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 also brows.
Let’s dive a little more into the main considerations, starting with our performance tests.
Metric notation of the robot vacuum
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| Undervaluation category | Weight | What we looked for | |||||
|---|---|---|---|---|---|---|---|
| Performance | 30% | Performance score extrapolated from AVG (AVG_SAND + AVG_BLACKRICE) | |||||
| Value / price | 25% | Retail price assessment given all other features. That is to say, does this price seem just for the value offered? Is it justified by the performance, functionality and efficiency of navigation? | |||||
| Features | 15% | What features offer the void? Auto-spouse? Several batteries? Nav Tech? Mopping? | |||||
| Execution time | 20% | Consider the navigation efficiency score (1-10), depending on the time taken to finish a complete cleaning cycle in the vacuum navigation test of the Cnet Labs robot. | |||||
| Ease of use | 10% | UX-How easily / Quikck has the configuration experience? Did he come with an intelligent house feature? Smartphone application? Voice assist? |
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 dry black rice and not cooked as a replacement for crumbs and sand as analogous for thinner 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.
Fresh wood floor test
Low battery carpet test
Pillar carpet test
These long -term shots on the head each show the track of a Roborock S7 Maxv Ultra because it cleanses our test room. We attach luminous sticks to the top of the cleaner directly above the vacuum intake to get an idea of the blanket that the vacuum offers and the way it browsing intelligently. In this case, the S7 Maxv Ultra is as complete and consistent as the robot vacuum cleaners.
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.
Now compare this to this next GIF, which shows you three races from the Irobot Romberba J7 Plus combo. Notice the difference? The Roomba was less effective to cover the entire room, missing the lower corner to the left in two of the three tracks, and it had a lot of difficulty providing an adequate blanket around the legs of this simulated catering table.
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. Roborock S8 Pro Ultra uses both technologies, which helps explain why it works so well here. Meanwhile, the Romberba relies alone on cameras and sensors, with lasers excluded from the mixture.
The Irobot Romberba J7 Plus held its promise to move away from the dog’s poop (false or other).
However, these cameras are really useful. Just look at the GIF above, which shows what happened when we put the Irobot Roomba J7 more to the test – in particular, its promise to identify and avoid waste for pets. With a variety of dog poop (I assure you, false) scattered in a small closed test floor, the Roomba did its best to vacuum without touching any. He succeeded, never coming up against any of our disgusting testing.
The Samsung Jetbot has constantly failed our test of solid animals waste. In each race, he would eventually pass or push one of our dog poop models.
Now compare this with the Samsung Jetbot Ai Plus, which also promises to use its cameras to locate and avoid pets. The result was not great; With each test, he would end up falling on one of our test batteries. Thank God, they were not real.
