Best Robot Vacuums We’ve Tested (July 2025)

Each vacuum of the robot that we consider for the recommendation is put to the test in our test laboratory in Louisville, Kentucky. In addition to the test floors where we carry out our controlled pickup tests, we monitor each vacuum of the robot in a special test room filled with simulated furniture to assess the way it browses around common obstacles. Beyond that, we check the ability of each vacuum cleaner robot to swallow up animal hair without clogging or leave locks in bulk behind, we consider cleaning capacities, and we check how much it browse in false dogs.

Let’s dive a little more into the main considerations, starting with our performance tests.

Picking power of the robot vacuum cleaner

Regarding aspiring prowess, we want to know how effective each robot is against crumbs and other common debris, and also how it behaves against much smaller particles such as dust, dirt and sand. To find out, we use sand as analog for finer particles.

In each case, we disperse a controlled quantity on three test stages: low battery carpet, intermediate carpet and hardwood floors. The low battery carpet is shorter, less in a plush with shorter fibers, so generally the robot vacuum cleaners have easier to remove it (but not always). The middle of the arbor is a softer and softer carpet with higher fibers. It tends to be more difficult for the vacuum of robots (but again, not always). Then, we take the robot vacuum cleaner, carefully emptying his dust tank, send it to clean the affected area and finally measure the weight of everything he managed to pick up. This gives us a percentage of collection of the total amount. From there, we repeat each race twice more and on average the results.

In recent months, we have eliminated our black rice test on hardwood floors, because, more or less, each emptiness of the robot that we tested marked almost 100%. We now use the sand test as a main reference to assess cleaning performance. We consider that everything that is 50% and more as a good score for sand.

Navigation skills on the robot vacuum cleaner

Your robot vacuum will not clean your home as well as capable of navigating it. The ideal cleaner will facilitate the search for space in room and automatically avoid obstacles along the way, which allows automated cleaning with appropriate low maintenance.

We make sure to observe each vacuum cleaner of the robot because it cleanses to have a good idea of ​​how it browses, but to obtain the best comparison of more cleaning to more cleaning, we take exposure photos with long heads of each because it cleanses our dark test room, with glow sticks attached to the top of each directly above the suction room. The resulting images show us light trails that reveal the road to the robot while it sails in the room and cleanse our simulated furniture.

You will find below an example of the Ecovacs Deebot T30, our best as a whole. It offers superb zone coverage and has made a navigation in a very organized and efficient manner. He obtained a 10 out of 10 in the navigation score, taking an average of 15 minutes to finish a full cycle.

On the other hand, we have an emptiness of the robot with bad navigation, the Florio Noesis. He missed several places in the room, and the image of the Light Chemin test contains some brighter spots, potentially indicating the robot vacuum cleaner has spent time turning in place. What is notable is the very disorganized navigation model. All of this led to bad navigation scores.

In large part, this comes back to technology at stake. Over the years, we have always noted that robot vacuum cleaners who use Lidar navigation guided by laser tend to be very good to map their environment and find their way. Meanwhile, 3D cartography cameras with an intelligence of object recognition can give the emptiness of robots the additional capacity to identify and adapt to obstacles on their way.