corroboration of the intuitionistic fuzzy rough preference relation. This paper determines the ranking order of all alternatives and the best one by using the correlation coefficient between each option and the ideal choice. In the meantime, the appropriate example improves decision-making for robotic vacuum cleaners by effectively handling uncertain and imprecise data, thereby optimising cleaning performance.

Floor Cleaning Robot using Android App

The contemporary household landscape is witnessing a paradigm shift towards enhanced intelligence and automation. The integration of home automation systems not only provides increased convenience but also affords individuals more time for other pursuits. This project endeavors to conceptualize and actualize a Vacuum Robot that operates autonomously and manually through a designated mobile application. The Vacuum Cleaner Robot, utilizing components such as Arduino Mega, Arduino Shield, LDR Sensor, Real-Time Clock, Motor Shield L293D, Ultrasonic Sensor, and IR Sensor, aims to streamline the cleaning process, replacing manual vacuuming with an efficient automated solution. The primary objective of this initiative is to successfully design and implement a prototype Vacuum Robot that adheres to user-friendly criteria outlined in the aforementioned context.

Automatic Vacuum Cleaner Robot

Automatic Vacuum Cleaner Robot

Smart Vacuum Cleaning Robot

In today's fast-paced world, the demand for efficient and convenient household technologies continues to surge. Among these, smart vacuum cleaning robots have emerged as indispensable tools, promising automated cleaning solutions that integrate seamlessly into modern lifestyles. This abstract presents a comprehensive overview of a cutting-edge smart vacuum cleaning robot embedded system, featuring five key components for enhanced functionality. The system is anchored by an Arduino Nano-based model, leveraging the versatility and reliability of Arduino technology to orchestrate the robot's operations. Through a Bluetooth-operated interface facilitated by a dedicated mobile application, users can effortlessly control the robot's movements and cleaning schedules, offering unparalleled convenience and flexibility. Equipped with a dry vacuum cleaning system optimized for dust collection, the robot ensures thorough cleaning performance while minimizing maintenance hassles. Additionally, an ultrasonic sensor serves as a robust obstacle detector, enabling the robot to navigate intelligently and avoid collisions with furniture and other obstructions. Crucially, the entire system operates on a battery-powered module, granting the freedom to traverse various spaces without constraints imposed by traditional power sources. By synergizing these components, the proposed smart vacuum cleaning robot embedded system epitomizes the convergence of innovation and practicality, poised to revolutionize the landscape of home cleaning automation.

가정용 로봇청소기의 컬러 트렌드 분석

This study aims to analyze color trends focusing on household robot vacuum cleaners that have won Red Dot, iF, and IDEA design awards over the past four years. For this purpose, 89 products were selected from 2020 to 2023. The color analysis process involved collecting product photos and information posted on the Design Award website and extracting each product's primary, secondary, and accent colors. Munsell H V/C values were calculated by BabelColor CT&A based on sRGB data and distributed on the Hue & Tone color chart to conduct frequency analysis. At the same time, the frequency and characteristics by year were compared based on 18 color combination types. The results of the color trend synthesized were organized into color trends based on Hue and Tone charts, color trends for primary, secondary, and accent colors, and trends by color combination type.

Research on human-computer interaction and user experience of smart home system

This article aims to present a perceptive summary of cutting-edge home automation systems, exploring basic topics including human-computer interaction, communication technologies, networking protocols, and the fascinating science underlying the industry's most well-known applicationthe robotic vacuum cleaner. Put succinctly, using intelligent sweeping robots in smart homes signifies a significant advancement in machine autonomy and artificial intelligence. Furthermore, integrating renewable energy and grid management solutions with smart home systems will be fueled by the pursuit of sustainability and energy efficiency. Ultimately, exploring the intelligent home system's core has shed light on technology and its revolutionary effects on our day-to-day existence. As we stand on the edge of a better future, the convergence of connection, communication, and interaction invites us to embrace innovation and push what is possible in our personal lives.

Visual-and-Language Multimodal Fusion for Sweeping Robot Navigation Based on CNN and GRU

Effectively fusing information between the visual and language modalities remains a significant challenge. To achieve deep integration of natural language and visual information, this research introduces a multimodal fusion neural network model, which combines visual information (RGB images and depth maps) with language information (natural language navigation instructions). Firstly, the authors used faster R-CNN and ResNet50 to extract image features and attention mechanism to further extract effective information. Secondly, GRU model is used to extract language features. Finally, another GRU model is used to fuse the visual- language features, and then the history information is retained to give the next action instruction to the robot. Experimental results demonstrate that the proposed method effectively addresses the localization and decision-making challenges for robotic vacuum cleaners.

Case-Based Reasoning in Achieving Sustainability Targets of New Products

Improving product sustainability is becoming an increasingly significant challenge for modern enterprises. A growing number of manufacturers are interested in enhancing product sustainability throughout the product life cycle. This study is concerned with using case-based reasoning to identify ways of improving product sustainability and select variables for model specification. Parametric models are applied to search for opportunities to improve product sustainability. This can be achieved through changes introduced at the product design stage. Simulations are performed using constraint-satisfaction modeling to identify conditions for achieving the sustainability targets of new products. Constraint-satisfaction modeling provides a suitable framework for finding all possible sustainability-enhancing changes (if any) during the new product development process. These changes may support R&D specialists in identifying opportunities to improve the sustainability of new products. We demonstrate the usefulness of the proposed approach with an example in which our method enabled a reduction in the product failure rate and an increase of battery lifespan for a robot vacuum cleaner line. We analyzed several factors affecting two targets of product sustainability: minimizing the product failure rate and maximizing battery lifespan. Our findings indicate that R&D staff size is the biggest factor in reducing the product failure rate, and that battery capacity is the most significant factor in battery lifespan.

Desain Prototype Robot Vacuum Cleaner Otomatis Berbasis Arduino Uno

The Arduino Uno-based Robot Vacuum Cleaner has been created as an innovative solution to deal with the problem of difficulty in cleaning the floor. Instead of traditional methods such as sweeping or using a large vacuum cleaner that require time and effort, this robot can be operated efficiently through Arduino Uno automatic control. By utilizing Arduino Uno, the robot is able to perform various functions automatically, including moving forward, backward, turning right, and turning left, while sucking up dirt independently. Through the test results, it was found that this robot vacuum cleaner can vacuum dust and garbage thoroughly. In addition, it can operate without error for 3 hours on a full battery, demonstrating reliable and efficient performance.

Inferior, Yet Transformative: the User Experience with Robotic Vacuum Cleaners

Abstract Robotic vacuum cleaners are a prime example of home automation and a rich source of information about how people experience it. On the basis of a 3-week diary study, this article compares householders’ user experience (UX) of robotic vacuum cleaners with their UX of three types of manual vacuum cleaner. The main finding is that robotic vacuum cleaners are inferior in use, yet transform vacuuming. While their inferiority is about their pragmatic qualities, their transformative power relates to their autonomy, agency and hedonic qualities. This contradictory UX involves that robotic vacuum cleaners perform worse or not at all on seven out of nine surfaces and, at the same time, appear to escalate cleanliness standards. The transformation of household chores such as vacuuming is ongoing and calls for further research on engineering and merging the pragmatic and hedonic aspects of UX.

Robotic ease of Disassembly Metric (Re-DiM) for human robot cooperative disassembly: A case study for a vacuum cleaner

Remanufacturing, a beacon of economic and ecological alignment, hinges on disassembly’s complexity. As both manual and robotic approaches face challenges, systems in which human and robots can work iteratively offer adaptable solutions, curbing the required system flexibility at acceptable capital expenditure. However, today no metrics exist to evaluate the ease of disassembly of product designs when considering a combination of manual and robotic disassembly Therefore, this study extends the existing ease of Disassembly Metric (e-DiM) for designers, policymakers, and recyclers in anticipation of the industrialization of human-robot cooperative disassembly systems. For the development of such a metric the expected limitations of robotic disassembly systems are first discussed based on prior research. Subsequently, a set of criteria are presented to define which operations can be performed successfully in a flexible system. Finally, the Robotic ease of Disassembly metric (Re-DiM) is introduced, which serves to comprehensively evaluate both manual and robotic disassembly times. To validate Re-DiM’s applicability, the ease of both manual and robotic disassembly is evaluated for all critical components of a robotic vacuum cleaner. The presented case study highlights today’s robotic disassembly challenges, underscoring also the relevance of novel design for robotic disassembly approaches.

Convolutional neural network for building exterior and interior classification (Kemerovo, Tomsk)

Purpose: The concept development of convolutional neural network for real-time distinguishing between building interior and exterior.The relevance of the topic of the scientific article is determined by the technology integration into everyday life. Houses are increasingly referred to as a smart house. One of the elements of this control system, is robot vacuum cleaner, which cleans various surfaces. Difficulties encountered by such a technique largely depend on the environment definition, in which it is located.The intelligent system can independently distinguish between building interior and exterior, thereby greatly increasing the performance of the firmware complex of modern technology in both domestic and industrial segments.

Unveiling the Mind of the Machine

Abstract Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers.

Vision-based robotics using open FPGAs

Robotics increasingly provides practical applications for society, such as manufacturing, autonomous driving, robot vacuum cleaners, robots in logistics, drones for inspection, etc. Typical requirements in this field are fast response time, low power consumption, parallelism, and flexibility. According to these features, FPGAs are a suitable computing substrate for robots. A few vendors have dominated the FPGA market with their proprietary tools and hardware devices, resulting in fragmented ecosystems with few standards and little interoperation. New and complete open toolchains for FPGAs are emerging from the open-source community. This article presents an open-source library of Verilog modules useful for vision-based robots, including reusable image processing blocks for perception and reactive control blocks. This library has been developed using open tools, but its Verilog modules are fully compatible with any proprietary toolchain. In addition, three applications with a real robot and open FPGAs have been developed for experimental validation using this library. In the last application, the mobile robot successfully follows a colored object using two low-cost cameras (to increase the robot’s field of view) and includes a third camera on top of a servo-driven turret for tracking a second independent object while following the first one in parallel. Resource consumption of all applications has been measured and compared with state-of-the-art proprietary toolchains, revealing that reconfigurable computing with open FPGAs using open tools is now an attractive alternative to designing and creating intelligent vision-based robotic applications using vendor-dependent proprietary tools and FPGAs.

Real-Time Indoor Air Quality Index Prediction Using a Vacuum Cleaner Robot's AIoT Electronic Nose

Real-Time Indoor Air Quality Index Prediction Using a Vacuum Cleaner Robot's AIoT Electronic Nose

Towards Indoor Suctionable Object Classification and Recycling: Developing a Lightweight AI Model for Robot Vacuum Cleaners

Robot vacuum cleaners have gained widespread popularity as household appliances. One significant challenge in enhancing their functionality is to identify and classify small indoor objects suitable for safe suctioning and recycling during cleaning operations. However, the current state of research faces several difficulties, including the lack of a comprehensive dataset, size variation, limited visual features, occlusion and clutter, varying lighting conditions, the need for real-time processing, and edge computing. In this paper, I address these challenges by investigating a lightweight AI model specifically tailored for robot vacuum cleaners. First, I assembled a diverse dataset containing 23,042 ground-view perspective images captured by robot vacuum cleaners. Then, I examined state-of-the-art AI models from the existing literature and carefully selected three high-performance models (Xception, DenseNet121, and MobileNet) as potential model candidates. Subsequently, I simplified these three selected models to reduce their computational complexity and overall size. To further compress the model size, I employed post-training weight quantization on these simplified models. In this way, our proposed lightweight AI model strikes a balance between object classification accuracy and computational complexity, enabling real-time processing on resource-constrained robot vacuum cleaner platforms. I thoroughly evaluated the performance of the proposed AI model on a diverse dataset, demonstrating its feasibility and practical applicability. The experimental results show that, with a small memory size budget of 0.7 MB, the best AI model is L-w Xception 1, with a width factor of 0.25, whose resultant object classification accuracy is 84.37%. When compared with the most accurate state-of-the-art model in the literature, this proposed model accomplished a remarkable memory size reduction of 350 times, while incurring only a slight decrease in classification accuracy, i.e., approximately 4.54%.

Design Prototype Robot Vacuum Cleaner Based on Arduino Microcontroller with Autonomic Mode

The development of robotics technology in the current era has become a highly relevant need to assist humans in various everyday activities. One significant application of this technology is the creation of a prototype "Robot Vacuum Cleaner Based on Arduino Microcontroller with Autonomous Mode." This research aims to design and develop a robot vacuum cleaner capable of autonomously cleaning rooms. The design encompasses several key aspects. The physical design of the robot vacuum cleaner is an essential initial step. The shape, size, and layout of components within the robot are carefully designed to ensure easy maneuverability within the room, efficient cleaning of hard-to-reach corners, and the ability to avoid obstacles. The robot is equipped with several main components, including 3 ultrasonic sensors for obstacle detection, 3 omni wheels for excellent mobility, 2 motor drivers to control wheel movement, and 1 Arduino microcontroller as the central brain coordinating all robot operations. One of the key features of this robot is its autonomous mode. This means the robot can operate independently without human intervention. Ultrasonic sensors are used to detect obstacles around the robot. When an obstacle is detected, the robot changes direction or avoids it, allowing it to continue cleaning the room. The robot is designed to provide users with an efficient and user-friendly experience. Users only need to activate the robot, and it will perform its tasks autonomously without human intervention. This research is expected to provide significant benefits by offering an efficient solution for everyday room cleaning. The well-designed physical appearance and autonomous mode integration of this robot vacuum cleaner make it easy to use and provide a more efficient cleaning experience. Overall, this research contributes to the broader development of robotics technology in the modern era.