How do Robot Vacuums Know How to Clean Your Smart Home?
April 15, 2024
I posted last week about when it’s time to get a pool cleaning robot and what to look for. It made me wonder if robot pool cleaners cleaned the same way that robot vacuums do in our home. Then I realized I really didn’t know how robot vacuums do their job. Embarrassingly, Debbie and I have two of them — a Roomba and a Deebot. So, I did some research I thought I’d share for those that are taking their robot vacuums for granted like we have been.
When evaluating a robot vacuum, it’s natural to focus on features that directly impact cleaning, like suction power, cleaning modes, and how much dirt and dust it can hold before you have to empty it. But if you want to get an idea of how well a particular robot vacuum will clean your home, it’s important to also consider how it maps and navigates your home.
Mapping and navigation ultimately determine how efficiently and thoroughly a robot vacuum cleans. Effective navigation allows robot vacuums to maneuver through various obstacles, traverse different floor types, and cover the entire cleaning area without missing spots. It also contributes to shorter cleaning times and longer battery life by optimizing cleaning paths and avoiding unnecessary retracing. Accurate navigation enhances the overall user experience, as well, providing convenience and confidence in homeowners who rely on these smart appliances to maintain their living spaces.
So, let’s take a look at the different navigation types robot vacuums use.
Robot vacuums map and navigate your home in a number of ways ranging from the most primitive to the most advanced approaches. These technologies aren’t mutually exclusive and are often used in various combinations to improve navigation, but it’s important to understand how each works on its own to make the most informed purchasing decision.
Sensor mapping is the earliest and most basic navigation method used by robot vacuums. It employs an array of sensors to tell the robot its position on the floor and identify any safety issues.
All robot vacuums, regardless of price, incorporate an array of sensors to help them find their way around your home, but budget robot vacuums often rely exclusively on sensor mapping navigation.
The most common sensors you’ll find on any robot vacuum include:
Cliff sensors: These enable the robot vacuum to identify stairs, ledges, and steep thresholds so they don’t fall or tip over. Cliff sensors on the base of the robot bounce infrared light beams off the floor to calculate the distance between the two. The longer it takes the light to return to the sensor, the longer the fall distance. If a significant drop is detected, the robot will stop and reverse direction.
Wall sensors: Wall sensors help the robot vacuum locate and navigate along walls. This helps prevent collisions but also allows them to follow wall edges where dirt typically accumulates for a more thorough cleaning.
Bump sensors: Bump sensors allow robot vacuums to detect physical contact with obstacles. The most basic form is physical bumpers or touch-sensitive pads located around the perimeter of the device. When the vacuum makes contact with an obstacle, such as a wall or furniture leg, these bumpers depress a trigger, signaling to the vacuum that it has encountered an obstruction. More advanced robot vacuums may use other technologies, such as infrared light or software algorithms, for obstacle detection.
Optical encoders: These sensors are positioned on the vacuum’s wheels to track the distance traveled by the robot. They use light sensors to count wheel rotations. By monitoring wheel movement, the robot can accurately calculate its travel distance, which helps with navigation, route planning, and timely return to the charging base when needed.
Battery sensor: Robot vacuums need to periodically return to their charging base when their battery is depleted. To do so, they must detect when their battery is low and accurately gauge the distance traveled from the base to ensure they can return within the remaining charge. Battery sensors play a critical role in monitoring and managing the battery charge and providing information about the vacuum’s distance from the charging dock.
Sensor-based robot vacuums generally offer an affordable and straightforward cleaning solution for users on a budget. Even with the most basic sensors, these models can effectively navigate around obstacles and prevent falls, making them suitable for small spaces with simple layouts.
However, the navigation precision of sensor-only robot vacuums may be limited, resulting in random cleaning patterns and potential inefficiencies such as long cleaning times and missed spots. Sensor-based vacuums may also struggle in complex environments without the advanced smart features found in higher-end models.
While sensor-dependent vacuums are affordable, require less maintenance, and are easy to operate, users should consider their cleaning efficiency and suitability for their specific home environment before opting for a sensor-based robot vacuum.
A quick breakdown on sensor-based navigation
Pros
- Budget-friendly
- Simple operation
- Suitable for small spaces
- Few, if any, privacy concerns
Cons
- Limited navigation precision
- Random cleaning patterns
- Difficulty with complex environments
- Lower cleaning efficiency
Some robot vacuums utilize a built-in digital camera for navigation, allowing them to “see” the environment more like a human does. Cameras enhance robot vacuum functionality and user experience in several ways. Primarily, they provide greater navigation precision than sensors alone, because they capture and analyze visual data to better understand cleaning areas and navigate around obstacles more accurately.
They also enable advanced smart features, such as object recognition and room mapping. Some camera-equipped robot vacuums can recognize and avoid specific objects, such as kids toys or cables, increasing safety and efficiency. Room mapping capabilities allow for customized cleaning schedules and targeted cleaning of specific areas. Additionally, some models offer remote monitoring capabilities, allowing users to view live video feeds of their homes and use the vacuum as a surveillance device for added security.
However, camera-mapping robot vacuums come with some drawbacks. Just as humans struggle to see in the dark, camera-dependent robots face similar challenges, limiting their ability to navigate in low-light conditions unless equipped with supplementary lighting. These robots may also misinterpret dark tiles, carpets, and deep shadows cast by furniture as drop-offs and avoid navigating over these areas.
Robot vacuums equipped with digital cameras raise several privacy concerns as well, including the potential for unauthorized surveillance, data security breaches, and third-party access to captured images or videos. Captured images may also inadvertently reveal details about the home’s layout or personal information, posing risks to privacy and security. Users should carefully review privacy policies, disable camera or microphone functionalities if unnecessary, and take steps to secure their home network to mitigate these risks.
A quick breakdown on camera mapping
Pros
- Precise navigation
- Efficient cleaning patterns
- Advanced smart features
- Remote monitoring
Cons
- Limited functionality in low light
- Dependency on visual input
- Privacy concerns
- Cost
Digital cameras are also often paired with SLAM algorithms or LIDAR for greater navigation accuracy. SLAM (Simultaneous Localization and Mapping) is an algorithmic technique robot vacuums use to create maps of their surroundings while simultaneously determining their position within those maps. It works by collecting data from cameras, LiDAR, wheel encoders, and other sensors to identify features in the environment.
Based on this data the robot then initializes a map — often visually displayed in the vacuum’s companion app — and continuously updates it as it moves, refining its estimated position through probabilistic localization methods.
SLAM algorithms also identify and correct errors by detecting loop closures. These are instances where the robot revisits a previously visited location to refine maps and improve consistency. Overall, SLAM enables robots to navigate and map complex environments autonomously and accurately.
SLAM offers several advantages over other mapping methods, including precise navigation, adaptability to complex environments, real-time mapping capabilities, and optimized cleaning paths.
In a blind test, you’d be able to tell a SLAM robot vacuum from a purely sensor-based one by its movement. Rather than bouncing from one obstacle to the next, a SLAM robot will move in logical lines and navigate around edges and corners with ease. For homeowners, that translates to reduced cleaning times and better overall coverage.
The downside of SLAM technology is that it requires advanced sensors and computational power, which increases the cost of robot vacuums that use it. SLAM may also be susceptible to mapping errors in environments with reflective surfaces or low-light conditions, higher power consumption, and increased complexity leading to potential maintenance issues.
Because SLAM technology involves mapping the interior of homes, there’s a possibility that sensitive information about the layout and contents of the living space could be captured. This raises concerns about privacy violations, especially if this data is accessed or shared without the homeowner’s consent. If the robot vacuum is also equipped with cameras for vSLAM (visual SLAM), there may be additional privacy risks related to video surveillance and potential data breaches.
A quick breakdown on SLAM navigation
Pros
- Accurate navigation
- Adaptability to complex environments
- Real-time mapping
- Optimized cleaning paths
Cons
- Cost
- Power consumption
- Mapping errors
- Potential privacy concerns
LiDAR (Light Detection and Ranging) is a sensing technology that employs laser light to gauge distances to objects in the surrounding environment. It operates by emitting invisible laser beams in various directions, which bounce off objects, such as walls, furniture, or obstacles, and return to the sensor.
By measuring the time taken for these beams to travel, LiDAR calculates distances to create a three-dimensional map of the environment. This data aids the robot vacuum in navigation, allowing it to efficiently maneuver through spaces, avoid obstacles, and plan optimal cleaning paths.
LiDAR navigation offers several advantages over other mapping methods. For starters, LiDAR sensors provide highly accurate distance measurements, enabling precise mapping of the environment and accurate localization of the robot. They are also less affected by changes in lighting conditions compared to other types of sensors, making them suitable for use in a variety of environments.
Another strength of LiDAR is its wide field of view, which allows it to capture a broad range of environmental data in a single scan, reducing the need for the robot to make frequent scanning movements or rotations to capture data from different angles.
This comprehensive coverage enables more efficient navigation, better obstacle avoidance, and faster mapping processes. Because of these benefits, LiDAR is typically used by the newest and most advanced robot vacuums on the market.
But like all navigation methods, LiDAR has its limitations. LiDAR sensors may struggle to detect certain materials like glass or transparent surfaces, leading to potential navigation challenges in environments with these materials.
Moreover, they require significant power to operate, which can impact the overall power consumption and battery life of the robot vacuum. LiDAR sensors are also relatively expensive compared to other navigation sensors, which can drive up the cost of products that use them.
Finally, LiDAR is subject to the same privacy issues around home mapping as SLAM.
A quick breakdown on LiDAR navigation
Pros
- Accurate navigation
- Adaptability to complex environments
- Real-time mapping
- Optimized cleaning paths
Cons
- Cost
- Power consumption
- Mapping errors
- Potential privacy concerns
Selecting the right robot vacuum for your needs involves considering several factors, including cost, the complexity of your home, and your desired cleaning speed. If budget is a primary concern, a sensor-based robot vacuum may be the best option, assuming you can tolerate the trade-off for less precise navigation. For those with larger or more intricate living spaces, investing in a robot vacuum equipped with advanced navigation technologies such as SLAM, LIDAR, or camera-based navigation can pay off with greater efficiency and coverage.
Additionally, if you prioritize quick cleaning sessions, choosing a robot vacuum with faster navigation speeds and optimized cleaning paths can help minimize cleaning time. If you weigh these considerations alongside your specific preferences and requirements, you can confidently select the robot vacuum that best suits your home and lifestyle.
I was pleasantly surprised to learn more about how robot vacuums work after doing this research. And to circle back to my original curiosity that made me wonder if robot pool cleaners cleaned the same way that robot vacuums do — they do.
I’m curious what your thoughts are around robot vacuums. Maybe you already have one. If so, I’d love to hear about your experience with it and which one you purchased. If not, which type would you choose based on the information above?
Let Debbie and I know what you think in the comments, DMs and emails as we really enjoy hearing from you. Thanks again to all those following Debbie and I through our home building journey. It’s great to hear your success stories and suggestions as we move through the process. And if you like the content I’m posting each week, don’t forget to ‘Like’ and ‘Follow.’
In full disclosure, I’m not an affiliate marketer with links to any online retailer on my website. When people read what I’ve written about a particular product and then click on those links and buy something from the retailer, I earn nothing from the retailer. The links are strictly a convenience for my readers.
