Navigation system and method for navigating autonomous mobile robot in production environment, handheld device and autonomous mobile robot

Abstract

A navigation system and method for navigating an autonomous mobile robot in an environment, a handheld device, and an autonomous mobile robot are provided. The navigation system includes: at least one optical sensor attached to the autonomous mobile robot; a controller in communication with the at least one optical sensor; and a plurality of optical identifiers distributed at fixed locations within the environment and detectable by the at least one optical sensor. Each of the plurality of optical identifiers encodes a location within the environment. The controller is configured to: obtain a picture of the environment through the at least one optical sensor; detecting a visible optical identifier of the plurality of optical identifiers within a field of view of the at least one optical sensor; decoding the visible optical identifier; and navigating the autonomous mobile robot based on the real-time positioning of the autonomous mobile robot within the environment using the decoded visible optical identifier.

Description

Technical Field

[0001] The present disclosure relates to a navigation system for navigating an autonomous mobile robot within an environment, an autonomous mobile robot implementing such a navigation system, and a corresponding method for navigation. Background Art

[0002] In environments such as production or maintenance environments, in particular for aircraft and spacecraft, the fuselages of aircraft and spacecraft are frequently inspected using optical scanners. Such inspections are performed in order to detect undesirable anomalies such as dents, rivet pull-ins, out-of-profile deformations, blend-outs and scratches. For this purpose, the optical scanner can be arranged in a handheld unit and an operator of the unit can use the handheld unit to scan the surface of the fuselage in a grid-like or matrix-like pattern. In order to accurately locate the anomalies detected on the surface of the fuselage, the operator can manually mark the surface each time a scan is performed, for example by attaching a corresponding mark indicating the position on the surface, which mark is also photographed by the optical scanner and the scan is thus matched to the surface area. In order to reduce the workload, the overlap between the individual scans must be minimized.

[0003] Furthermore, autonomous mobile robots are known that are, in principle, capable of autonomously scanning the surface of an aircraft fuselage and autonomously moving within an environment. For example, such autonomous mobile robots may include robots to which various end effectors (such as the aforementioned optical scanners) can be attached. In particular, due to the large size of, for example, an aircraft fuselage, autonomous mobile robots need to be able to navigate within an environment (e.g., within an assembly hangar). This is typically accomplished using range scanners (e.g., LiDAR scanners).

[0004] However, such navigation only allows a very limited navigation range, and the particular automatic navigation of an autonomous mobile robot between different areas, for example between buildings of a production plant, can be problematic. Summary of the Invention

[0005] The object is therefore to provide a navigation / positioning system which is reliable and safe and which enables automatic navigation / positioning within large environments, for example for navigating an autonomous mobile robot within an environment or for determining a working position on an object.

[0006] In the present disclosure, a navigation system for an autonomous mobile robot (first aspect), a handheld device for performing work tasks (second aspect), an autonomous mobile robot that implements the disclosed navigation system (third aspect), and a method for navigating an autonomous mobile robot (fourth aspect) are disclosed. It should be understood that the features and embodiments described with respect to any one of these aspects are also fully valid for the remaining aspects, and vice versa. Most features and embodiments will be described with respect to the navigation system. However, these features and embodiments can also be implemented using the autonomous mobile robot, handheld device, and method. Any and all features, feature combinations, and embodiments of the disclosed features, feature combinations, and embodiments are explicitly disclosed for all aspects of the present disclosure.

[0007] According to a first aspect, a navigation system for navigating an autonomous mobile robot in an environment is provided. The navigation system includes at least one optical sensor attached to the autonomous mobile robot; a controller in communication with the at least one optical sensor; and a plurality of optical identifiers distributed at fixed locations within the environment and detectable by the at least one optical sensor. Each of the plurality of optical identifiers encodes a location within the environment. The controller is configured to: obtain an image of the environment via the at least one optical sensor; detect visible optical identifiers from the plurality of optical identifiers within a field of view of the at least one optical sensor; decode the visible optical identifiers; and use the decoded visible optical identifiers to navigate the autonomous mobile robot based on the real-time positioning of the autonomous mobile robot within the environment.

[0008] An autonomous mobile robot can be used to autonomously perform certain operations within an environment. It should be understood that, although primarily described herein with respect to aircraft applications, the autonomous mobile robot can be used for any other conceivable application and can also be not only an inspection robot, but can also, for example, perform certain manufacturing processes, such as riveting. The novel aspects of the present application do not relate to a specific application of the autonomous mobile robot, but rather to the navigation of such a robot, regardless of the robot's purpose.

[0009] For example, in the manufacture of aircraft, spacecraft (also for any other conceivable means of transportation or object), operations that may need to be processed on large surfaces or in large environments, such as surface inspection for abnormalities, riveting operations or other operations are performed. At present, such operations are mainly performed manually by inspectors or manufacturers using handheld devices. In addition, for example, in inspection applications, optical scanners (such as matrix cameras) are usually used to scan most of the surface (for example, the fuselage of an aircraft) in a grid pattern to detect abnormalities, such as dents, rivet pull-ins, deformation outside the contour, transition zone defects and scratches. The detected abnormalities must be related to their position on the scanned surface. For this reason, before scanning, the operator usually marks each part, for example, by attaching corresponding stickers or other marks to the surface, so that these marks are recorded together with the scan to be related to the detected abnormalities. All of the above leads to a high workload and high time requirements.

[0010] These shortcomings can be avoided by using autonomous mobile robots that perform the corresponding operations completely automatically. However, to avoid human intervention, autonomous mobile robots need to be able to navigate within the environment in a safe and reliable manner.

[0011] The autonomous mobile robot may, for example, include a drive unit (e.g., including a motor unit (e.g., an electric motor powered by a battery)) and a propulsion device that contacts the ground on which the autonomous mobile robot is placed. The propulsion device may, for example, include wheels (some of which may be steerable), a track drive, or any other steerable propulsion device.

[0012] The environment in which the autonomous mobile robot navigates can be, for example, a production environment (e.g., an assembly hangar for aircraft or spacecraft), a maintenance environment / hangar, a logistics environment, or any other environment in which an autonomous mobile robot is generally required to navigate. In particular, the environment can be an indoor environment, an outdoor environment, or both, and can include multiple areas. In particular, the navigation system also provides navigation between indoor and outdoor environments or generally within a certain area and between such areas.

[0013] The navigation system and the corresponding method are based on optical navigation using a plurality of optical identifiers distributed within an environment. The optical identifiers are arranged at fixed positions within the environment, and in particular, each of the optical identifiers encodes the corresponding position of the corresponding optical identifier within the environment. In a preferred embodiment, the optical identifiers are QR codes, wherein the data content of each QR code (or generally, optical identifiers, regardless of the type of optical identifier) ​​corresponds to the position of the corresponding QR code within the environment. However, the optical identifiers may also be any other suitable identifiers as further described below. The data content of the optical identifiers may, for example, be in the form of coordinates relating to a map of the environment stored in or accessible by a controller.

[0014] For example, in an aircraft assembly hangar, optical identifiers can be placed on walls within the hangar, on fixed structures (e.g., beams, support structures for the aircraft fuselage), or at any other fixed location within the environment. Furthermore, optical identifiers can even be placed on objects that an autonomous mobile robot itself performs some actions (e.g., inspection scans), since objects (e.g., aircraft fuselages) are typically located at different locations within the environment while being handled.

[0015] The optical identifier can be printed, painted, projected onto the corresponding location, or placed in any other suitable manner. Preferably, the optical identifier is not placed on the ground (especially when the optical identifier is printed / painted or the like), as such an optical identifier may become undetectable over time due to degradation. However, in principle, it is also possible to place the optical identifier on the ground. Furthermore, if the optical identifier is projected, there is nothing to prevent it from being projected onto the ground.

[0016] The navigation system also includes a controller and at least one optical sensor, such as a high-resolution camera directly attached to the autonomous mobile robot and capable of capturing optical identifiers. The controller can be located directly at the autonomous mobile robot, but it can also be a remote controller in communication with the autonomous mobile robot. For example, the autonomous mobile robot can be equipped with a forward-facing camera with a certain field of view. Furthermore, the autonomous mobile robot can have multiple such optical sensors facing different directions of the autonomous mobile robot. As the autonomous mobile robot moves within the environment, some of the multiple optical identifiers enter the corresponding field of view of the at least one optical sensor at certain points, depending on the robot's current position within the environment.

[0017] In particular, as the autonomous mobile robot moves, the controller uses at least one optical sensor to continuously capture images of the environment (e.g., as discrete images taken at periodic time intervals or in a video stream). Each time a corresponding optical identifier from the plurality of optical identifiers enters a field of view of the at least one optical sensor, the controller is configured to detect / identify the corresponding optical identifier within the field of view (i.e., a visible optical identifier).

[0018] The controller is further configured to decode (i.e., decode the data content of) the optical identifiers (visible optical identifiers) visible at the current location, and thereby obtain the corresponding current location of the visible optical identifiers within the environment. Because the controller knows the orientation of each of the at least one optical sensor on the autonomous mobile robot, it can determine the orientation and viewing direction of the autonomous mobile robot as a whole relative to each visible optical identifier, i.e., the angular position of the visible optical identifier relative to the autonomous mobile robot. The decoded and thus known positions of the visible optical identifiers and the viewing direction of the autonomous mobile robot relative to each of the visible optical identifiers can be used to determine the current location (i.e., coordinates) of the autonomous mobile robot within the environment. This determination can generally be accomplished using any suitable method (e.g., triangulation, trilateration, triangulation, etc.).

[0019] Furthermore, based on the autonomous mobile robot's current viewing direction relative to each of the visible optical identifiers, the autonomous mobile robot's overall orientation within the environment and, therefore, its current direction of movement (i.e., the direction the autonomous mobile robot would travel if not steered) can be determined. The controller can then use real-time positioning and orientation to navigate the autonomous mobile robot within the environment (the term "real-time positioning" refers to the continuous determination of the autonomous mobile robot's current position). The controller can have a map of the environment stored in a data storage device, and can use the map to determine the autonomous mobile robot's current position and orientation within the environment using the known (decoded) positions of the visible optical identifiers and the determined viewing direction of the autonomous mobile robot relative to each of the visible optical identifiers.

[0020] According to an embodiment, the controller is configured to estimate the distance to each of the visible optical identifiers and to navigate the autonomous mobile robot by applying a triangulation method between each pair of visible optical identifiers.

[0021] In conventional triangulation, the position of an object is determined by observing it using two sensors whose positions and distances are known. Therefore, each of the two sensors establishes an observation line (an imaginary line connecting the corresponding sensor and the object) with the object. By observing the object from both sensors, the observation angle between the corresponding observation line and the line connecting the two sensors can be directly measured for each sensor. The two observation lines and the line connecting the two sensors then form a triangle. Based on the known positions of the two sensors (and their distance) and the observation angle, the position of the object can be determined by determining the intersection of the observation lines.

[0022] However, unlike conventional triangulation methods, in the disclosed navigation system, the object to be located (the autonomous mobile robot, or more precisely, the controller) determines its own position. Therefore, a modified triangulation method is used that also takes into account the distance to the optical identifier. The two sensors used in the conventional triangulation method described above are represented by at least one optical sensor arranged on the autonomous mobile robot. Instead of using a connecting line between the two sensors, a connecting line between two of the visible optical identifiers (whose positions within the environment are known, as described above) is used. The observation line described above then corresponds to a line connecting the autonomous mobile robot to each of the two visible optical identifiers. However, the autonomous mobile robot cannot directly measure the observation angle (the corresponding angle between the known connecting line of the two optical identifiers and the observation line between the autonomous mobile robot and the corresponding optical identifier) ​​simply by using the viewing direction of the visible optical identifier. This is, of course, because the position of the autonomous mobile robot is not yet known.

[0023] Therefore, the distance to each of the visible optical identifiers is further determined. To this end, for example, each of the optical identifiers can have the same size, and the distance can be determined based on the apparent size observed by the corresponding optical sensor. The farther apart the optical identifiers are, the smaller the apparent size observed by the optical sensor. For example, the distance can be estimated based on the corresponding calibration of the optical sensor (e.g., based on a lookup table or a machine learning model). Alternatively or additionally, the distance to each of the visible optical identifiers can be determined, for example, by using time of light measurement (e.g., a LiDAR sensor attached to an autonomous mobile robot) or by using any other suitable distance measurement method.

[0024] Using the estimated distances to the optical identifiers in each pair of visible optical identifiers and the determined viewing directions relative to each of these optical identifiers (e.g., expressed as angles with respect to the forward direction of the autonomous mobile robot), the controller can determine the viewing angles with respect to each pair of visible optical identifiers and can determine the position of the autonomous mobile robot based on the viewing angles by determining the intersection of the viewing lines for each pair of visible optical identifiers, as in conventional triangulation.

[0025] Using each pair of visible optical identifiers for determining the position of the autonomous mobile robot provides redundancy and plausibility control. If the individual positions deviate from each other by a critical amount, the positioning performed using the optical identifiers can be considered malfunctioning, and the autonomous mobile robot can be stopped. Furthermore, if the positions using different pairs of visible optical identifiers deviate from each other by a non-critical amount, the position can be determined as the average of the individual positions.

[0026] According to another embodiment, the controller is configured to assign a weight to each of the visible optical identifiers based on distance. Optical identifiers that are closer to the autonomous mobile robot are assigned a higher weight for navigating the autonomous mobile robot.

[0027] Distance estimates for optical identifiers (or generally any object) based on optical scans of the optical identifiers (e.g., images of the optical identifiers recorded using an optical sensor, such as a camera) are more accurate for optical identifiers that are closer to the optical sensor. This is, for example, because alignment inaccuracies (e.g., angular inaccuracies) of the camera have a greater impact on measurements of longer distances, as small deviations in angle lead to larger deviations in the estimated position of the corresponding optical identifier, and thus to larger offsets in the position of the autonomous mobile robot determined using the farther-away optical identifiers (since the position of the autonomous mobile robot is determined relative to the optical identifiers). Consequently, visible optical identifiers that are closer to the autonomous mobile robot are assigned a higher weight for determining the real-time position. In particular, when determining the real-time position of the autonomous mobile robot, the controller can, for example, construct a weighted average of the individual positions determined based on each pair of optical identifiers, such that optical identifiers that are closer to the autonomous mobile robot have a greater impact on the determined real-time position, and therefore have a greater impact on the navigation of the autonomous mobile robot. In particular, when each of the optical identifiers has the same actual size (not the recorded size), the controller can identify which of the optical identifiers is closer to the autonomous mobile robot and assign a higher weight to those optical identifiers that appear larger in the optical scan (e.g., the recorded image). This increases the overall accuracy of navigation. In addition, individual optical identifiers (not just pairs of optical identifiers) can be assigned individual weights for use in triangulation for each pair of optical identifiers.

[0028] According to further embodiments, the at least one optical sensor comprises at least one of a high-resolution camera and a short-range, low-resolution camera.

[0029] A short-range, low-resolution camera attached to the autonomous mobile robot can be used exclusively for navigation. A high-resolution camera can also be used exclusively for navigation, but can also be used by the autonomous mobile robot when performing certain tasks, such as optical inspection scanning of the surface of an aircraft fuselage. The high-resolution camera is particularly useful for detecting optical identifiers that are far from the autonomous mobile robot. By additionally utilizing the high-resolution camera present on the autonomous mobile robot (because the autonomous mobile robot uses it, for example, to perform tasks such as surface scanning), the overall navigation accuracy can be improved because the overall field of view can be increased, and therefore more optical identifiers can be seen.

[0030] However, it should be understood that each of the at least one optical scanner may be any suitable camera or other optical scanner capable of detecting an optical identifier.

[0031] According to further embodiments, each of the optical identifiers is a printed or light-projected optical identifier and comprises at least one of the following: a QR code, a barcode, a JAB code, an Aztec code, and a reference number.

[0032] In particular, these optical identifiers may have a size that is relatively large so that they can be easily detected by optical sensors when distributed within an environment. Each of these optical identifiers may be detected by a camera device acting as an optical sensor and may be decoded by a controller.

[0033] QR codes (Quick Response Codes), JAB codes (just another kind of barcode), and Aztec codes are two-dimensional matrix codes that can store information, whereas barcodes are one-dimensional codes used to store information. A JAB code is similar to a QR code, but it is a colored 2D matrix symbol consisting of colored squares arranged in a square or rectangular grid. It contains a primary symbol and optionally multiple secondary symbols and can store more information than, for example, a conventional QR code. In general, any of these optical identifiers, or any other suitable optical identifier, can be used in the disclosed navigation system. In particular, these optical identifiers can be designed to carry as data content the location within the environment in which the corresponding optical identifier is located. Alternatively, the optical identifier can simply carry, for example, the number of the corresponding optical identifier, which is associated with a location within a map of the environment accessible to the controller. Preferably, the optical identifier is a QR code.

[0034] According to another embodiment, the plurality of optical identifiers includes a first subset of optical identifiers and a second subset of optical identifiers, wherein the first subset is associated with a first region of the environment and the second subset is associated with a second region of the environment.

[0035] For example, in aircraft applications (but also in other applications), when an autonomous mobile robot is inspecting an entire fuselage, it may be necessary for the autonomous mobile robot to work on different levels, i.e., vertically separated floors. Such floors may be connected, for example, by elevators. Each of these levels may represent a corresponding mapped area within the environment, so that two-dimensional localization of the autonomous mobile robot can be achieved by switching between corresponding maps. The corresponding maps then only cover the corresponding areas of the environment. Furthermore, the autonomous mobile robot may need to travel between different buildings (e.g., between separate assembly hangars). The different buildings then correspond to various areas of the overall environment. Traveling between such buildings may also include traveling between indoor and outdoor areas.

[0036] Thus, the multiple optical identifiers can be separated into separate subsets, each of which is associated with a corresponding area and, therefore, a corresponding map. This allows the controller to automatically switch to the corresponding map when an optical identifier for another area (i.e., an area outside the current area, as determined by the last position fix) is detected. Furthermore, at certain transition points, such as at elevator entrances or assembly hangar doors, dedicated optical identifiers can be placed that specifically instruct the controller to switch to the corresponding map after the transition point. For example, when an elevator is coming from the first floor, it can itself be included in the map for each floor. When the autonomous mobile robot enters the elevator and travels to another floor, it can detect a corresponding optical identifier that instructs the controller to switch to the map for the next floor, or it can simply do so upon detecting the first optical identifier for another floor. This makes it possible to cover a large environment, even one that can be divided vertically into different areas, while still using only two-dimensional navigation.

[0037] According to a further embodiment, the position determination using the decoded visible optical identifier is determined by referring to a map of the environment stored in a data storage device based on the visible optical identifier.

[0038] Such a map may include the locations of all optical identifiers within the environment (or only the locations in certain areas of the environment in some embodiments). Thus, each optical identifier encodes its corresponding location within the map, allowing the autonomous mobile robot to navigate within the map and, therefore, within the environment. The map may also optionally include travel paths between target points within the environment.

[0039] According to another embodiment, the navigation system further includes at least one LiDAR scanner disposed at the autonomous mobile robot and in communication with the controller. The at least one LiDAR scanner is configured to scan the autonomous mobile robot's surrounding environment. The controller is configured to additionally locate the autonomous mobile robot within the environment based on the scans by the at least one LiDAR scanner. The controller is configured to compare the positioning of the at least one LiDAR scanner with the positioning of the at least one optical sensor and obtain a corresponding variance.

[0040] At least one LiDAR scanner is used as redundancy, in particular for ensuring safety. For example, the navigation system can use the LiDAR scanner to continuously scan the distance between the side of the autonomous mobile robot and the corresponding wall or other object. If these distances are inconsistent with the positioning obtained with the aid of an optical sensor (hereinafter referred to as optical navigation), the corresponding mismatch or variance (i.e., the position difference between the positioning methods) is determined. For example, if the navigation system determines a certain position and orientation within the assembly hangar by using an optical scanner, the corresponding map includes the walls of the assembly hangar. Therefore, the controller can determine (based on the map and the determined positioning and orientation) how far the next wall on each side of the autonomous mobile robot should be. If the LiDAR scanner deviates from these distances, the controller determines the mismatch between the methods and can therefore identify incorrect or inaccurate navigation.

[0041] According to further embodiments, the controller is configured to navigate the autonomous mobile robot based solely on the optical identifier when the variance is below a first threshold, and to stop the autonomous mobile robot when the variance is above a second threshold.

[0042] Small deviations between optical navigation and LiDAR navigation may not be a problem. Therefore, if the variance between optical navigation and LiDAR navigation is above a predefined first threshold but below a second threshold (intermediate variance range), the autonomous mobile robot can continue to rely solely on optical navigation. If the first threshold is exceeded, the navigation system can still use optical navigation, but can increase the involvement of other navigation methods (such as LiDAR scanners), for example by correcting the path of the autonomous mobile robot based on the determined variance.

[0043] However, if a critical variance (a second threshold) is exceeded, the controller may stop the autonomous mobile robot to ensure safety until the problem is resolved by a human operator or until the navigation system is reset by such an operator. The second threshold may be higher than the first threshold, or may be the same as the first threshold. In the latter case, there is no intermediate variance range, and if the variance exceeds the first threshold, the navigation system immediately enters an emergency stop.

[0044] According to another embodiment, the controller is configured to store the navigation history of the autonomous mobile robot. The navigation history is used as training data for the artificial intelligence module (AI module).

[0045] Such AI modules may also generally be referred to as machine learning modules. For example, the navigation history may also include the paths that the autonomous mobile robot has traveled in the past, such as any events or problems encountered on these paths. For example, such events or problems may include inaccuracies determined by additional navigation methods (e.g., by a LiDAR scanner as described above with respect to the embodiments), emergency stops, collisions with fixed objects, and similar events on the path and the locations where these events occurred. By using this data as training data, the AI ​​module can learn to avoid such events in the future. For example, if the determined inaccuracy of the positioning of the autonomous mobile robot is always of the same size repeatedly at a specific location, the AI ​​module can automatically correct the positioning at that location based on the training data in the future. Any other AI algorithm for improving optical navigation is conceivable.

[0046] According to further embodiments, the AI ​​module is used to optimize the path of the autonomous mobile robot and / or to identify anomalies within the environment.

[0047] For example, if different paths are available between two points of interest within an environment, the AI ​​module can determine the fastest path without any incidents or problems based on training data (past travel), or can avoid paths where problems have occurred in the past.

[0048] According to another embodiment, each of the multiple optical identifiers is arranged at one of the following locations: a wall within the environment, a supporting structure for the product to be processed by the autonomous mobile robot, the product to be processed by the autonomous mobile robot, a second autonomous mobile robot or another robotic system in communication with the controller, a drone, a handheld device, or a human operator.

[0049] If some of the optical identifiers are placed on movable objects, such as drones or other robots, these drones or robots can communicate with the autonomous mobile robot (or more precisely, with a controller, which can also be a central controller for all devices operating within the environment) and can transmit their current location to the controller, in particular via a data channel, such as a WiFi connection or any other communication connection. As described above, in this way, the controller can associate the corresponding optical identifiers with their current location and use them in the same way as fixed optical identifiers. In addition, other robots and drones can also receive the current location of the autonomous mobile robot and navigate in the same way. The autonomous mobile robot itself can also carry at least one optical identifier. Thus, a network of devices, such as autonomous mobile robots, is established for collaborative navigation, which supports each other in navigating within the environment.

[0050] According to a second aspect, a handheld device for a human operator to perform a work task on an object is disclosed. The handheld device includes at least one work tool, a camera, and a controller. The controller is configured to: obtain, via the camera, an image of the environment in which the handheld device is operated; and detect a visible optical identifier from a plurality of optical identifiers arranged at fixed locations within the environment. A visible optical identifier is an optical identifier within the field of view of at least one optical sensor. The controller is further configured to: decode the visible optical identifier; and, using the decoded visible optical identifier, associate data related to the work task with a work location on the object where the work task has been performed, based on the real-time location of the handheld device within the environment.

[0051] The handheld device can be, for example, an inspection device, a riveting device, or any other handheld device used to perform work tasks on an object, such as a fuselage. For example, if the handheld device is an inspection device, the work tool can be an optical scanner used to obtain an optical scan of the surface of the object (e.g., the fuselage). Typically, it is necessary to save, for example, the locations on the fuselage where the corresponding scan (or generally, the work task) has been performed (e.g., the location of the fuselage stringer or frame (or both) where the scan was performed). In order to avoid manually entering or marking locations on the object, the handheld device uses the same principles as the navigation system described above to obtain its current location. Therefore, the corresponding discussion on how to obtain real-time positioning by detecting visible optical identifiers within the environment will not be repeated here.

[0052] However, real-time positioning can be obtained, for example, each time a scan (or some other work task) is performed, rather than for navigation purposes. The position determined at each work task (i.e., the location of the handheld device within the environment) is used to determine the location on the object where the work task was performed (e.g., a location on the fuselage). These locations can then be associated with data related to the work task (e.g., an optical scan performed at that location).

[0053] The camera device of the handheld device used to obtain a picture of the environment can be an integrated camera device of the handheld device, or can be a camera device that can be attached to the handheld device and communicates with the controller and can be used by the controller to obtain a picture of the optical identifier in the environment. For example, the camera device can be the camera device of a smartphone that is attached to the handheld device via a corresponding adapter. The smartphone can then be connected to the controller and can be used to obtain the corresponding picture.

[0054] According to a third aspect, an autonomous mobile robot is provided. The autonomous mobile robot includes at least one optical sensor and a controller. The controller is configured to: obtain an image of an environment in which the autonomous mobile robot is located via the at least one optical sensor; and detect a visible optical identifier. The visible optical identifier is within the field of view of the at least one optical sensor. The visible optical identifier is one of a plurality of optical identifiers located at fixed positions within the environment. Each of the plurality of optical identifiers encodes a position within the environment. The controller is further configured to: decode the visible optical identifier; and use the decoded visible optical identifier to navigate the autonomous mobile robot based on the real-time positioning of the autonomous mobile robot within the environment.

[0055] The autonomous mobile robot can be, for example, an inspection robot or any other robot. The autonomous mobile robot has already been described above with respect to the navigation system itself. Therefore, the corresponding discussion will not be repeated here. Instead, for further details, reference is made to the above discussion of the navigation system and its implementation, which is fully applicable to the autonomous mobile robot.

[0056] According to a fourth aspect, a method for navigating an autonomous mobile robot within an environment is provided. The method includes: obtaining, by a controller, an image of the environment using at least one optical sensor attached to the autonomous mobile robot; and detecting, by the controller, a visible optical identifier from a plurality of optical identifiers. The visible optical identifier is within a field of view of the at least one optical sensor. Each of the plurality of optical identifiers encodes a fixed location within the environment. The method also includes: decoding, by the controller, the visible optical identifier; and using, by the controller, the decoded visible optical identifier to navigate the autonomous mobile robot based on the autonomous mobile robot's real-time position within the environment.

[0057] The method for simultaneously navigating an autonomous mobile robot has been described with respect to a navigation system. Therefore, the discussion of the navigation system and its implementation is fully valid for the method and will not be repeated here. Instead, reference is made to the discussion of the navigation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In the following, exemplary embodiments are described in more detail with reference to the accompanying drawings. These drawings are schematic and not drawn to scale. The same reference numerals refer to the same or similar elements. The accompanying drawings show:

[0059] Figure 1 is a schematic diagram of an autonomous mobile robot utilizing / implementing the navigation system / method of the present disclosure.

[0060] Figure 2 is a schematic top view of an environment in which a navigation system / method according to the present disclosure is used in an exemplary scenario.

[0061] Figure 3 is a schematic cross-sectional view of an environment in which a navigation system / method according to the present disclosure is used in another exemplary scenario.

[0062] Figure 4 is a schematic diagram of a handheld device for associating data related to a work task with a corresponding work location using the positioning method of the navigation system described herein.

[0063] Figure 5 It is possible to use, for example, Figure 2 and Figure 3 Flowchart of a method for navigating an autonomous mobile robot in an environment implemented by a navigation system. DETAILED DESCRIPTION

[0064] Figure 1 An autonomous mobile robot 100 is shown. The autonomous mobile robot 100 includes a robot body 104, a robot arm 101 (sometimes also referred to as a manipulator), and an end effector 102 attached to the robot arm 101. In addition, a tray 103 (e.g., for holding different end effectors 102 or as an associated drone 160 ( Figure 2 、 Figure 3) is attached to the robot body 104. The autonomous mobile robot 100 also includes a drive unit (only wheels 130 are shown) for driving the autonomous mobile robot 100 above the ground. The autonomous mobile robot 100 also includes a plurality of optical sensors 110 in the form of short-range low-resolution cameras 111, and a plurality of LiDAR scanners 112. The optical sensors 110 and the LiDAR scanners 112 are arranged around the robot body 104 so that they can view the robot's surroundings. It should be understood that the camera 111 can also be any other type of camera, such as a high-resolution camera, a variable focal length camera (for example, using a fluid lens, etc.). The autonomous mobile robot 100 also includes an additional optical sensor 110 in the form of a high-resolution camera 113 as part of the end effector 102. It should be understood that more than one high-resolution camera 113 can also be provided, and some of the low-resolution cameras 111 can be replaced by high-resolution cameras 113.

[0065] The end effector 102 may be, for example, an optical scanner 102 for performing optical inspection scans of a surface (e.g., the surface of an aircraft or spacecraft fuselage) to detect anomalies such as dents, rivet pulls, out-of-profile deformations, transition zone defects, and scratches. A high-resolution camera 113 is part of the optical scanner 102 in the depicted configuration and may be, for example, a high-resolution matrix scanner. However, the robot body 104 may also directly carry one or more high-resolution cameras 113.

[0066] Although shown in simplified form, it should be understood that the robotic arm 101 can generally be a robotic arm 101 having at least three degrees of freedom (rotation) such that the end effector 102 can be moved to any three-dimensional position and orientation.

[0067] The controller 120 is included as a resident controller 120 within the autonomous mobile robot 100 in the depicted configuration. The controller 120 includes a data storage device 121 and an optional artificial intelligence module (AI module) 122. The controller 120 can be used to control the general operation of the autonomous mobile robot 100 and can be particularly configured to implement the navigation system and method described with reference to the following figures. The optical sensor 110 and the LiDAR scanner 112 communicate with the controller 120. Although at least for the purposes of the navigation system 10 ( Figure 2 ) and methods are shown as a resident controller 120, but the controller 120 can also be a remote controller arranged at another location outside the autonomous mobile robot 100 but in communication with the robot and in particular with the optical sensor 110. Figure 1 Also shown as about Figure 2An exemplary QR code 2 is shown as one of the optical identifiers 2 that are part of the navigation system 10. The exemplary QR code 2 is attached to the robot body 104 of the autonomous mobile robot 100 and can be used to identify the autonomous mobile robot 100 (e.g., by Figure 2 The current position determined by the navigation system 10 and transmitted via the data connection is provided to other devices, such as to other autonomous mobile robots 100 or to the relevant Figure 4 Then, when the handheld device 170 is implemented Figure 2 These other devices may also use the position of the autonomous mobile robot 100 when navigating the system 10 .

[0068] Figure 2 A navigation system 10 implemented by an autonomous mobile robot 100 is shown, which exemplarily has an environment 1 comprising two areas 5, 6 in the form of assembly hangars (highly schematic), which are connected by an outdoor path 11. The assembly hangars 5, 6 are shown in a schematic top view. In particular, the first area 5 ( Figure 1 In each of the areas 5, 6, two aircraft fuselages 9, or more precisely, the body parts of such fuselages 9, are shown. Each of the fuselages 9 is supported by a support structure 7. In each of the areas 5, 6, there are four vertical support beams 12, which can, for example, support the upper platform at the corresponding assembly station, such as Figure 3 In each of the two areas 5, 6, there are currently two autonomous mobile robots 100, each of which can be as described in relation to Figure 1As depicted, the autonomous mobile robot 100 is an inspection robot for performing surface inspections of the fuselage 9. However, the autonomous mobile robot 100 may also be any other type of autonomous mobile robot 100 (e.g., an autonomous riveting robot). The autonomous mobile robot 100 can autonomously navigate within the environment 1. As part of a navigation system 10 primarily implemented by the autonomous mobile robot 100, within each of the areas 5 and 6 of the environment 1, there are multiple optical identifiers 2 (for clarity, not all optical identifiers are indicated by reference numerals), for example in the form of QR codes or any other form described further above. The optical identifiers 2 are depicted as small squares in a highly schematic representation. It should be understood that the optical identifiers 2 are arranged so that, if positioned appropriately, they can generally be viewed by the optical sensor (camera) 110 of the autonomous mobile robot 100. The optical identifiers 2 can be arranged at different heights or all at the same height. In the depicted example, four optical identifiers 2 are arranged at each of the vertical support beams 12. Furthermore, one optical marker 2 is arranged at each vertical beam of the support structure 7 supporting the fuselage 9, and a plurality of further optical markers 2 are arranged on the walls of the assembly hangars / areas 5, 6. The assembly hangars 5, 6 are accessible via corresponding doors 8. At each door 8, an optical marker 2 is also arranged externally to facilitate the transfer of the autonomous mobile robot 100 from the outdoors to the indoors.

[0069] Each optical identifier 2 encodes the location within the environment 1 where the corresponding optical identifier 2 is located. For example, each optical identifier 2 may be in the form of a QR code that encodes data content including the coordinates of the location where the corresponding QR code is located. The optical identifiers 2 may be printed, attached (e.g., printed on a sticker attached to the corresponding location), projected, or otherwise located at the corresponding location. Preferably, the optical identifiers 2 all have the same size.

[0070] Figure 2 8 and 10. A situation is shown in which one of the autonomous mobile robots 100 has just entered the area 5 through the corresponding door 8 and is navigating within the area 5 of the environment 1 using the navigation system 10. The three fields of view 13, 14, 15 of the three forward-facing cameras / optical sensors 110 are indicated by dashed lines. The first field of view 13 corresponds to the high-resolution camera 113 (see FIG. Figure 1). The second and third fields of view 14, 15 correspond to the close-range, low-resolution camera device 111. Within the first field of view 13, six optical identifiers 2 are currently visible (two on each of the corresponding vertical support beams 12 within the field of view, and two on the wall in front of the autonomous mobile robot 100). Furthermore, within each of the second and third fields of view 14, 15, two optical identifiers 2 are visible (on the corresponding vertical support beams 12). Thus, a total of ten optical identifiers 2 are currently visible, which can be used by the controller 120 to determine the current position and orientation of the autonomous mobile robot 100. The controller decodes the visible optical identifiers 2 and thereby obtains their positions within the environment 1. Furthermore, based on the apparent size of the currently visible optical identifiers within the recorded image, the controller 120 determines the distance to each of the visible optical identifiers 2.

[0071] Using the determined positions of the visible optical identifiers 2 and the determined distances to each of the visible optical identifiers (taking into account the viewing direction, which is known because the arrangement of the optical sensor (camera) 110 on the autonomous mobile robot 100 is known to the controller 120), the controller 120 determines the current positioning of the autonomous mobile robot 100, for example using a triangulation method using each pair of visible optical identifiers 2, as further described above.

[0072] Optionally, in the triangulation method, a weight is assigned to each of the visible optical identifiers 2, and this weight is used in triangulation and determining the final position. Specifically, the controller 120 may assign higher weights to optical identifiers 2 that are closer to the autonomous mobile robot 100, as these optical identifiers produce more accurate positioning results. For example, the position of the autonomous mobile robot 100 is generated using triangulation of each pair of visible optical identifiers 2. Depending on the weights assigned to the optical identifiers, the controller 120 may determine the final position as, for example, a weighted average of the individual positions.

[0073] In this manner, the autonomous mobile robot 100 continuously determines its position as it moves through the environment 1. Thus, the controller 120 continuously obtains images of the environment 1 using the optical sensors 110, detects visible optical identifiers 2 within the images that are within the combined field of view of all optical sensors 110, decodes the data content of the detected visible optical identifiers 2, determines the real-time position of the autonomous mobile robot within the environment 1, and navigates the autonomous mobile robot 100 based on the real-time position.

[0074] Alternatively, a redundant check may be performed using the LiDAR scanner 112 or another navigation method. Figure 2In the example, the autonomous mobile robot 100 can use the LiDAR scanner 112 positioned on the side of the autonomous mobile robot 100 to determine the distance to the corresponding opposing wall. The position determined by the LiDAR scanner 112 is compared with the positioning obtained by optical navigation (navigation using the optical identifier 2), and the variance between the two positioning methods is estimated. If the variance is below a first threshold, navigation is performed solely using optical navigation. If the variance is above a second threshold (which may also be the same as the first threshold), the autonomous mobile robot 100 is stopped and the human operator 180 may be notified to address the situation.

[0075] exist Figure 2 In each of the areas (assembly hangars) 5 and 6, there is also a drone 160. The drone 160 can navigate in the same manner as the autonomous mobile robot 100. In particular, as used herein, the term "autonomous mobile robot," while described with respect to ground-based vehicles, also encompasses other vehicles such as the drone 160. The drone 160 and other autonomous mobile robots 100 and ( Figure 2 A handheld device 170 (shown in FIG) can also assist in navigating the autonomous mobile robot 100. The optical identifier 2 can also be attached to each mobile object (drone 160 (indicated only by reference numeral 2, not explicitly shown), autonomous mobile robot 100, handheld device 170, etc.) or to the body 9 itself. The mobile objects 100, 170, 160 can each continuously determine their current position and can assist each other in determining real-time positioning by continuously transmitting their positions to other mobile objects 100, 160 via a network connection. In this way, each mobile object 100, 170, 160 can serve as a reference position, just like the fixed optical identifier 2.

[0076] Alternatively, if more identifiers of the optical identifiers 2 are currently visible, the autonomous mobile robot 100 may move faster, since in this case the accuracy of positioning may be increased.

[0077] Furthermore, the navigation system 10 can enable navigation between areas 5 and 6 because once the autonomous mobile robot 100 enters a new area, the corresponding map of the new area is automatically loaded. For example, if the autonomous mobile robot 100 leaves the area via door 8 Figure 25 in the zone 5, a map of the outdoor area, in particular a map containing the path 11, can be loaded. The autonomous mobile robot 100, or more precisely the controller 120, can detect such a zone change by means of detecting the corresponding optical identifier 2, for example, if an optical identifier 2 belonging to a new zone is decoded for the first time, or if a dedicated optical identifier 2 containing a hint of a zone change is decoded. For example, the optical identifiers 2 on the sides of the door 8 may contain instructions to switch to a map of the area behind the door 8 once the autonomous mobile robot 100 passes through them. Thus, for example, once the autonomous mobile robot 100 leaves one of the zones 5, 6, the controller can first load a map of the outdoor area (path 11), and can then load a map of the other of the zones 5, 6 upon entering the corresponding door 8. In Figure 2 In FIG. 1 , one of the optical identifiers 2 (in the area 6 ) is also shown in enlarged form as an example of a QR code 2 .

[0078] Figure 3 Shown Figure 2 A cross-sectional view of a portion of one of the assembly hangars. Figure 3 , a scenario is shown in which autonomous mobile robots 100 operate on different layers (i.e., vertically separated regions 5, 6) as areas of an environment 1. One autonomous mobile robot 100 is shown operating on the top layer 6, while another autonomous mobile robot 100 operates on the bottom layer 5 of the corresponding environment 1. Each of the autonomous mobile robots 100 can switch between these layers 5, 6, i.e., change areas of the environment with different maps. For example, the autonomous mobile robot 100 can switch layers with the help of an elevator (not shown). Then, the autonomous mobile robot 100 can be operated in the same manner as described above. Figure 2 The transition between one level 5, 6 and another level 5, 6 is accomplished in the same manner as described above for switching between zones. However, here, the elevator serves as the transition point. In other words, once the autonomous mobile robot 100 enters the elevator (which it can navigate to using optical navigation in the manner described above), it can detect this by decoding corresponding optical identifiers 2, for example, located on the side of the elevator door. It can then enter the elevator, for example, using the LiDAR scanner 112, and switch the map to that of the new level 5, 6. Figure 3 Also shown is a handheld device 170 used by a human operator 180 to work on the surface of the fuselage 9, as described below with respect to Figure 4 Further described.

[0079] Figure 4A handheld device 170 is shown for performing work tasks on an object 9, such as an aircraft fuselage 9. For example, the handheld device 170 can be a manual inspection scanner having an optical scanner 172 (similar to the optical scanner 102 of the autonomous mobile robot 100) that is used to manually scan the surface of the aircraft fuselage 9 for anomalies. As depicted, the handheld device 170 includes a work tool 172, here in the form of an optical scanner 172. However, the work tool 172 can also be any other work tool 172, such as a riveting tool, etc. The handheld device 170 also includes a controller 173, a camera 171, and a handle 176 for holding the handheld device 170. Here, the camera 171 is the camera of a smartphone 174 attached to the handheld device 170 via an adapter 175.

[0080] Typically, it is necessary to save, for example, the location on the fuselage 9 where the corresponding scan (or, more generally, the work task) has been performed (e.g., the location of the stringer or frame (or both) of the fuselage 9 where the scan was performed). To avoid manually entering or marking the location on the object / fuselage 9, the handheld device 170 uses the same principles as the navigation system 10 described above to obtain its current location. Therefore, the corresponding discussion of how to obtain real-time positioning by detecting visible optical identifiers 2 within the environment 1 will not be repeated. Any and all features described with respect to real-time positioning of the autonomous mobile robot 100 using the navigation system 10 are fully valid for the handheld device 170.

[0081] However, real-time positioning can be obtained, for example, each time a scan (or some other work task) is performed, rather than for navigation purposes. The positioning determined at each work task (i.e., the position of the handheld device within the environment 1) is used to determine the location on the object 9 (e.g., the location on the fuselage 9) where the work task (here, the optical scan) has been performed. These locations can then be associated with data related to the work task (e.g., the optical scan performed at that location).

[0082] The camera 171 of the handheld device 170 used to obtain pictures of the environment 1 can be an integrated camera 171 of the handheld device 170, or (as depicted) can be a camera 171 that can be attached to the handheld device 170 and in communication with the controller 173 and can be used by the controller 173 to obtain pictures of the optical identifiers 2 within the environment 1. For example, as depicted, the camera 171 can be the camera 171 of a smartphone 174 that is attached to the handheld device 170 at a corresponding adapter 175. The smartphone 174 can then be connected to the controller 173 and can be used to obtain corresponding pictures.

[0083] About navigation system 10 ( Figure 2 、 Figure 3) describes the process of obtaining a position fix and will not be repeated here. As depicted, in Figure 4 In the case of FIG. 1 , the camera device 171 captures two optical identifiers 2, such as QR codes 2, arranged on the support structure 7 of the fuselage. However, this is only an exemplary case. Depending on the scanning position, the camera device can capture other optical identifiers 2 and more or fewer optical identifiers 2. This mechanism is similar to that of the navigation system 10 ( Figure 2 、 Figure 3 ) Positioning the autonomous mobile robot 100 is exactly the same as described.

[0084] Furthermore, if the handheld device 170 is present in the environment 1, the handheld device 170 itself may also carry at least one optical identifier 2, which may be a device described herein, for example, with respect to Figure 2 and Figure 3 The described navigation system 10 is used. The handheld device 170 can also in particular send its current position to devices such as drones 160 and autonomous mobile robots 100 that are navigated via the navigation system 10, so that the handheld device 170 can be used as an additional position reference, just like the optical identifiers 2 located at fixed positions within the environment 1, as described above with respect to Figure 2 and Figure 3 Further described.

[0085] Continue to refer to Figures 1 to 3 , Figure 5 A flow chart of a method 200 for navigating an autonomous mobile robot 100 within an environment 1 is shown. Figure 1 The autonomous mobile robot 100 Figure 2 、 Figure 3 The method 200 is performed by the navigation system 10 of FIG. The steps of the method 200 have been described simultaneously with the discussion of the navigation system 10. Therefore, for the sake of brevity, the steps of the method 200 will only be described very briefly.

[0086] In step 210 , the method 200 begins by obtaining a picture of the environment 1 . The picture may be obtained by the controller 120 via the optical sensor 110 of the autonomous mobile robot 100 .

[0087] In step 220, the controller 120 detects a visible optical identifier 2 (e.g., a QR code 2) from among the plurality of optical identifiers 2. The visible optical identifier 2 is the optical identifier 2 that is currently within the combined field of view of the optical sensors 110, as described above with respect to the navigation system 10. Each of the optical identifiers 2 encodes its fixed position within the environment 1.

[0088] In step 230 , the controller 120 decodes the visible optical identifiers 2 and determines the current position of the autonomous mobile robot 100 within the environment 1 based on the decoded optical identifiers 2 . As described above, determining the position may be accomplished by using a weighted triangulation method for each pair of visible optical identifiers 2 .

[0089] Steps 210, 220, and 230 are performed continuously while the autonomous mobile robot 100 moves through the environment 1. In other words, the autonomous mobile robot 100 determines its position within the environment 1 in real time by monitoring the optical identifiers of the environment 1 as it moves.

[0090] In step 240, the controller 120 navigates the autonomous mobile robot 100 based on the real-time positioning. Step 240 may be performed simultaneously with the continuous positioning of the autonomous mobile robot 100 within the environment.

[0091] It should be noted that "comprising" or "including" does not exclude other elements or steps, and "a" or "an" does not exclude a plurality. It should also be noted that features or steps described with reference to any of the above embodiments may also be used in combination with other features or steps of other embodiments described above. Reference numerals in the claims should not be considered as limitations.

[0092] Reference Signs List

[0093] 1 (Production) Environment

[0094] 2 Optical identifiers

[0095] 3 First subset of optical identifiers

[0096] 4 Second subset of optical identifiers

[0097] 5. First area of ​​the environment (assembly hangar; bottom floor)

[0098] 6 Second area of ​​the environment (assembly hangar; top floor)

[0099] 7 Support structure

[0100] 8 doors

[0101] 9 Body

[0102] 10. Navigation System

[0103] 11 outdoor paths

[0104] 12 Vertical support beams

[0105] 13 First Field of View

[0106] 14 Second Field of View

[0107] 15 Third Field of View

[0108] 100 autonomous mobile robots

[0109] 101 Robotic Arm / Manipulator

[0110] 102 End effector, optical scanner

[0111] 103 Pallets

[0112] 104 Robot Body

[0113] 110 optical sensors

[0114] 111 Close-range low-resolution camera device

[0115] 112 LiDAR Scanner

[0116] 113 High-resolution camera

[0117] 120 controller

[0118] 121 Data storage device

[0119] 122 Artificial Intelligence Module (AI Module)

[0120] 130 rounds

[0121] 160 drones

[0122] 161 UAV optical scanner

[0123] 162 UAV camera device

[0124] 170 handheld devices

[0125] 171 Camera (handheld device)

[0126] 172 (Handheld) working tools, optical scanners

[0127] 173 Controller

[0128] 174 smartphones

[0129] 175 adapter

[0130] 176 handle

[0131] 180 Human Operators

[0132] 200 Methods

[0133] 210 Get the picture

[0134] 220 Detection of optical identifiers

[0135] 230 Decoding the optical identifier

[0136] 240 Navigation based on optical identifiers

Claims

1. A navigation system (10) for navigating an autonomous mobile robot (100) within an environment (1), the navigation system (10) comprising: at least one optical sensor (110) attached to the autonomous mobile robot (100); a controller (120) in communication with the at least one optical sensor (110); as well as a plurality of optical identifiers (2) distributed at fixed positions within the environment (1) and capable of being detected by the at least one optical sensor (110); wherein each of the plurality of optical identifiers (2) encodes a location within the environment (1); Wherein, the controller (120) is configured to: obtaining a picture of the environment (1) by means of the at least one optical sensor (110); detecting a visible optical identifier (2) of the plurality of optical identifiers (2) within a field of view of the at least one optical sensor (110); decoding the visible optical identifier (2); and The autonomous mobile robot (100) is navigated based on the real-time positioning of the autonomous mobile robot (100) within the environment (1) using the decoded visible optical identifier (2).

2. The navigation system (10) according to claim 1, wherein: The controller (120) is configured to estimate the distance to each of the visible optical identifiers (2) and to navigate the autonomous mobile robot (100) by applying a triangulation method using each pair of visible optical identifiers (2).

3. The navigation system (10) according to claim 2, wherein: The controller (120) is configured to assign a weight to each of the visible optical identifiers (2) based on the distance; and The visible optical identifier (2) closer to the autonomous mobile robot (100) is assigned a higher weight for navigating the autonomous mobile robot (100).

4. The navigation system (10) according to any one of the preceding claims, wherein: The at least one optical sensor (110) includes at least one of a high-resolution camera (113) and a short-range, low-resolution camera (111).

5. The navigation system (10) according to any one of the preceding claims, wherein: Each of the optical identifiers (2) is a printed optical identifier (2) or a light-projected optical identifier (2) and comprises at least one of: QR code; barcode; JAB code; Aztec code; and Reference number.

6. The navigation system (10) according to any one of the preceding claims, wherein: The plurality of optical identifiers (2) comprises a first subset (3) of optical identifiers (2) and a second subset (4) of optical identifiers (2); wherein the first subset (3) is associated with a first area (5) of the environment (1); and The second subset (4) is associated with a second area (6) of the environment (1).

7. The navigation system (10) according to any one of the preceding claims, wherein: A position determination using the decoded visible optical identifier (2) is performed based on the visible optical identifier (2) by reference to a map of the environment (1) stored in a data storage device (121).

8. The navigation system (10) according to any one of the preceding claims, further comprising at least one LiDAR scanner (112) arranged at the autonomous mobile robot (100) and in communication with the controller (120); in, The at least one LiDAR scanner (112) is configured to scan the surrounding environment of the autonomous mobile robot (100); wherein the controller (120) is configured to also locate the autonomous mobile robot (100) within the environment (1) based on scanning by the at least one LiDAR scanner (112); and The controller (120) is configured to compare the positioning of the at least one LiDAR scanner (112) with the positioning of the at least one optical sensor (110) and obtain corresponding variances.

9. The navigation system (10) according to claim 8, wherein: The controller (120) is configured to: When the variance is below a first threshold, navigating the autonomous mobile robot (100) based solely on the visible optical identifier (2); and When the variance is higher than a second threshold, the autonomous mobile robot (100) is stopped.

10. The navigation system (10) according to any one of the preceding claims, wherein The controller (120) is configured to store a navigation history of the autonomous mobile robot (100); The navigation history is used as training data for an artificial intelligence module (122).

11. The navigation system (100) according to claim 10, wherein: The artificial intelligence module (122) is used to optimize the path of the autonomous mobile robot (100) and / or to identify anomalies within the environment (1).

12. The navigation system (100) according to any one of the preceding claims, wherein: Each of the plurality of optical identifiers (2) is arranged at one of the following locations: a wall within the environment (1); a support structure (7) for products to be handled by the autonomous mobile robot (100); products to be processed by the autonomous mobile robot (100); a second autonomous mobile robot (100) or another robotic system in communication with the controller (120); drones (160); a handheld device (170); or Human operator (180).

13. A handheld device (170) for performing a work task on an object (9) by a human operator (180), the handheld device (170) comprising: at least one work tool (172); Camera device (171); as well as Controller (173); Wherein, the controller (173) is configured to: obtaining, by means of the camera device (171), a picture of the environment (1) in which the handheld device (170) is operated; detecting a visible optical identifier (2) among a plurality of optical identifiers (2) arranged at fixed positions within the environment (1), wherein the visible optical identifier (2) is an optical identifier (2) within a field of view of the at least one optical sensor (110); decoding the visible optical identifier (2); and Using the decoded visible optical identifier (2), data related to the work task is associated with a work location at the object (9) where the work task has been performed based on the real-time positioning of the handheld device (170) within the environment (1).

14. An autonomous mobile robot (100), comprising: at least one optical sensor (110); as well as Controller (120); Wherein, the controller (120) is configured to: Obtaining a picture of the environment (1) in which the autonomous mobile robot (100) is located by means of the at least one optical sensor (110); detecting a visible optical identifier (2), wherein the visible optical identifier (2) is located within a field of view of the at least one optical sensor (110), wherein the visible optical identifier (2) belongs to a plurality of optical identifiers (2) located at fixed positions within the environment (1), and wherein each of the plurality of optical identifiers (2) encodes a position within the environment; decoding the visible optical identifier (2); and The autonomous mobile robot (100) is navigated based on the real-time positioning of the autonomous mobile robot (100) within the environment (1) using the decoded visible optical identifier (2).

15. A method (200) for navigating an autonomous mobile robot (100) within an environment (1), the method (200) comprising: A controller (120) obtains (210) a picture of the environment (1) via at least one optical sensor (110) attached to the autonomous mobile robot (100); detecting (220), by the controller (120), a visible optical identifier (2) from a plurality of optical identifiers (2), wherein the visible optical identifier (2) is in a field of view of the at least one optical sensor (110), and wherein each of the plurality of optical identifiers (2) encodes a fixed location within the environment (1); decoding (230) the visible optical identifier (2) by the controller (120); and The decoded visible optical identifier (2) is used by the controller (120) to navigate (240) the autonomous mobile robot (100) based on the real-time positioning of the autonomous mobile robot (100) within the environment (1).

Images