The way a robot vacuum travels around the room as it cleans really matters.
Any robot vacuum cleaner will remove at least some dirt from your floors. How much ground it covers, though, and its behavior moving through rooms will vary a great deal from model to model.
The most important factor behind this is a robot's navigation system. Its navigation technology, together with software, determines a vacuum's actions. That plays a massive role in how well a given robot cleans a space, or even hunt for garden weeds. As you might expect, some robot vacuums perform the task better than others.
Read more: How to prep your house so your robot vacuum won't get stuck | The best robot vacuums of 2019: iRobot Roomba vs. Neato vs. the rest
Fortunately, our brand-new robot vacuum testing room at our warehouse lab in Louisville, Kentucky, can help us show the differences between robot vacuums, including how well they perceive, interact and otherwise move around in a physical space.
To break in our new test room, we ran nine current robot vacuum models across its floor.
There are three main types of systems robot vacuum cleaners typically use to navigate a space. The first is a simple collection of collision, wheel, brush and cliff sensors. They tell robots when they hit or are about to hit objects. With that information, they can slow down or change course altogether. Additionally these sensors help vacuums avoid falls down flights of stairs.
You tend to find these systems in budget robot vacuums. The upside is they cost a lot less than more complex machines. The 9 SharkNinja Ion S87 and 9 Eufy Robovac 11S are examples of products with this style of navigation.
Basic robot vacuums equipped with collision and proximity sensors, like the Eufy RoboVac 11S Max here, bounce around obstacles to find their way, but miss a lot of open floor space.
The downside is that they operate in a random fashion, bumping into things and veering willy-nilly around the room. The very first iRobot Roombas did the same. Sadly that results in incomplete floor coverage. Spots in tight places (corners, table and chair legs) get lots of repeat attention. Open areas, however, are likely vacuumed once (or perhaps not at all) since the robot travels in a straight line until it detects something in its path.
This image is designed to show the actual area the robot covered for cleaning. As you can tell, the Ecovacs Deebot 500 was very inconsistent.
These machines take a long time to run too, roughly three times as long as the most advanced robot vacuums need to attack the same area.
Sure, long clean times won't matter much if you tend to vacuum when nobody's home, and have all day to do it. When company is due to arrive in 45 minutes, or other time-limited situations, that's a problem.
Other robot vacuums combine the basic array of collision sensors with a main visual sensor that's augmented by a lens. These vacuums use a navigation algorithm called VSLAM (or visual simultaneous location and mapping). The optical system can identify landmarks on the ceiling, as well as judge the distance between walls.
The iRobot Roomba i7+ shows a more logical, thorough navigation path thanks to its optical technology.
VSLAM also calculates the vacuum's relative position in a room in real time, letting the bot create a map as it cleans. Robot vacuums that operate this way navigate a room with greater efficiency, systematically cleaning the floor in a logical pattern. They won't waste time vacuuming areas of a room the robot knows it has already travelled over. As a result, they can cover the same area in a shorter time, and with better coverage than a robot based only on physical sensors.
iRobot's current line of Roombas, such as the ,099 i7+ and ,299 S9+, have this kind of navigation system. The same is true of higher-end Ecovac models like the 9 Deebot 711.
That optical navigation translates to much more thorough coverage across our test room floor.
Visually driven robot vacuums have a few drawbacks. Since their optical sensors need at least some amount of ambient light present, they have trouble finding their way in completely dark rooms. Compared with basic models, you'll pay extra for these more intelligent robots too.
Another way robot vacuums can sense their environment is with lidar (light detection and ranging). It's the same sort of technology you'll find in many self-driving car prototypes such as those from Waymo and Uber. All Neato Botvacs use this method, including the 9 Botvac D7 Connected, the company's current flagship model.
Here's a view of the Neato Botvac D6 Connected using its laser LIDAR SLAM system in our test room.
Top-tier Ecovac Deebots like the 9 Ozmo 930 have built-in lidar too. In this sophisticated system, a turret-based laser mounted on the top of the robot vacuum illuminates objects to help the robot figure out their location and distance. Vacuums equipped with lidar can also detect the size and shape of things in their path.
Guided by lidar, the navigation pattern of Neato's Botvac D6 was very systematic, optimizing its pathing to get the job done completely, and in a short amount of time.
They actively scan their surroundings too. That's why these machines tend to cover floors with extreme efficiency For instance, both the Neato Botvac D7 and Botvac D6 cleaned our test room floor in just under 21 minutes.
The 9 SharkNinja Ion S87, with its basic navigation, spent 1 hour and 9 minutes cleaning the floor of our test room. Likewise, two budget Ecovacs machines, the 9 Deebot 500 and 9 Deebot 600, both had cleaning times of over 1 hour (60 and 64 minutes respectively). The longest though was the 9 Eufy RoboVac 11S Max (100 minutes, 34 seconds), also the cheapest model in this group.
Even with a pathing plan that looks sparse, the Neato Botvac D6 managed to cover essentially the entire test room floor.
Shorter runtime isn't the only benefit to lidar. Paired with the SLAM (or simultaneous location and mapping) algorithm, these robots also create detailed maps on the fly. You can perform useful interactions with those maps too. For instance, you can drop virtual boundaries within them, or make restricted zones at will for the robot to avoid. These vacuums also navigate in the dark if necessary. All that is great. Just remember you'll pay a premium for these machines. They typically occupy the ultra-high-end rung of the market.
A new approach a few robot vacuums take is to combine multiple navigate technology into one system. That includes brush, cliff, wheel and optical sensors, as well as laser emitters. There aren't that many products that do this at the moment.
One you can buy today is the 9 Electrolux Pure i9. This unique robot vacuum is equipped with a pair of front-firing lasers. Sitting in the middle of them, on the vacuum's front face, is also a big optical sensor behind a lens.
The Electrolux Pure i9, using a hybrid navigation and sensor system, definitely missed areas of our test room floor.
Even with all that tech, the Pure i9's movement through our test room appeared confused. It didn't roll along confidently like the Neato and Roomba machines. Instead it muddled through it in fits and starts, constantly pivoting in different directions.
The Electrolux Pure i9 uses a hybrid optical and laser navigation system. Even so, it often looked confused rolling across our test room floor.
With so many tools, as well as enhanced software and processing power, robots with hybrid navigation have the potential to offer unheard of levels of automation and intelligence. I think the upcoming Ecovacs Deebot 960 looks especially promising. Ecovacs says the vacuum will be able to actually identify objects like shoes, clothing and piles of toys.
Robot vacuums with hybrid sensor systems have promise. The Electrolux Pure i9 is one, but it didn't cover our test room floor as well as other machines.
And the company says the robot's AI-based recognition will learn new objects over time. Perhaps that list will include pet messes and other wet, goopy or sticky debris. That would be a welcome update, potentially saving your flooring and your carpet from becoming even messier than before the robot vacuum started cleaning.
We've conducted straight-line, cleaning performance-based tests for robot vacuums in the past, but that really only tells part of the picture about how well a robot vacuum will clean your home. How well it can navigate a space, how much area it actually covers and how long it takes are all important factors, too.
To help us capture that information, we built an industry-standard testing room, as specified by the International Electrotechnical Commission (IEC), the international standards body that, among other things, governs robot vacuum testing methods for manufacturers.
Inside our test room are objects and challenges designed to mimic what a robot will encounter as it cleans a room. That includes constructs designed to mimic large furniture like sofas or dressers, smaller objects like lamps or table and chair legs, and even surface irregularities like carpets, transitions between flooring and electrical cords.
A camera mounted above captures a bird's-eye view of all the action. From there we can figure out the path each vacuum takes during its cleaning cycle. This system also allows us to calculate how much of the floor a machine actually covers, and the time it takes to do it.
Look for more robot vacuum testing from us in the near future. For now, we can at least say conclusively that not all robot vacuums are the same, and the way a bot navigates around a room will impact not only its cleaning performance, but also how long it takes to get the job done.B:
2017年彩图“【上】【车】！【上】【车】！【快】【上】【车】！”【警】【察】【将】【一】【个】【个】【的】【妇】【女】、【儿】【童】，【送】【上】【了】【卡】【车】，【儿】【童】【很】【少】，【大】【部】【分】【都】【是】【妇】【女】。 【艾】【什】，【托】【瑞】【贝】【卡】【的】【关】【系】，【得】【以】【一】【起】【上】【了】【一】【辆】【卡】【车】。 【其】【他】【的】【青】【壮】【男】【子】，【甚】【至】【也】【有】【女】【人】，【他】【们】【手】【里】【拿】【着】**、【防】【爆】【盾】【牌】【这】【种】【近】【战】【武】【器】，【也】【都】【做】【好】【了】【准】【备】。 【不】【要】【小】【看】【这】【种】【装】【备】，【这】【可】【是】【专】【门】【对】【付】【暴】【徒】【的】【止】【暴】【制】
【看】【着】【风】【吹】【雪】【那】【厌】【恶】【的】【样】【子】，【易】【言】【真】【为】【她】【娘】【感】【到】【伤】【心】【啊】！ 【不】【过】【这】【也】【怪】【不】【了】【风】【吹】【雪】，【毕】【竟】【她】【也】【是】【被】【蒙】【在】【鼓】【里】【的】 【算】【了】，【赶】【紧】【把】【真】【相】【告】【诉】【她】【吧】 “【那】【个】【小】【雪】【你】【娘】【其】【实】【并】【没】【有】【将】【你】【抛】【弃】【你】【别】【生】【气】，【先】【听】【我】【说】【完】，【你】【先】【看】【看】【地】【上】【这】【俩】【人】【是】【谁】”【听】【到】【易】【言】【对】【自】【己】【的】【称】【呼】，【风】【吹】【雪】【差】【点】【又】【炸】【了】
【天】【空】【在】【某】【一】【瞬】【间】【突】【然】【亮】【如】【白】【昼】。 【强】【烈】【的】【光】【芒】【照】【下】，【极】【暗】【极】【亮】【的】【骤】【然】【变】【化】【让】【所】【有】【人】【都】【不】【禁】【闭】【上】【了】【眼】。 【姜】【渐】【离】【在】【合】【眼】【的】【瞬】【间】【运】【起】《【玄】【瞳】》，【而】【后】【将】【眼】【睛】【睁】【开】。 【然】【后】【他】【就】【看】【到】【从】【天】【上】【有】【无】【数】【根】【白】【色】【的】【线】【条】【落】【了】【下】【来】，【线】【条】【很】【细】，【细】【的】【让】【人】【怀】【疑】【会】【不】【会】【连】【一】【只】【蚍】【蜉】【挂】【在】【上】【面】【都】【能】【轻】【易】【的】【将】【其】【扯】【断】。 【其】【他】【人】【很】【快】【也】
【网】【友】【们】【的】【态】【度】【各】【异】，【置】【身】【其】【中】【的】【人】【反】【应】【也】【各】【不】【相】【同】。 【休】【息】【室】【里】，【白】【灵】【脸】【色】【惨】【白】【地】【瘫】【倒】，【然】【后】【猛】【地】【惊】【醒】【似】【地】，【慌】【张】【地】【四】【处】【摸】【手】【机】。 【摸】【到】【之】【后】，【拨】【通】【柳】【肃】【的】【电】【话】【一】【瞬】，【她】【仿】【佛】【抓】【住】【了】【最】【后】【一】【根】【救】【命】【稻】【草】，【双】【眼】【放】【光】【地】【喊】： “【柳】【总】！【救】【我】！【救】【我】！” 【妆】【容】【精】【致】【却】【形】【容】【狼】【狈】【的】【女】【子】【开】【始】【崩】【溃】【地】【大】【哭】：“【我】【求】【你】，2017年彩图【周】【六】【考】【试】，【很】【重】【要】！【周】【末】【恢】【复】【更】【新】
【谢】【谢】【包】【少】【爷】【送】【我】【的】【武】【器】…… 【那】【几】【个】【学】【员】【都】【诧】【异】【的】【看】【着】【他】【们】……【这】【包】【大】【少】【爷】【什】【么】【时】【候】【和】【叶】【家】【的】【那】【个】【废】【柴】【关】【系】【这】【么】【好】【了】，【不】【可】【能】【吧】，【难】【道】【包】【大】【少】【爷】【善】【心】【大】【发】【啦】……？ 【包】【文】【星】【现】【在】【那】【是】【一】【个】【心】【里】【苦】【啊】，【强】【装】【着】【笑】【脸】【说】【道】：【你】【拿】【回】【去】【好】【好】【用】【吧】，【改】【天】【有】【空】【我】【们】【一】【起】【吃】【饭】，【算】【是】【你】【感】【谢】【我】【的】…… 【等】【到】【叶】【修】【走】【后】，【留】【下】【的】
“【尹】【老】【师】，【你】【的】【这】【个】【问】【题】【不】【是】【多】【余】【的】【嘛】，【之】【前】……” 【樊】【尚】【伍】【似】【乎】【对】【于】【尹】【波】【的】【这】【一】【问】【题】【十】【分】【的】【不】【解】，【按】【照】【上】【一】【期】【节】【目】【录】【制】【时】，【这】【个】【哆】【啦】A【梦】【的】【表】【现】【来】【看】，【对】【方】【怎】【么】【可】【能】【不】【是】【专】【业】【歌】【手】【呢】？ 【对】【方】【演】【唱】【的】【那】【首】《【七】【友】》【感】【染】【力】【之】【强】，【直】【接】【让】【实】【力】【颇】【高】【的】【霸】【王】【龙】【都】【被】【淘】【汰】【掉】【了】，【如】【果】【不】【是】【专】【业】【歌】【手】【的】【话】，【这】【不】【是】【在】【开】【玩】【笑】
【面】【对】【这】【个】【发】【现】，【景】【辰】【的】【心】【情】【却】【异】【常】【平】【静】。 【以】【前】【在】【叶】【翕】【音】【身】【上】【所】【有】【的】【不】【解】【和】【疑】【团】，【因】【为】【这】【本】《【大】【明】【实】【录】》【全】【部】【都】【解】【开】【了】。 【这】【样】【的】【解】【释】，【反】【倒】【让】【景】【辰】【一】【颗】【原】【本】【疑】【虑】【重】【重】【的】【心】，【彻】【底】【安】【放】【下】【来】。 【只】【要】【叶】【翕】【音】【的】【身】【世】【与】【其】【他】【势】【力】【没】【有】【牵】【扯】，【其】【他】【的】【都】【不】【重】【要】。 【此】【刻】，【景】【辰】【只】【剩】【下】【对】【叶】【翕】【音】【浓】【浓】【的】【心】【疼】。 【如】【果】