Research on Innovative Applications and Market Impacts of

AI-Based Robotic Vacuum Cleaners

Jiaqiao Xie

International Business, Curtin Singapore, 10 Science Park Road, Singapore

Keywords: Artificial Intelligence, Robotic Vacuum Cleaners, Multi-Sensor Fusion, Path Planning, Reinforcement

Learning.

Abstract: With the rapid advancement of smart home technology, artificial intelligence (AI) has gradually become the

driving force to push the evolution of home appliances. The thesis focuses on analyzing the innovative

applications of robotic vacuum cleaners and their practical outcomes of AI through the key technical areas of

environmental sensing, path planning, human-machine interaction, and reinforcement learning. This study

discussed using multi-sensor fusion technology to achieve obstacle recognition and ecological modelling,

applying advanced algorithms to enhance cleaning efficiency, and incorporating natural language processing

for voice command control. Furthermore, the thesis explores the application of reinforcement learning in

dynamically adjusting cleaning routes. The research findings demonstrate that AI not only significantly

enhances the intelligence of robotic vacuum cleaners but also drives product differentiation and

comprehensive improvements in user experience. Finally, the thesis evaluates the future prospects of robotic

vacuum cleaners within the smart home ecosystem based on market survey data and offers recommendations

to address existing technical challenges. This work provides a reference framework for future technological

innovation and market promotion of smart appliances through in-depth theoretical and practical analysis.

1 INTRODUCTION

In recent years, the global smart home appliance

market has grown rapidly, and smart home products

have increasingly gained more and more demand in

households. Among the multitudinous smart home

appliances, robotic vacuum cleaners as an essential

segment have quickly gained consumers’ favour due

to their automated cleaning feature and convenience

(Chataut et al., 2023). According to the latest

statistics, the value of global smart home market has

over $155 billion, and robotic vacuum cleaner

industry is growing at nearly $3 billion annually (at

an average compound annual growth rate of 2%),

which is rapidly expanding its market share (Chataut,

et al., 2023).

Breakthroughs in artificial intelligence (AI) have

provided new insights and technical approaches for

developing robotic vacuum cleaners. These devices

have evolved from random cleaning patterns to

intelligent decision-making based on multi-sensor

fusion, path planning, and reinforcement learning,

https://orcid.org/0009-0001-2490-3533

showcasing the profound integration of AI into

hardware products (Yanjie et al., 2024). Existing

studies have largely focused on fundamental

navigation and obstacle recognition. However, as

user demands diversify and home environments grow

more complex, there is increasing pressure for higher

levels of robotic vacuum intelligence (Yanjie et al.,

2024). While prior research has enhanced cleaning

efficiency and user experience to some extent,

challenges remain in dynamic environmental

perception and personalized cleaning strategies.

The purpose of this paper is to explore innovative

applications of AI in robotic vacuum cleaner field

through an in-depth analysis from both technological

and market perspectives. In terms of technical side,

the implementation of environmental sensing, path

planning, human-machine interaction, and

reinforcement learning algorithms in robotic vacuum

cleaners are discussed in the paper, and representative

products such as Ecovacs X1 Omni and Xiaomi’s

Omni Robot Vacuum Pro are highlighted. On the

market side, it examines how AI-driven

Xie, J.

Research on Innovative Applications and Market Impacts of AI- Based Robotic Vacuum Cleaners.

DOI: 10.5220/0013849300004719

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on E-commerce and Modern Logistics (ICEML 2025), pages 543-547

ISBN: 978-989-758-775-7

543

differentiation, user experience enhancement, and

ecosystem collaboration enable robotic vacuum

cleaners to stand out amid intense competition.

Combining literature reviews, case studies, and

experimental reports, this research seeks to construct

a systematic technical and market evaluation

framework, revealing how technological

advancements improve product performance and

assessing their economic impact in practical

applications.

The paper is divided into five sections. The first

section introduces the research background, current

state of the field, and main objectives. The second

section delves into AI’s innovative applications in

robotic vacuum cleaners. The third section analyzes

how these innovations influence the home appliance

market. The fourth section proposes

recommendations to address existing technological

bottlenecks. The fifth and final section summarizes

the research findings and discusses future

development directions. This study not only holds

theoretical significance but also provides practical

value for advancing smart appliance technologies and

their market applications.

Against the backdrop of an increasingly

competitive global smart home appliance market, this

research offers new technical insights for upgrading

and transforming robotic vacuum cleaners. It also

provides valuable references for related companies in

product development, market promotion, and

ecosystem construction. In the future, with the

continued integration of emerging technologies such

as AI, 5G communication, and big data, robotic

vacuum cleaners might embrace even greater

development opportunities.

2 AI AND INNOVATION

APPLICATIONS OF ROBOTIC

VACUUM CLEANERS

In the development of robotic vacuum cleaners, the

integration of artificial intelligence provided

necessary support and opportunity for advancing their

intelligence and automation capabilities. In this

section, AI applications in robotic vacuum cleaners

will be detailed explored and focused on four key

areas: environmental perception, path planning,

human-machine interaction, and reinforcement

learning.

2.1 Environmental Perception

Environmental perception technologies are the

foundation of autonomous navigation and obstacle

avoidance functions in robotic vacuum cleaners’

design. Currently, the mainstream realization

methods rely on multi-sensor fusion techniques,

incorporating tools such as Lidar, cameras, ultrasonic

sensors, and infrared sensors (Megalingam et al.,

2025). By collecting and analyzing data from these

sensors, robotic vacuum cleaners can quickly

construct indoor environment maps, and precisely

identify and localize furniture, obstacles, and

dynamic targets (Megalingam et al., 2025). For

instance, the Ecovacs X1 Omni employs a dual-

sensor system combining Lidar and high-definition

cameras (Li, 2022). Such a feature of Omni

significantly enhances the accuracy of environmental

recognition and resilience against interference (Li,

2022). In terms of data processing, techniques such as

filtering, feature extraction, and deep learning

algorithms enable robots to detect path obstacles in

complex environments in real time and dynamically

adjust cleaning strategies (Ramalingam et al., 2021).

2.2 Path Planning

Efficient path planning is critical to improving the

cleaning efficiency of robotic vacuum cleaners.

Traditional cleaning algorithms often rely on

predetermined and fixed rules or random paths, which

may leave blind spots at home where not cleaned

when facing complex household environments

(Abdulsaheb and Kadhim 2023). As techniques of

optimization have been advanced, robotic vacuum

cleaners now frequently employ path planning

methods based on graph theory, genetic algorithms

(GA), and ant colony optimization (ACO)

(Abdulsaheb and Kadhim 2023). By segmenting,

analyzing, and reconstructing environmental

(scanning) maps, robots can generate optimal

cleaning paths, reduce redundant cleaning, and

maximise coverage (Abdulsaheb and Kadhim 2023).

Some models also implement real-time path

optimisation, using online data updates to respond

immediately to changes in the home environment,

thereby greatly improving overall cleaning efficiency

(Abdulsaheb and Kadhim 2023).

2.3 Human-Machine Interaction

Human-machine interaction (User experience) is

increasingly emphasized as a crucial bridge between

users and devices in modern smart home products

(Yapici et al., 2022). Robotic vacuum cleaners rely on

natural language processing (NLP) and voice

recognition technologies to facilitate interaction

(Wan et al., 2022). For example, Xiaomi’s Omni

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544

Robot Vacuum Pro features a built-in voice

recognition module capable of understanding various

accents and, through integrated NLP algorithms,

quickly interpreting command intentions. This

enables a range of operations such as remote control,

scheduled cleaning, and zoned cleaning (XiaoMi n.d.,

2025). Furthermore, deep integration with mobile

apps allows users to monitor device status in real

time, receive maintenance suggestions, and handle

fault alerts-creating a highly intuitive and convenient

interaction experience (XiaoMi, n.d., 2025; Tung and

Campos 2021; Ge et al., 2023).

2.4 Reinforcement Learning

Reinforcement learning, a key method within

machine learning, has gained widespread use in the

field of robotic vacuum cleaners (Butaney 2024).

Unlike traditional planning algorithms, the

reinforcement learning technology could enable

vacuum robots to autonomously learn optimal

strategies through ongoing interactions with the

surrounding environment (Li et al., 2024). For

instance, the robotic vacuum cleaner could record

areas that are frequently dirty in the home and use

feedback from cleaning results to refine its intensity

of path planning and cleaning. This results in

intelligent, personalized cleaning strategies. Over

time, the device not only adapts to changing

conditions but also continually improves its

performance, effectively becoming “smarter” with

repeated use (Butaney, 2024).

2.5 New Technologies Exploring

In addition to these established technologies, the

combination of edge computing and cloud data

processing opens new possibilities for robotic

vacuum cleaners (Dawarka and Bekaroo, 2022). By

connecting and uploading some computational tasks

to the cloud platforms, vacuum robots could receive

real-time global data updates, therefore, could utilize

big data analytics to achieve more precise

environmental modelling and fault prediction

(Dawarka and Bekaroo, 2022). Moreover, the

widespread adoption of 5G technology promises

faster data transmission speeds and lower latency and

enhances robots’ responsiveness and cleaning

efficiency in complex environments. In the future,

more and more high-precision sensors and advanced

intelligent algorithms will be introduced and driving

robotic vacuum cleaners toward even higher levels of

intelligence as sensor technologies continue to

improve.

3 ANALYSIS OF MARKET

IMPACT OF INNOVATIVE

PRACTICES

With the deeper integration of artificial intelligence in

robotic vacuum cleaner technology, the robot

products have seen significant advancements in

functionality, user experiences, and market

competitiveness. This section will examine the

market impact by discussing three dimensions:

product differentiation, experience enhancement of

users, and ecosystem synergy effects.

3.1 Product Differentiation

Traditional robotic vacuum cleaners typically offer

limited functions, focusing primarily on simple

cleaning tasks (Rui and Ying, 2025). However, with

the introduction of artificial intelligence, the new

generation of robotic vacuum cleaners now goes

beyond traditional cleaning to deliver advanced smart

features like providing “self-cleaning stations” and

functionality of “auto-dust collection” (Li et al.,

2023). These innovations could effectively address

several challenges associated with traditional

products. They have improved maintenance

procedures, enhanced cleaning efficiency, and

increased overall user convenience. At the same time,

the advantage could enable vacuum robot products to

stand out from the competitive market due to the

empowerment of these technologies. Therefore,

companies could create unique product positioning

that caters to consumers’ growing expectations for

advanced smart appliances, thus capturing a larger

share of the increasingly competitive market by

leveraging technological innovation.

3.2 Experience Enhancement of Users

User experience is a critical factor in determining

whether household appliances successfully integrate

into daily life. Vacuum Cleaners Robot installed with

AI could provide users a more seamless and intuitive

interactive experience with features like intelligent

navigation, voice control, and remote monitoring (Ge

et al., 2023). Consumers can remotely start or stop

cleaning tasks via mobile apps, check device status

and fault alerts timely manner, and even perform self-

diagnostics and online maintenance (Ge et al., 2023).

These user-centric designs not only improve

operational efficiency but also strengthen brand trust

and loyalty, contributing to positive word-of-mouth

effects (Ge et al., 2023).

Research on Innovative Applications and Market Impacts of AI- Based Robotic Vacuum Cleaners

545

3.3 Ecosystem Synergy Effects

In the smart home ecosystem, interconnectivity

among various devices has become a mainstream and

popular trend (Rodriguez-Garcia et al., 2023). As a

key node in the smart appliance category, robotic

vacuum cleaners can integrate with platforms such as

Tmall Genie and Google Home to enable features like

voice control and scenario-based interactions (Li and

Zhang, 2024). This multi-connection feature (synergy)

not only enhances the product’s added value but also

creates a smarter, more convenient living

environment for consumers (Li and Zhang, 2024).

Companies can attract a wide range of partners,

collectively establishing a multidimensional smart

home ecosystem that spans lighting, security, and

entertainment by developing open platforms and

ecosystems. Such collaborative efforts drive

innovation and sustainable development across the

entire industry (Li and Zhang, 2024).

4 RECOMMENDATIONS

Although the widespread application of artificial

intelligence has brought numerous breakthroughs to

the development of robotic vacuum cleaners, certain

technical bottlenecks persist in practical

implementation. Firstly, in complex home

environments, these robots still experience a degree

of missed spots and less-than-optimal path planning

when faced with dynamic obstacles. While multi-

sensor fusion has improved environmental

recognition accuracy, further optimization is needed

for real-time data processing and predictions in

complex scenarios. Secondly, the stability and

response speed of current voice recognition and

natural language processing technologies under noisy

conditions remain areas for improvement. Lastly,

reinforcement learning algorithms can encounter

slow convergence and overfitting issues over

extended periods of use, limiting the further

refinement of personalized cleaning strategies.

To address these challenges, this study makes the

following recommendations. First, companies should

strengthen cross-field collaborations with universities

and research institutions to develop dynamic obstacle

avoidance algorithms tailored to complex home

environments. Second, while maintaining cost

control, hardware configurations should be further

optimized to enhance sensor precision and data

processing capabilities, thereby improving overall

environmental awareness. Third, leveraging edge

computing and 5G technology, edge-cloud

collaboration should be advanced to enable real-time

data transmission and processing, increasing the

robot’s responsiveness to dynamic changes. Fourth,

multi-model integration and online learning

mechanisms should be introduced to continuously

improve reinforcement learning algorithms,

enhancing their convergence speed and adaptability,

and ensuring optimal cleaning strategies even in

evolving household conditions. Fifth, enterprises

could establish a comprehensive user feedback

platform to collect and analyze real-world usage

issues and suggestions, therefore to provide

profirsthand data to guide their technological

enhancements.

By implementing these initiatives, the robotic

vacuum cleaner companies could achieve a higher

level of intelligence and better meet consumers’

diverse and personalised needs. Hence, the smart

home industry would be driven toward a new stage of

development.

5 CONCLUSION

This study provides a comprehensive analysis of the

innovative applications of artificial intelligence in the

robotic vacuum cleaner field, and in particular,

focuses on the implementation and effectiveness of

environmental perception, path planning, human-

machine interaction, and reinforcement learning

algorithms. The findings of this study are that robotic

vacuum cleaners’ autonomous navigation, obstacle

avoidance, and cleaning efficiency could be

significantly enhanced by effectively utilizing multi-

sensor fusion, advanced algorithm optimization, and

intelligent interaction systems. Moreover, the

vacuum robot devices would perform and finish more

precise and efficient cleaning tasks when

continuously combining ongoing integration of edge

computing and 5G technology support, as modern

home environments are becoming increasingly

complex and dynamic. On the market side,

technological innovation could not only drive product

differentiation but also greatly enhance users’

experience and device intelligence, which contributes

to the development of a broader smart home

ecosystem.

In conclusion, robotic vacuum cleaners are on the

brink of a new transformation powered by the

combined advancements in artificial intelligence and

5G technology. In the face of drastic market

competition, companies must continuously refine

their technical approaches, promote cross-

disciplinary collaboration deeply, and incorporate

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emerging technologies such as big data and cloud

computing to accelerate intelligent upgrades. In the

future, AI-powered robotic vacuum cleaners are

expected to evolve beyond being merely cleaning

tools, and become integral components of a smart

home ecosystem that delivers smarter, more

convenient, and more efficient living experiences.

The insights from this study also aim to offer valuable

guidance for innovation and market expansion in

other smart home appliances, heralding a future

where intelligent living becomes increasingly

universal and comprehensive.

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