Abstract: In one embodiment, a user indicates one or more virtual zones on an area map on a user device for a particular robot. The zones are then transferred to the robot. The robot determines an optimum order for multiple zones and a path for navigating to the different zones.

Abstract: In one embodiment, a system and method is provided for optimizing the total time taken for cleaning an area by a cleaning robot, which is the sum of the cleaning time and the time taken for the robot to re-charge its battery mid-cleaning when the area is too large to clean on a single charge. In one embodiment, the remaining area to be cleaned is determined, and the mid-cleaning re-charge time is reduced to the amount of charge needed to clean the remaining area, plus a buffer. Hence, a reduction in mid-cleaning charging time results in reduced total time taken to clean a given space.

Abstract: In one embodiment, a cleaning robot stores multiple floorplans. The robot automatically determines which floorplan it is in by localizing and trying to match its detected environment to the stored map. The best fit is the map for the current floor. If there is no match above a confidence threshold, the robot assumes it is a new floor or the floorplan has changed (e.g., furniture has moved), and the robot initiates a discovery mode to map the floorplan and add it to the robot's memory.

Abstract: Embodiments provide a plurality of personalized, user selectable styles. Each style is determined by a combination of dimensions. Each style, except the default balanced style, prioritizes one of the dimensions over the other cleaning dimensions. In one embodiment, the styles are cleaning styles and the cleaning dimensions are cleaning time, coverage and obstacle avoidance. The cleaning styles are gentle, fast, thorough and balanced. The gentle style protects expensive furniture, with obstacle avoidance prioritized over cleaning time and coverage. The fast style cleans quickly, prioritizing cleaning time over coverage and obstacle avoidance. The thorough style prioritizes coverage over cleaning time and obstacle avoidance. The balanced style prioritizes all three cleaning dimensions substantially the same. The prioritization is achieved through adjustments to the autonomous robot navigation. User selection of a style can be achieved directly or automatically based on various mechanisms, such as user history.

Abstract: This disclosure describes various methods and apparatus for detecting and characterizing debris pickup during a cleaning operation. A debris detection sensor is described capable of counting the number of particles retrieved by a robotic vacuum during the cleaning operation and associating particles identified with particular areas. The location information can be obtained using various sensors on-board the robotic vacuum. In some embodiments, a cleaning operation can be rerouted when senor readings from the debris detection sensor deviate from historical sensor readings by a predetermined amount.

Abstract: In one embodiment, a method is disclosed for a robot (e.g., cleaning robot) to produce a bird's eye view (planar) map by transforming from a robot camera view and stitching together images tagged by location. The planar map can then be presented on a user interface as a floorplan, showing the location of objects (such as rugs). A camera is mounted in the cleaning robot sufficiently high in the cleaning robot housing to provide an angled view downward at a floor. The camera's field of view is captured as an image, and a portion or segment of that image is cropped. The cropped segment is transformed from the camera perspective to a planar view (before or after cropping), and is combined with other images to form a map of a floorplan.

Abstract: In one embodiment, a virtual boundary is provided in the global coordinates of the area map and is converted into a plurality of line segments corresponding to a plurality of partial maps. In one embodiment, a physical boundary indicator is used during a training/mapping run, with the location added to the area map and the physical boundary indicator later moved. In one embodiment, the virtual boundary changes over time to change cleaning areas, act as a gate, change associated cleaning mode, etc. In one embodiment, virtual areas with boundaries are selected by a user.

Abstract: The present disclosure describes techniques for a robotic cleaning device to determine a plan to clean an environment based on types of surfaces in the environment. The plan can include a route to take in the environment and/or one or more configurations to apply to the robotic cleaning device during the route. Determining the plan can include inserting detected surface types into an environmental map of the environment. The environmental map can then be used on future routes such that the robotic cleaning device can know a surface type of a surface before the robotic cleaning device reaches the surface. In some examples, a type of a surface of the environment can be detected by the robotic cleaning device based on optical polarization of light.

Abstract: Embodiments provide a plurality of personalized, user selectable styles. Each style is determined by a combination of dimensions. Each style, except the default balanced style, prioritizes one of the dimensions over the other cleaning dimensions. In one embodiment, the styles are cleaning styles and the cleaning dimensions are cleaning time, coverage and obstacle avoidance. The cleaning styles are gentle, fast, thorough and balanced. The gentle style protects expensive furniture, with obstacle avoidance prioritized over cleaning time and coverage. The fast style cleans quickly, prioritizing cleaning time over coverage and obstacle avoidance. The thorough style prioritizes coverage over cleaning time and obstacle avoidance. The balanced style prioritizes all three cleaning dimensions substantially the same. The prioritization is achieved through adjustments to the autonomous robot navigation. User selection of a style can be achieved directly or automatically based on various mechanisms, such as user history.

Abstract: A robotic surface treatment apparatus treats corners of rooms more effectively through intricate guidance of the apparatus through inside and outside corners. In one aspect, contact and/or non-contact sensors provide information to one or more on-board processors on the apparatus to enable selective overriding of obstacle avoidance program code and allow the apparatus to get closer to walls to facilitate treatment. In another aspect, the sensors provide information to the on-board processors to control backup motion of the apparatus to cover previously-missed areas when turning corners. In yet another aspect, the apparatus is shaped to have its treatment mechanism positioned more closely to the front of the apparatus to enable treatment more closely to walls near corners. In one embodiment, the robotic surface treatment apparatus is a robotic vacuum. The vacuum may have its cleaning brush positioned near a flat front portion of the apparatus.

Abstract: A robotic cleaning device and method of control thereof. The device and method enable multiple cleaning passes within an environment efficiently, cleaning the entire environment more than once, or cleaning the entire environment and then focusing on one or more particularly dirty areas to provide enhanced cleaning by making one or more passes over a dirty area in a dominant and a non-dominant direction. The robotic cleaning device may identify such areas in the course of its cleaning, or it may record such areas previously as being historically dirty and requiring additional attention. The device may vacuum; it may shampoo; it may polish; or it may perform other cleaning operations. Where the device is battery-powered, the device may check its state of charge before performing the enhanced cleaning, and may repeat the enhanced cleaning if the battery or batteries have sufficient charge.

Abstract: Techniques that optimize performance of simultaneous localization and mapping (SLAM) processes for mobile devices, typically a mobile robot. In one embodiment, erroneous particles are introduced to the particle filtering process of localization. Monitoring the weights of the erroneous particles relative to the particles maintained for SLAM provides a verification that the robot is localized and detection that it is no longer localized. In another embodiment, cell-based grid mapping of a mobile robot's environment also monitors cells for changes in their probability of occupancy. Cells with a changing occupancy probability are marked as dynamic and updating of such cells to the map is suspended or modified until their individual occupancy probabilities have stabilized.

Abstract: Techniques that optimize performance of simultaneous localization and mapping (SLAM) processes for mobile devices, typically a mobile robot. In one embodiment, erroneous particles are introduced to the particle filtering process of localization. Monitoring the weights of the erroneous particles relative to the particles maintained for SLAM provides a verification that the robot is localized and detection that it is no longer localized. In another embodiment, cell-based grid mapping of a mobile robot's environment also monitors cells for changes in their probability of occupancy. Cells with a changing occupancy probability are marked as dynamic and updating of such cells to the map is suspended or modified until their individual occupancy probabilities have stabilized.

Abstract: A robotic surface treatment apparatus treats corners of rooms more effectively through intricate guidance of the apparatus through inside and outside corners. In one aspect, contact and/or non-contact sensors provide information to one or more on-board processors on the apparatus to enable selective overriding of obstacle avoidance program code and allow the apparatus to get closer to walls to facilitate treatment. In another aspect, the sensors provide information to the on-board processors to control backup motion of the apparatus to cover previously-missed areas when turning corners. In yet another aspect, the apparatus is shaped to have its treatment mechanism positioned more closely to the front of the apparatus to enable treatment more closely to walls near corners. In one embodiment, the robotic surface treatment apparatus is a robotic vacuum. The vacuum may have its cleaning brush positioned near a flat front portion of the apparatus.

Abstract: A robotic surface treatment apparatus treats corners of rooms more effectively through intricate guidance of the apparatus through inside and outside corners. In one aspect, contact and/or non-contact sensors provide information to one or more on-board processors on the apparatus to enable selective overriding of obstacle avoidance program code and allow the apparatus to get closer to walls to facilitate treatment. In another aspect, the sensors provide information to the on-board processors to control backup motion of the apparatus to cover previously-missed areas when turning corners. In yet another aspect, the apparatus is shaped to have its treatment mechanism positioned more closely to the front of the apparatus to enable treatment more closely to walls near corners. In one embodiment, the robotic surface treatment apparatus is a robotic vacuum. The vacuum may have its cleaning brush positioned near a flat front portion of the apparatus.

Abstract: Techniques that optimize performance of simultaneous localization and mapping (SLAM) processes for mobile devices, typically a mobile robot. In one embodiment, erroneous particles are introduced to the particle filtering process of localization. Monitoring the weights of the erroneous particles relative to the particles maintained for SLAM provides a verification that the robot is localized and detection that it is no longer localized. In another embodiment, cell-based grid mapping of a mobile robot's environment also monitors cells for changes in their probability of occupancy. Cells with a changing occupancy probability are marked as dynamic and updating of such cells to the map is suspended or modified until their individual occupancy probabilities have stabilized.

Abstract: A distance measuring system and method employing a laser distance sensor may have utility in various applications. In accordance with one aspect of the present invention, a laser distance sensor may acquire accurate distance measurements with a short baseline.

Abstract: Techniques that optimize performance of simultaneous localization and mapping (SLAM) processes for mobile devices, typically a mobile robot. In one embodiment, erroneous particles are introduced to the particle filtering process of localization. Monitoring the weights of the erroneous particles relative to the particles maintained for SLAM provides a verification that the robot is localized and detection that it is no longer localized. In another embodiment, cell-based grid mapping of a mobile robot's environment also monitors cells for changes in their probability of occupancy. Cells with a changing occupancy probability are marked as dynamic and updating of such cells to the map is suspended or modified until their individual occupancy probabilities have stabilized.

Abstract: A robotic surface treatment apparatus treats corners of rooms more effectively through intricate guidance of the apparatus through inside and outside corners. In one aspect, contact and/or non-contact sensors provide information to one or more on-board processors on the apparatus to enable selective overriding of obstacle avoidance program code and allow the apparatus to get closer to walls to facilitate treatment. In another aspect, the sensors provide information to the on-board processors to control backup motion of the apparatus to cover previously-missed areas when turning corners. In yet another aspect, the apparatus is shaped to have its treatment mechanism positioned more closely to the front of the apparatus to enable treatment more closely to walls near corners. In one embodiment, the robotic surface treatment apparatus is a robotic vacuum. The vacuum may have its cleaning brush positioned near a flat front portion of the apparatus.

Abstract: Techniques that optimize performance of simultaneous localization and mapping (SLAM) processes for mobile devices, typically a mobile robot. In one embodiment, erroneous particles are introduced to the particle filtering process of localization. Monitoring the weights of the erroneous particles relative to the particles maintained for SLAM provides a verification that the robot is localized and detection that it is no longer localized. In another embodiment, cell-based grid mapping of a mobile robot's environment also monitors cells for changes in their probability of occupancy. Cells with a changing occupancy probability are marked as dynamic and updating of such cells to the map is suspended or modified until their individual occupancy probabilities have stabilized.