Source: https://patents.google.com/patent/US20090194137A1/en
Timestamp: 2019-12-08 05:52:30
Document Index: 672498375

Matched Legal Cases: ['§ 119', 'art 400', 'art 500', 'art 600', 'art 700', 'art 800', 'art 900', 'art 1000', 'art 1100', 'art 1200', 'art 1300', 'art 300', 'art 1500']

US20090194137A1 - Service robot and method of operating same - Google Patents
US20090194137A1
US20090194137A1 US12/361,441 US36144109A US2009194137A1 US 20090194137 A1 US20090194137 A1 US 20090194137A1 US 36144109 A US36144109 A US 36144109A US 2009194137 A1 US2009194137 A1 US 2009194137A1
US12/361,441
US8838268B2 (en
SEEGRID OPERATING CORPORATION
2008-01-28 Priority to US2398008P priority Critical
2009-01-28 Priority to US12/361,441 priority patent/US8838268B2/en
2009-01-28 Application filed by Seegrid Corp filed Critical Seegrid Corp
2009-04-13 Assigned to SEEGRID CORPORATION reassignment SEEGRID CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: FRIEDMAN, SCOTT JUSTIN-MARL, MORAVEC, HANS PETER
2009-08-06 Publication of US20090194137A1 publication Critical patent/US20090194137A1/en
2014-09-16 Publication of US8838268B2 publication Critical patent/US8838268B2/en
2016-03-28 Assigned to SEEGRID OPERATING CORPORATION reassignment SEEGRID OPERATING CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SEEGRID CORPORATION
2016-06-08 Assigned to SEEGRID CORPORATION reassignment SEEGRID CORPORATION CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: SEEGRID OPERATING CORPORATION
238000004140 cleaning Methods 0 claims description 114
238000009421 internal insulation Methods 0 description 2
In accordance with aspects of the present invention, a service robot, such as a robotic cleaner, can be configured to more effectively service an environment. The service robot can include one or more sensors that sense its location, the location of objects, or both, and can also include noise reduction elements. The service robot can determine that it is under a “furnishing” and implement a different servicing pattern.
This application claims the benefit of priority under 35 U.S.C. § 119(e) from provisional application Ser. No. 61/024,011, entitled “METHODS FOR OBJECT, SURFACE, AND FUNCTION-SPECIFIC CLEANING FOR ROBOT CLEANERS,” filed on Jan. 28, 2008; from provisional application Ser. No. 61/023,980, entitled “METHOD FOR CLEANING UNDERNEATH FURNISHINGS FOR ROBOTIC CLEANER,” filed on Jan. 28, 2008; from provisional application Ser. No. 61/024,001, entitled “METHODS FOR IMPROVING ROBOTIC CLEANING VIA GLOBAL LOCATION AWARENESS,” filed on Jan. 28, 2008; from provisional application Ser. No. 61/024,022, entitled “METHODS AND APPARATUSES FOR NOISE REDUCTION FOR ROBOTIC CLEANERS,” filed Jan. 28, 2008; from provisional application Ser. No. 61/024,037, entitled “METHODS FOR ENVIRONMENTAL SCHEDULE AWARENESS FOR ROBOTS THAT SERVICE A FACILITY,” filed on Jan. 28, 2008, which is incorporated herein by reference in its entirety.
The present inventive concepts relate to methods for effectiveness and efficiency of robotic cleaners.
One of the advantages of robotic cleaners is that they can clean in places that are hard or impossible to clean manually. Many types of furnishings found in houses and commercial and industrial buildings provide a small amount of clearance under the furnishing. Examples include shelves, couches, and beds. These clearance spaces are often too small to clean easily manually, yet they are large enough to accumulate dirt and dust.
Previous attempts at providing robotic cleaners that can clean under furnishing have been limited in their flexibility, and also in their success. Most current commercial systems employ a “cleaning algorithms” technique. In this type of system, the cleaner moves in a single or a series of random or semi-random patterns around the floor. The pattern(s) are varied as too time, or other calculations the robot cleaner performs. However, in all cases, the goal of the cleaner is to optimize the cleaning of a larger space that happens to include the space under the furnishings.
These systems also have a design, such that the robot is able to fit into the very small under furnishing spaces, as well as passably clean the more open spaces of a building. The design compromises required to create a robotic cleaner that can both fit into the very small spaces under furnishings and also clean the open expanses of a building are daunting. Therefore, the efficiency and effectiveness of such systems is poor. For this reason, many systems have given up on this challenge entirely.
Efficiency and effectiveness of robot cleaners is typically low because the robots do not use techniques that make use of global location awareness. That is, they are generally unaware of their actual location. Previous service cleaning robots have ignored this problem, resulting in sub-optimal cleaning. Without a location context, the servicing robots are limited in terms of providing a series of tasks within an overall location, for example.
Moreover, current robot cleaners do not use what they learn as they do their job to improve their work. As an example, current robot cleaners will always clean when scheduled even when there is too much activity in the area to be able to clean at the scheduled time, resulting in a cleaning failure each time.
Also, that very action of servicing (e.g., cleaning) an environment or facility is a complex activity that can require a large fund of knowledge to perform adequately. Previous cleaning robots, as an example, have ignored this fund of knowledge problem. Instead, they tend to rely on generalized techniques to clean all different surfaces, objects, or functional areas of a facility.
Additionally, some service robots, such as robotic vacuums, can produce a relatively large amount of noise. Previous attempts at reducing noise in robotic cleaners have relied on traditional techniques that have been used to quiet manual vacuum cleaners. These include, but are not limited to: covering the working mechanisms with hard plastic shells, carefully designing moving parts to reduce noise production, and artificially limiting the strength of the cleaning mechanism to reduce the maximum noise levels products.
In accordance with one aspect of the invention, provided is a method of performing robotic cleaning. The method includes: navigating a cleaning robot through an environment using automatic self-control by the robot; sensing furnishings in the environment by the robot while navigating and cleaning; and avoiding contact with the furnishings based on sensor data acquired during the sensing.
The navigating can include the robot optimizing its migration through the environment based on the sensor data.
The optimizing can include changing a navigation pattern of the cleaning robot from a first pattern to a second pattern.
The sensing can include determining if the robot can fit underneath a furnishing, and the navigating can include migrating under the furnishing when the sensing determines that the robot can fit underneath the furnishing.
The sensing can further include using light sensing, including differentiating lower light intensity levels under the furnishing from higher light intensity levels in open spaces.
The sensing can further include upward facing height sensing, including differentiating a shorter un-occluded distance in an upward direction near the furnishing from a longer un-occluded distance in open spaces.
The sensing can further include using one or more of differentiating acoustic or electromagnetic qualities of under furnishing spaces from acoustic or electromagnetic qualities of open spaces.
The sensing can further include determining where open spaces are when robotic cleaner is underneath an object.
The robotic cleaner can be a robotic vacuum cleaner.
In accordance with another aspect of the invention, provided is a robotic cleaner configured to determine when it is under a furnishing. The robotic cleaner includes: a platform supporting a cleaning subsystem; a navigation controller coupled to a drive mechanism and configured to navigate the platform through an environment; and one or more sensors configured to sense objects in the environment during navigation and cleaning; wherein the navigation controller controls the drive mechanism to avoid contact with the objects based on sensor data acquired by the one or more sensors.
The robotic cleaner can have a height of six inches or less, and be optimized to fit under furnishings.
The navigation controller can be configured to optimize its migration through the environment based on the sensor data.
The navigation controller can be configured to optimize the migration of the cleaning robot by changing a navigation pattern of the cleaning robot from a first pattern to a second pattern.
The one or more sensors can be configured to determine if the cleaning robot can fit underneath an object, and the navigation controller can be configured to enable migration under the object when the one or more sensors determine that the robot can fit underneath the object.
The one or more sensors can include light sensors configured to differentiate lower light intensity levels under the object from higher light intensity levels in open spaces.
The one or more sensors can include upward facing height sensors configured to differentiate a shorter un-occluded distance in an upward direction near an object from a longer un-occluded distance in open spaces.
The one or more sensors can include one or more of acoustic and electromagnetic sensors configured to differentiate acoustic or electromagnetic qualities of under object spaces from acoustic or electromagnetic qualities of open spaces.
The one or more sensors can be configured to enable to the robotic cleaner to determine where open spaces are when robotic cleaner is underneath an object.
The robot cleaner can be a robotic vacuum cleaner.
In accordance with another aspect of the invention, provided is a method of performing robotic servicing using global location awareness. The method includes: storing in memory identifications of a plurality of locations within an environment; servicing the locations with a robot; and tracking a frequency of service of each location by the robot.
The method can further include the robot tailoring servicing routes to optimally service all locations.
The method can further include servicing high-use locations with greater frequency than lower-use locations.
The service robot can be a robotic cleaner.
The robotic cleaner can be a robotic vacuum.
The method can further include tracking dirt levels at each location and varying cleaning activity to clean high dirt level locations with increased frequency.
The method can further comprise: assigning functional labels to each location; and associating locations with specific events for times of the day, after which cleaning should be performed in those locations.
In accordance with another aspect of the invention, provided is a service robot configured to utilize global location information. The service robot includes: a platform supporting a service subsystem; a navigation controller coupled to a drive mechanism and configured to navigate the platform through an environment; and a data storage system that stores identifications of a plurality of locations within the environment; a navigation controller configured to track a frequency of service of each location by the robot.
The navigation controller can be further configured to tailor servicing routes to enable the robotic cleaner to optimally service all locations.
The robot can further comprise one or more sensors configured to sense levels of dirt.
The navigation controller can be further configured to track dirt levels at each location and vary cleaning activity to clean high dirt level locations with increased frequency.
The navigation controller can be further configured to: assign functional labels to each location; and associate locations with specific events for times of the day, after which cleaning should be performed in those locations.
In accordance with aspects of the invention, provided is a robotic cleaner and method that allows superior reduction in the noise of robotic cleaners.
The robotic vacuum cleaner can include layers of sound dampening materials that are incorporated into the robot.
The robotic cleaner can include placement of microphones, speakers, and electronic computation and circuitry to provide for active noise reduction of the sounds of the robotic cleaner.
The robotic cleaner can include acoustic muffling chambers that are incorporated into the robot.
In accordance with aspects of the present invention, provided are a robot and methods that enable the robot to service a facility optimally by making use of an environmental schedule of the facility.
The robot can be configured to automatically and selectively operate in view of an environmental schedule of a facility, and to make the schedule.
The robot can be configured to perform a people census, whereby through the robot's own sensing, or through receiving such information from an external source, the robot learns where people are likely, and unlikely to be at various times of the day, and uses a statistical model of likelihood to work as much as possible when and where people are not present.
The robot can be configured to perform a people census, whereby real-time data is used to trigger or queue the robot to begin or end work. For instance, a sensor recording when the car leaves the garage or when people leave a room, can be used to trigger the robot to begin work.
The robot can be configured to analyze a digitized calendar of appointments of people, and to form a plan to work when people are scheduled to not be present according to the calendar.
The robot can be configured to predicatively calculate the likelihood of a person returning at any given time, so that the robot can stop working before the person arrives.
In accordance with aspects of the present invention, provided is a robot and method that allows a robot to clean a facility by providing access to cleaning knowledge that is specific to the surface or object being cleaned, or to the area of the facility being cleaned. Methods in accordance therewith can enable the robot to optimally service a facility.
A knowledgebase can be included in the robot, whereby when the robot senses a specific type of object, surface, or area to be cleaned, the robot queries an internal knowledgebase for specific cleaning techniques for cleaning the object or surface.
The knowledgebase can be a knowledgebase of techniques identified as able to work well for cleaning such object, surface or area.
A library of learned behaviors can be included in the robot, whereby when the robot senses a specific type of object, surface, or area to be cleaned, the robot queries an internal library of learned behaviors for specific cleaning techniques for cleaning the object or surface.
The library can be a library to behaviors that have worked well in the past for cleaning such object, surface or area.
A remote knowledgebase can be provided, whereby when the robot senses a specific type of object, surface, or area to be cleaned it queries a remote knowledgebase or bases for specific cleaning techniques for cleaning the object or surface to be cleaned.
The remote knowledge base could be created and/or provided by trusted sources, such as the manufacturers of the artifacts to be cleaned.
A remote library of learned behaviors could be provided, whereby when the robot senses a specific type of object, surface, or area to be cleaned it queries an remote library of learned behaviors for specific cleaning techniques for cleaning the object or surface to be cleaned.
The remote library can be a library to behaviors that have worked well in the past for cleaning such object, surface or area.
FIG. 1 shows an embodiment of a service robot including top mounted sensor, in accordance with aspects of the present invention.
FIG. 2 shows an embodiment of a service robot including multiple sensors, in accordance with aspects of the present invention.
FIG. 3A shows a block diagram of an embodiment of a service robot, in accordance with aspects of the present invention.
FIG. 3B is a cross section of an embodiment of a robotic cleaner with passive acoustic dampening.
FIG. 3C is a cross section of an embodiment of a robotic cleaner with active acoustic dampening.
FIG. 3D is a cross section of an embodiment of a robotic cleaner with an acoustic muffling chamber.
FIG. 4 is a flowchart that illustrates an embodiment of a method for servicing an environment by a service robot, according to aspects of the present invention.
FIG. 5 is a flowchart that illustrates an embodiment of a method for ending a servicing activity of a service robot, in accordance with aspects of the present invention.
FIGS. 6 and 7 are flowcharts illustrating embodiments of a location awareness method used by a service robot, in accordance with aspects of the present invention.
FIGS. 8-11 are flowcharts depicting embodiments of methods for environmental schedule awareness for robots that service an environment.
FIGS. 12-15 are flowcharts of embodiments of methods for object, surface, and function-specific service for a service robot.
FIG. 1 shows an embodiment of a service robot 100 that can be used to service an environment, e.g., a facility. In this embodiment, the service robot can include an upward facing sensor 110, shown in a top surface of the service robot 100. The sensor 110 can be used to determine whether the robotic cleaner 300 is under a furnishing or in an open space, as examples. Upward facing sensor can be any one or more of a variety of types of sensors, e.g., such as a photosensor, an acoustic sensor, and infrared sensor. As an example, a properly configured photosensor can be configured to detect light intensity differences between open spaces and covered spaces—such as under a furnishing. In such a case, detection of a significant drop in light intensity could indicate that the service robot had moved under a furnishing. Conversely, an increase in light intensity could indicate that the service robot moved out from underneath a furnishing.
FIG. 2 shows an embodiment of a service robot cleaner 200 with multiple sensors 210. Such multiple 210 sensors could be used to allow the service robot 200 to determine a direction of its movement. For example, assuming photosensors, if the robotic cleaner is moving under a furnishing, the first detector(s) that are under the furnishing will detect less light than those still in the open space—at that point in time. Then the next detector under the furnishing will detect less light and so on. That data enables determination of the direction of the robotic cleaner relative to the furnishing and open space. A similar analysis can be performed when moving from underneath the furnishing into the open space.
FIG. 3A shows a block diagram of an embodiment of a service robot 300, such as those described in FIGS. 1 and 2 above. As an example, the service robot 300 could be a wireless robotic vacuum. The service robot 300 can be configured to implement the various methods described herein below. In some embodiments, one or more external sensors could be located within the environment, in addition to or as an alternative to, sensors mounted on the robot. In such a case the service robot could interact with the external sensors or a system that communicates with the external sensors, e.g., over a wireless network.
In this embodiment, the service robot 300 includes a platform supporting a servicing subsystem 306 and a navigation controller 302 coupled to a drive mechanism (not shown) and configured to navigate the platform through an environment. The service robot also includes one or more sensors (as shown in FIGS. 1 and 2). Sensor data acquired from the sensor(s) can be stored in a storage media 304, e.g., a hard drive, random access memory, read only memory, or some combination thereof. Other data and program modules can also be stored in storage media 304. The program modules can be executable by at least one processor to implement methods, or portions thereof, described herein. Functional modules need not be implemented in executable program code, however. One or more functional modules could, additionally or alternatively, be implemented in hardware, firmware, or both, as examples.
The storage media 304 can also include maps of the environment, or other information useful or necessary for migrating the service robot through the environment. In various embodiments, the service robot could also build, update, or both maps of the environment. Such map building and navigation could be done as described in U.S. Pat. No. 7,446,766 entitled “Multidimensional Grids and Systems and Methods for Applying Same,” and/or U.S. patent application Ser. No. 12/263,983 filed Nov. 3, 2008, entitled “Multidimensional Grids and Systems and Methods for Applying Same.” The storage media 304 can, in various embodiments, be used to store robot location information determined using the sensor data.
In various embodiments the navigation controller 302 can be configured to control the drive mechanism to avoid contact with furnishing (e.g., object) in the environment based on sensor data acquired by the one or more sensors.
In various embodiments, the service robot can also include a communications module 308, which could enable wired or wireless communications with a control system, other robots, or other wireless devices (e.g., personal digital assistant, cell phone, wireless laptop, etc.).
In various embodiments, e.g., where the service robot is a robotic cleaner, the servicing subsystem 306 can include a dirt detector. The dirt detector can be used to track the amount of dirt taken in at a location. This information could be stored and used to determine a schedule of cleaning for various places within the environment.
In various embodiments the service robot can include one or more types of acoustic dampening mechanism, as discussed in the examples of FIGS. 3B-3D.
FIG. 3B is a cross section of an embodiment of service robot 300 with passive acoustic dampening, such as a robotic cleaner. In FIG. 3B the robot and cleaning mechanisms are shown as block 312. These can be as known in the art, or as hereafter developed. A passive acoustic dampening material is provided to substantially encase or cover the servicing subsystem 306, e.g., a cleaning mechanisms. In this embodiment, the bottom portion of the service robot is not covered by the passive acoustic dampening material, allowing for exposure of translation mechanisms disposed there. However, in other embodiments, passive acoustic dampening material could also be included on the bottom portion.
In various embodiments, passive acoustic dampening materials can include typical materials suited for such purposes, such as various types and configurations of foam.
FIG. 3C is a cross section of an embodiment of service robot 300 with active acoustic dampening. In this embodiment, the active acoustic dampening includes a set of microphones 322, a set of speakers 324, and acoustic cancelling functionality 326. The acoustic cancelling functionality receives inputs from the microphones 322 and outputs noise cancelling tones via speakers 324.
FIG. 3D is a cross section of an embodiment of service robot 300 with an acoustic muffling chamber 332 formed therein. In this embodiment, the muffling chamber 332 is formed around the servicing subsystem (not shown) disposed therein, which muffles the noise therefrom. The muffling chamber 332 is formed between an outer shell 334 and internal insulation material 336. The internal insulation material is preferably contoured to enhance noise muffling properties.
In accordance with aspects of the present invention, service robot 300 can be a robotic cleaner configured to implement a method of more effectively cleaning a space that includes furnishings, for example. The cleaner and method can determine that the cleaner is under a “furnishing” and implement a different cleaning pattern as a function thereof. The determination that the robotic cleaner is under a furnishing can be accomplished using any one or more of a plurality of types of sensors mounted on, integral with, or coupled to the robotic cleaner.
FIG. 4 is a flowchart 400 that illustrates an embodiment of a method for a service robot (e.g., a robotic vacuum cleaner) according to aspects of the present invention. The service robot can be configured to sense objects in the environment during navigation and servicing.
In this embodiment, fur illustrative purposes, it is presumed that the service robot is a robotic cleaner. In step 402 the robotic cleaner begins cleaning. In step 404 the robotic cleaner determines whether it is under a “furnishing.” As used herein, the word “furnishing” can mean any physical item or object located in a space (or environment) being cleaned. It need not be limited to furniture, for example.
In the present embodiment the robotic cleaner presumes that it is not under a furnishing unless it senses, or otherwise determines, that it is under a furnishing. If in step 404 the robotic cleaner determined that it was not under a furnishing, the robotic cleaner continues to clean the space using a general cleaning pattern, in step 406. This general cleaning pattern can be any of those used in currently available robotic cleaners.
However, if in step 404 the robotic cleaner determined that it was under a furnishing, the process would continue to step 408, where the robotic cleaner would begin cleaning with a special pattern. The special pattern would be tailored for better cleaning under a furnishing. For example, the special cleaning pattern can be tighter, since it is designed to be effective in a smaller footprint. In the preferred embodiment, when the robotic cleaner senses that it is moving out from underneath the furnishing, it turns back the other way so that it stays underneath the furnishing until cleaning is complete.
Ultimately, the process can move to step 410 where a determination can be made of whether or not the robotic cleaner is done cleaning the space. If not, the process can revert to step 402. If so, the cleaner can exit the cleaning activity in step 412.
It will be understood by those skilled in the art, that the monitoring for whether the robotic cleaner is under a furnishing can be substantially constant, since the sensors can be in a sensing mode throughout the use of the robotic cleaner, periodic, or otherwise intermittent. Thus, in some embodiment, the sensed condition of moving under a furnishing can serve as an interrupt to the mode of the general, open space cleaning. The interrupt can cause the robotic cleaner to begin cleaning with the special pattern.
FIG. 5 is a flowchart 500 that illustrates an embodiment of a method for ending the above cleaning activity underneath a furnishing. In various embodiments, a timer can be used to measure the time that the robotic cleaner is in the special cleaning pattern. In step 502, when a predetermined time has elapsed the robotic cleaner can begin transitioning to an exit pattern in step 504. However, before completing the exit pattern, in step 506 the robotic cleaner can determine whether the robotic cleaner is still under the furnishing. If the answer is “no,” the robotic cleaner resumes, in step 208, the exit pattern in step 504. If the answer is “yes,” the robotic cleaner ends the exit pattern in step 510.
In other embodiments, the determination method may be based on upward facing height sensing, as the un-occluded distance in the upward direction is much shorter than in open spaces. In still other embodiments, the determination may be based on other perceptual techniques such as acoustics, or electromagnetic qualities of such under furnishing environments. In still other embodiments, the determination method may be based on a combination thereof.
In various embodiments, a robotic cleaner, such as those in FIGS. 1, 2 and 3, can be a low profile robotic cleaner. For example, the robotic cleaner can be less than 6 inches in height, and preferably about 2 inches or less in height.
FIG. 6 is a flowchart 600 of an embodiment of a method for controlling a service robot with location awareness. In this embodiment the service robot is a cleaning robot, such as a robotic vacuum.
In step 602 the robot is in the process of servicing (e.g., cleaning) a location (or area), e.g., a room. In step 604 the robot records completion of the cleaning and the location in a database. In step 606 the robot searches the database to determine the next location to clean. In this embodiment there is a presumption that the location for which the longest time has elapsed since the last cleaning should be the next location cleaned. In step 608, the robot navigates to the next location to be cleaned.
Note that in some embodiments the robot can include the database. However, in other embodiments the robot could communicate over a wireless network with a database system within which such information can be stored.
FIG. 7 is a flowchart 700 of another embodiment of a method for controlling a service robot with location awareness and dirt level awareness. In this method the robot not only includes sensors and related mechanisms to determine its location within a facility, but also includes one or more sensors to record a level of dirt associated with each location, as discussed above. The dirt level can be used as a parameter in determining the frequency of cleaning of different locations within the facility. High dirt level locations can be cleaned more frequently than low dirt level locations.
In step 702 the robot is cleaning in a location. In step 704 the robot records completion of the cleaning, including associating the date and time of cleaning in the database with location. The robot also senses and records the amount of dirt taken in when cleaning the location. In step 706 an algorithm is run to determine the next location to be cleaned. The algorithm takes into account the last time the location was cleaned and the dirt level associated with that location. In step 708 the robot navigates to a new location, to begin cleaning that location.
In other embodiments, the robot need not be configured with dirt sensors. However, the robot could be preprogrammed with to clean certain locations with higher frequency than other locations. For example, in an office building the highest traffic locations could be cleaned more frequently than other locations, or public locations could be cleaned more frequently than private locations.
As will be appreciated by those skilled in the art, various devices and approaches can be used to configure the robot with location awareness. For example, the robot could have a map stored therein and track its own movements relative to the map. The robot could be navigated around the facility to acquire the map—called “training.” The robot could navigate using markers or the like. As another example, the robot can be configured to track its movements within a wireless cellular network, e.g., using signal strength measuring techniques at access points distributed throughout the locations to be serviced. In short, the present invention is not limited with respect to possible location tracking and navigation approaches.
FIG. 8 is a flowchart 800 of an embodiment of a method for controlling a service robot using people census information. In this embodiment the service robot is a cleaning robot, such as a robotic vacuum.
In step 802 people census data is accumulated, which can happen in at least two ways. In step 804 a the robot accumulates people census data through its own observation. For example, the robot can be configured with sensors that can sense the presence of people, e.g., motion detectors, acoustic sensors, cameras, light sensor, and so on, or some combination thereof. The robot can be configured to make observations over time, collecting data over multiple uses. In step 804 b observation data is provided to the robot. In other embodiments, the robot could accumulate observation data using a combination of direct observation 804 a and indirect observation 804 b.
In step 806 statistical modeling can be performed using the accumulated people census data. The modeling can be performed by the robot, or in other embodiments the model can be downloaded to the robot. The model can indicate times and locations of people in the facility and/or areas therein to be serviced. In step 808 a determination of best times for the robot to service various locations is made. In step 810 a service plan or schedule is made based on the determination in step 108. In step 812 the service schedule is executed.
The service schedule can be updated over time based on subsequently collected people census data or other relevant information. In one embodiment, the robot is configured to collect such people census information and to continually or periodically updated the statistical mode and service schedule.
FIG. 9 is a flowchart 900 of another embodiment of a method for controlling a service robot. In this method when people have left the location the robot commences servicing the area.
In step 902 the robot is at rest at a location. In step 904 the robot receives information or data, e.g., a “trigger,” that indicates that people have left the location. In step 906 the robot commences servicing the location in response to the trigger. For example, when a room becomes vacated a robotic cleaner can begin to vacuum the room. The trigger could be externally provided or could be the product of one or more sensors included in the robot, or a combination thereof.
FIG. 10 is a flowchart 1000 of another embodiment of a method for controlling a service robot using digital calendars. In this method the robot creates or tailors a work schedule based on the schedules of people in the facility to be serviced by the robot.
In step 1002 the robot queries digital calendars of facility people, e.g., as in a Microsoft Outlook™ calendar database. This can either be a pull-query or a push query, or a combination thereof. In any case, the scheduling data for people in the facility is accumulated. The scheduling data can also include resource scheduling data. For example, if a room, such as cafeteria, is closed or unscheduled from 2 pm to 5 pm, the information could be included. In step 1004 the robot's work schedule (or plan) is created and/or optimized based on such scheduling data. For example, if a cafeteria is closed from 2 pm to 5 pm the robot could schedule cleaning of that room during that time; if a conference is not booked for an during a particular day, the robot could vacuum that conference room during that time; if a building entrance is closed on weekends the robot could schedule cleaning on the weekend. Additionally, if a group of people working in the same location are scheduled to be at a meeting at a certain time, the robot could schedule cleaning of that location during the time when the people were at the meeting. As another example, if an individual was scheduled to be on vacation for a particular day, the robot could schedule the cleaning of that person's office on that day. In step 1006 the schedule is executed.
FIG. 11 is a flowchart 1100 of another embodiment of a method for controlling a service robot using people census information. In step 1102 people census data is accumulated, e.g., as described with respect to steps 804 a and 804 b of FIG. 8. In step 1104 statistical modeling can be performed using the accumulated people census data. The modeling can be performed by the robot, or in other embodiments the model can be downloaded to the robot. The model can indicate times and locations of people in the facility and/or areas therein to be serviced.
In step 1106 likely return times of people can be determined. For example, the model could show that some people leave for lunch at 11:30 am, and it could be predicted that those people will return by 1 pm. In step 1108 a robot service schedule or plan is created and executed by the robot. In step 1110 the robot stops work before the likely return of people, based on the determination in step 1106.
If the robot is interrupted it can stop and wait until the person or people leave. The robot can be configured to resume servicing the location thereafter.
FIG. 12 is a flowchart 1200 of an embodiment of a method for controlling a service robot using onboard knowledgebase information. In this embodiment the service robot is a cleaning robot, such as a robotic vacuum. In this embodiment, the method presumes that the robot is in the act of cleaning.
In step 1202 the robot identifies an object or surface to be cleaned. For example, an object could be a machine, piece of furniture, or other item. A surface could be glass, hardwood flooring, carpeting, cement, and so on, as just a few examples. In step 1204 the robot queries an onboard knowledgebase, which has information relating to object and/or surface specific cleaning techniques (or procedures) stored therein. Based on the object or surface identified in step 1202, the robot determines the appropriate cleaning technique in step 1204. In step 1206 the robot applies the technique to clean the object or surface. Upon completion, the robot continues cleaning using general cleaning techniques in step 1208, until another object or surface is identified in step 1202.
FIG. 13 is a flowchart 1300 of an embodiment of a method for controlling a service robot using onboard library of object and/or surface techniques and procedures. In this embodiment the service robot is a cleaning robot, such as a robotic vacuum. The method presumes that the robot is in the act of cleaning.
In step 1302 the robot identifies an object or surface to be cleaned. In step 1304 the robot queries an onboard library of object and/or surface specific cleaning techniques (or procedures). Based on the object or surface identified in step 1302, the robot chooses the appropriate cleaning technique in step 1304. In step 1306 the robot uses the technique to clean the object or surface. Upon completion, the robot continues cleaning using general cleaning techniques in step 1308, until another object or surface is identified in step 1302.
FIG. 14 is a flowchart 300 of an embodiment of a method for controlling a service robot using a remotely accessible knowledgebase information. In this embodiment the service robot is a cleaning robot, such as a robotic vacuum. The method presumes that the robot is in the act of cleaning.
In step 1402 the robot identifies an object or surface to be cleaned. For example, an object could be a machine, piece of furniture, or other item. In step 1404 the robot queries a remote knowledgebase, which has information relating to object and/or surface specific cleaning techniques (or procedures) stored therein. Based on the object or surface identified in step 302, the robot determines the appropriate cleaning technique in step 1404. In step 1406 the robot applies the technique to clean the object or surface. Upon completion, the robot continues cleaning using general cleaning techniques in step 1408, until another object or surface is identified in step 1402.
FIG. 15 is a flowchart 1500 of an embodiment of a method for controlling a service robot using a remotely accessible library of object and/or surface techniques and procedures. In this embodiment the service robot is a cleaning robot, such as a robotic vacuum. The method presumes that the robot is in the act of cleaning.
In step 1502 the robot identifies an object or surface to be cleaned. In step 1504 the robot queries the remote library of object and/or surface specific cleaning techniques (or procedures). Based on the object or surface identified in step 1502, the robot chooses the appropriate cleaning technique in step 1504. In step 1506 the robot uses the technique to clean the object or surface. Upon completion, the robot continues cleaning using general cleaning techniques in step 1508, until another object or surface is identified in step 1502.
Those skilled in the art will appreciate that a service robot could implement any combination of the above described methods.
As will be appreciated by those skilled in the art, a service robot (e.g., vacuum) can include a memory for storing instructions and data, and a processor for executing the instructions. Thus, the methods discussed above can be programmed into the service robot for execution to accomplish the functions disclosed herein.
Also, while the service robot was indicated as a robotic cleaner in this embodiment, those skilled in the art will appreciate that methods in accordance with the present invention could be applied to any number of service robots, and could implement any number and types of sensors.
While the foregoing has described what are considered to be the best mode and/or other preferred embodiments, it is understood that various modifications can be made therein and that the invention or inventions may be implemented in various forms and embodiments, and that they may be applied in numerous applications, only some of which have been described herein.
1. A method of performing robotic cleaning, the method comprising:
navigating a cleaning robot through an environment using automatic self-control by the robot;
sensing furnishings in the environment by the robot while navigating and cleaning; and
avoiding contact with the furnishings based on sensor data acquired during the sensing.
2. The method of claim 1, wherein navigating includes the robot optimizing its migration through the environment based on the sensor data.
3. The method of claim 1, wherein the optimizing includes changing a navigation pattern of the cleaning robot from a first pattern to a second pattern.
8. The method of claim 4, wherein sensing further includes determining where open spaces are when robotic cleaner is underneath an object.
9. The method of claim 1, wherein the robot cleaner is a robotic vacuum cleaner.
10. A robotic cleaner configured to determine when it is under a furnishing, the robotic cleaner comprising:
a platform supporting a cleaning subsystem;
a navigation controller coupled to a drive mechanism and configured to navigate the platform through an environment; and
one or more sensors configured to sense objects in the environment during navigation and cleaning;
wherein the navigation controller controls the drive mechanism to avoid contact with the objects based on sensor data acquired by the one or more sensors.
20. A method of performing robotic servicing using global location awareness, the method comprising:
storing in memory identifications of a plurality of locations within an environment;
servicing the locations with a robot; and
tracking a frequency of service of each location by the robot.
22. The method of claim 21, further including servicing high-use locations with greater frequency than lower-use locations.
assigning functional labels to each location; and
associating locations with specific events for times of the day, after which cleaning should be performed in those locations.
27. A service robot configured to utilize global location information, the service robot comprising:
a platform supporting a service subsystem;
a data storage system that stores identifications of a plurality of locations within the environment;
a navigation controller configured to track a frequency of service of each location by the robot.
32. The robot of claim 29, wherein the navigation controller is further configured to:
assign functional labels to each location; and
associate locations with specific events for times of the day, after which cleaning should be performed in those locations.
33. A robotic cleaner with acoustic dampening, the robotic cleaner comprising:
one or more acoustic dampening devices configured to reduce noise produced by the robot.
35. A robotic cleaner adaptable to specific surfaces and objects, the robotic cleaner comprising:
one or more sensors configured to determine the presence of a surface, object, or area, wherein the cleaning subsystem is configured to utilize information guiding preferred cleaning of the surface, object, or area.
39. A service robot configured to use an environmental schedule of a facility, the service robot comprising:
a platform supporting a service subsystem; and
a navigation controller coupled to a drive mechanism and configured to navigate the platform through an environment,
wherein the servicing subsystem is configured to access a stored environmental schedule of the facility and to tailor servicing of the facility based on the environmental schedule.
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