In this blog post, we’ll explore the evolution of robot vacuum cleaners and examine how the artificial intelligence technologies incorporated into them—such as obstacle detection, user interaction, indoor positioning, and map-building—enable them to plan and perform cleaning tasks autonomously.
Xiaomi, a Chinese company sometimes referred to as “the continent’s mistake,” began selling its robot vacuum, the “Mi Robot Vacuum,” on September 6, 2016. At the time, the price ranged from approximately 250,000 won to the low 300,000 won range, making it quite affordable compared to existing robot vacuums. It captured the public’s attention with its groundbreaking price point and outstanding cleaning performance, and on the cross-border e-commerce platform Qoo10, approximately 600 units were sold over the two-month period from September to October 2016, making it one of the site’s bestsellers. Additionally, Qoo10 reported that sales of robot vacuums from the Chinese brand Chuwie in September 2016 increased by 121% compared to August. According to the International Federation of Robotics (IFR)’s “2016 Service Robotics Report” released at the time, sales of personal and household service robots in 2015 reached 5.4 million units—a 16% increase from the previous year—with robot vacuum cleaners and floor-cleaning robots accounting for 3.6 million units, an 11% increase. This trend demonstrates that robotic vacuum cleaners were gradually establishing themselves as mainstream household appliances, moving beyond being merely novel gadgets. Since then, robotic vacuum cleaner technology has advanced even more rapidly, evolving beyond simply following predetermined paths to a level where they can recognize their surroundings, create maps, avoid obstacles, and adjust their cleaning methods according to user commands. Today, products are emerging that utilize LiDAR, cameras, 3D sensors, various distance sensors, and artificial intelligence to identify the layout of a home and obstacles, and plan cleaning routes.
The reason robotic vacuum cleaners have gained such popularity is that they offer good performance at a relatively affordable price. Early robot vacuums—also known as intelligent robot vacuums—either followed paths predefined by the user or designer or cleaned using a “random pattern.” The random pattern involved the robot changing direction at random angles whenever it collided with an obstacle during cleaning. Additionally, virtual walls using infrared sensors or similar technologies had to be set up to prevent the robot from straying beyond specific areas. For example, the “Roomba,” released by iRobot in 2002, established itself as a representative early model of home robot vacuum cleaners. At the time, the “Roomba” cleaned spaces by changing direction when approaching obstacles or walls and moving along walls, and it also used sensors to detect fall hazards such as stairs. However, modern robotic vacuum cleaners have moved beyond simply changing direction after a collision; they use sensors and artificial intelligence to preemptively recognize their surroundings and select more appropriate paths. The artificial intelligence used here is not general-purpose intelligence—capable of understanding and judging every situation like a human—but rather narrow-domain AI designed to perform specific tasks, such as obstacle detection or path planning. The concept of “weak AI” can also be understood in this context. In a 1980 paper, philosopher John Searle distinguished between “strong AI,” which asserts that computers actually possess minds, and “weak AI,” which utilizes computers to simulate or analyze specific cognitive processes. Currently, these specific-purpose AI technologies are being utilized to enhance the automation and cleaning efficiency of robot vacuums, and they can be examined from three main aspects. First is the ability to recognize and avoid obstacles using vision recognition and various sensors; second is interaction with the user through an application; and third is the ability to independently map the cleaning area and plan cleaning routes using indoor positioning and mapping technologies.
First, a key AI function in robot vacuums is the ability to assess the surrounding environment and respond appropriately when unexpected situations arise. Whereas earlier robot vacuums simply changed direction after colliding with an obstacle, newer models use cameras and distance sensors to preemptively identify the location and shape of obstacles and maneuver to minimize collisions. Some of Xiaomi’s robot vacuums are equipped with camera-based AI and a 3D obstacle avoidance system, enabling them to recognize obstacles on the floor—such as cables, socks, slippers, and furniture—and plan avoidance routes. Additionally, distance sensors like LiDAR measure the surrounding space, helping the robot determine its own position and understand its environment. These technologies play a crucial role in enhancing the autonomous navigation performance of robot vacuums. In a 2016 quality test of robot vacuums conducted by the Korea Consumer Agency, the robots’ navigation performance was evaluated in a test course featuring various obstacles, and the products at that time were found to be capable of navigating areas exceeding a certain threshold on their own. Additionally, navigation-related experiments were conducted, such as testing whether the robots could cross door thresholds approximately 2 cm high. At the time, it was anticipated that this technology would evolve to recognize not only thresholds but also spaces with elevation changes below, thereby preventing falls. Indeed, today’s robot vacuums commonly feature fall prevention sensors and various distance sensors that detect stairs or steps and prevent falls. As such, the combination of vision recognition, artificial intelligence, and distance sensors has significantly improved upon the representative issues of early robot vacuums—collisions with obstacles and simple detours.
Second, robot vacuums have become capable of interacting with users by utilizing artificial intelligence and communication technologies. They notify users of cleaning progress or unexpected situations, and allow users to check cleaning status or designate cleaning zones via an app. Software and artificial intelligence technologies are utilized in the process of assessing the robot vacuum’s current status and determining what information to convey to the user. Additionally, depending on situations that arise during cleaning, users can provide feedback—such as changing the cleaning mode, instructing the robot to re-clean specific areas, or adjusting the cleaning path. Xiaomi’s “Mi Robot Vacuum,” introduced earlier, was also a representative product that could be controlled remotely via a mobile app. In today’s robot vacuums, these features have advanced further, allowing users to schedule cleanings, designate cleaning zones, set restricted areas, and manage maps via the app. Some models even utilize cameras to monitor the home environment or provide remote video viewing capabilities. Since users can check the robot vacuum’s status and issue necessary commands even when they are away from home, robot vacuums are evolving beyond mere autonomous cleaning devices into smart appliances connected to their users. In this way, robot vacuums not only clean and complete their tasks autonomously but can also adjust their cleaning methods through communication with the user. Consequently, they are equipped with a system that provides users with necessary information when unexpected situations arise and allows them to continue cleaning based on remote feedback from the user.
Finally, robot vacuums utilize artificial intelligence and location-tracking technology to independently map the interior of the home and select efficient cleaning paths. This process can be broadly divided into three stages: exploration of the cleaning area, zone segmentation, and cleaning route planning. First, when the robot begins cleaning, it uses distance sensors such as LiDAR or cameras to identify features of the surrounding environment and, based on this, constructs a map of the space. Recently, a method that combines LiDAR navigation with SLAM algorithms to map the home’s layout while estimating its own position in real time has become widely adopted. In the past, robots would map the space by exploring the entire area while following the outlines of obstacles within a given zone; however, today’s models integrate information from multiple sensors to map the space and estimate their position with greater precision. During this process, the robot vacuum’s artificial intelligence and software identify spatial features and construct a map. The second stage involves dividing the cleaning area into zones for efficient cleaning. Using the constructed map, the robot can distinguish between spaces such as the living room, bedrooms, and kitchen, or designate user-specified areas as separate cleaning zones. Current robot vacuums also offer features that automatically distinguish between rooms or allow users to select only specific areas for cleaning. The robot continuously calibrates its position as it moves, and within the range where position estimation is possible, it can calculate its movement status using dead reckoning. Finally, the robot finalizes its cleaning plan by determining the order and path for cleaning within the segmented areas. In other words, the robot vacuum’s AI does not require the user to specify each cleaning path individually; instead, it uses sensors to assess the surrounding environment, estimates its own position, divides the space into zones, plans cleaning paths, and proceeds with cleaning autonomously.
In this article, we have examined how artificial intelligence is utilized in robot vacuums through these three methods. Currently, the AI in robot vacuums is used for situational judgment, user interaction, obstacle detection, location tracking and map-building, and cleaning route planning; it appears that the potential for AI applications in robot vacuums will continue to grow in the future. For example, consider a robot vacuum that assesses the level of dirt in a specific area and adjusts its cleaning mode by zone based on those statistics. In fact, products have recently emerged that go beyond simply avoiding obstacles; they use cameras and AI to recognize dust and stains and adjust their cleaning strategies accordingly. Some of Xiaomi’s latest robot vacuums offer features that recognize various types of debris and obstacles and adjust their cleaning methods and evasion paths accordingly. For features such as zone-specific cleaning mode settings to become more sophisticated, two main conditions are required. First, technology is needed that allows the robot vacuum to accurately determine its own location, and second, artificial intelligence is needed to analyze the contamination levels in each zone and determine the appropriate cleaning mode. In the past, indoor positioning technology was researched at a limited level; one method involved dividing the space to be cleaned into multiple cells, measuring the RSSI (Received Signal Strength Indicator) values for each cell, accumulating the data, and then estimating the robot vacuum’s closest location by comparing it to its current RSSI value. However, technology has now advanced toward more precise indoor positioning and mapping by combining technologies such as LiDAR, cameras, inertial sensors, and SLAM. The scope of the technology is now expanding beyond simply determining the robot’s location to recognizing surrounding objects and the level of dirt, and adjusting cleaning methods accordingly. If these technologies become even more sophisticated, robotic vacuum cleaners could evolve from machines that simply clean floors in a predetermined manner into intelligent home appliances that assess the condition of the home and select the most appropriate cleaning method on a case-by-case basis.
Along with advancements in artificial intelligence, the decision-making capabilities of robots equipped with AI continue to improve, and robot vacuums—one such application—are also making steady progress. In the past, people typically envisioned round robot vacuums for use in the home, but in the future, robot vacuums with diverse shapes and functions are likely to emerge. In fact, various new technologies have already emerged recently, such as models with foldable or retractable LiDAR sensors to fit under low furniture, models with structurally enhanced mobility to cross thresholds, and models that use cameras and AI to detect obstacles and dirt. In particular, if robots can take over tasks that are difficult for humans to clean, the range of applications for robotic vacuum cleaners will expand even further. Take bathroom drains, for example. We often experience clogged drain pipes in our daily lives, and cleaning them is a truly difficult task. If a cylindrical AI robot vacuum were installed in the drain pipe, it could be maintained more quickly and conveniently than if a person were to clean it manually. Furthermore, using AI robots in narrow gaps that are difficult for humans to clean—such as the crevices around a gas stove or behind a sink—would help keep these areas consistently clean. As artificial intelligence continues to be integrated into robotic vacuums, the types and forms of cleaning tasks will become even more diverse, and these devices will evolve to automate even those cleaning tasks that are difficult for humans to perform manually. The intelligence contained within the small body of a robotic vacuum is evolving beyond simply removing dust from the floor; it is developing into technology that understands the spaces we live in and independently selects the most appropriate cleaning methods for those specific environments.
