Method for controlling a mobile robot and mobile robots

Abstract

The invention relates to a method for controlling a mobile robot and to a correspondingly configured robot that can process a plurality of transport tasks, each of which involves transporting an object. The robot processes the transport tasks by receiving information before starting the respective transport task and, in a planning step, planning a route based on the information about the respective transport task, and then moving along this route. While the robot moves along the route, it uses at least one sensor to record sensor data from its surroundings, which it stores. Starting with the second transport task, the robot takes the stored sensor data into account when planning the route in the planning step.

Description

The invention relates to a method for controlling a mobile robot and to a correspondingly configured robot which can process a plurality of transport tasks in which an object is transported in each case. In this case, the robot processes the transport tasks by receiving information before the start of the respective transport task and planning a route in a planning step on the basis of the information about the respective transport task, and then moving along this route. As the robot moves along the route, it acquires sensor data from its environment with at least one sensor, which it stores. Starting from the second transport task, the robot takes into account the stored sensor data during route planning in the planning step.Robots are increasingly used to handle more specialized tasks. In particular, mobile robots can travel paths in predefined environments and thereby carry out tasks. This can be of great use especially in the laboratory environment, since robots can replace tedious routine activities and can also take over method steps which can be unpleasant for laboratory personnel or can be associated with health hazards.However, the use of robots in such environments is often still difficult because the robot must be programmed accurately in advance to be able to perform a given task securely and without errors. It must be taken into account from the outset exactly which particularities may have material to be transported and which devices are available for processing. Also, the layout and arrangement of all devices in the laboratory must be known exactly so that the robot can navigate safely. In practice, this wide range limitation of robot applicability is imposed since the arrangement of items in the laboratory changes repeatedly and equipment is exchanged, added or updated. Moreover, it would be desirable if the robot did not always have to be reprogrammed in detail for new tasks.EP 4 060 440 B1 relates to a system having at least one installation system having at least a plurality of installation parts.EP 3 864 480 B1 describes an autonomous system which is used for a production process. It includes an apparatus configured to manipulate workpieces according to the tasks of the production process. A device controller generates an autonomous system world model that includes data objects representing the respective physical objects in the production process, e.g., the workspace, the workpieces, and the device.EP 3 543 812 B1 describes a machine tool system using a plurality of mobile robots to transfer workpieces to a plurality of machine tools, the machine tool system being provided with: a machine tool control unit that outputs work requests to the machine tools; a mobile robot control unit that determines processable times for the mobile robots based on the work requests; and a determination unit that compares the processable times that are for the mobile robots and that are respectively scheduled by the mobile robots, and causes the mobile robot to perform the requested work at the fastest processable time.EP 3 482 265 B1 describes a cyber-physical production system comprising a plurality of cyber-physical units configured to jointly produce a product comprising one or more workpieces. Each cyber-physical unit comprises one or more automation system devices, a network interface and a processor. The network interface is configured to receive one or more skill instances.EP 3 451 097 A1 describes methods and systems for dynamically managing the operation of robotic devices in an environment based on a dynamically maintained map of the robotic devices. A map of robotic devices may be determined, the map including predicted future locations of at least some of the robotic devices. One or more robotic devices may then be caused to perform a task.US 2023 / 0 273 598 A1 describes a computer-implemented method that includes operating a first autonomous system to perform a task based on executable code derived from objects in a world model of the first autonomous system. The world model objects of the first autonomous system represent an operating environment of the first autonomous system. The method includes determining a trigger when the first autonomous system is to begin interaction with a second autonomous system. The second autonomous system operates based on executable code derived from a world model that includes world model objects representing an operating environment of the second autonomous system.It is the object of the present invention to specify a method for controlling a mobile robot and a mobile robot which can take account of changes in the environment in which it is moving independently and is capable of finding sequences and routes which are suitable independently for new transport tasks.The object is achieved by the method for controlling a mobile robot according to claim 1, the mobile robot according to claim 16 and the computer program product according to claim 18.According to the invention, a method for controlling a mobile robot is specified. Control can be understood here to mean that an element, which is preferably an element of the robot itself and moves with it, controls drive elements, steering elements, manipulators, sensors and / or other components of the robot such that it performs a given task, here a transport task.In the method according to the invention, the robot processes a plurality of transport tasks, i.e. at least two transport tasks. In this case, the robot transports at least one object in each of the transport tasks. In this case, the same, the same or different objects can be transported in at least two of the transport tasks.The robot processes the transport tasks in each case by receiving information about the respective transport task before the beginning of the respective transport task. A transport task can thus begin with the transfer of information about the respective transport task to the robot. The information should be information about the respective transport task, i.e. it should relate to the transport task and have an influence on the execution of the transport task. Such information can be, for example, one or more properties of the object to be transported, a content of samples, for example cells, etc., specific transport conditions such as specification or speed, fill level, storage conditions (e.g. time without cooling, purity conditions), safety requirements (e.g. protection from access of third parties or conditions to the request for handling hazardous materials) or else a storage location of the object if, for example, the object is to be picked up by the robot only in the course of the transport task. Many other such information is conceivable.The information can also already contain information about devices to be approached by the robot, such as, for example, type, manufacturer, design (e.g. in the event of a collision), interaction points (where a device can be loaded), input formats (sample carriers), software-based control (e.g. SiLA, rest, OPC UA...), offered functions, for example including the parameters (e.g. heating, shaking, etc.), load, reservation, maintenance cycles of devices. The respective information may also contain information about the space in which the transport task is to be executed. For example, the information may include or be room geometries, device locations, environmental parameters such as temperature, humidity, purity class, and the like.The information can also be, for example, information about one or more sample carriers which represent the object or with which the object is to be transported. Such information can be, for example, geometry, grip positions, storage location and / or material of the sample carriers. If, for example, the sample carrier is a microtiter plate, the robot can then take into account that it is rectangular and is to be gripped at the edge. If the transport task contains removing the sample carrier from a storage facility first, the robot can pick up the sample carrier there on the basis of the information about the storage location.The method according to the invention also comprises a planning step in which the robot plans a route for the respective transport task on the basis of the information. This planning is preferably carried out by the robot itself, i.e. by a unit which is part of the mobile robot and moves with it during the execution of the transport task. This has the advantage that external control devices are not required and information which becomes known to the robot during the execution of the transport task can be immediately taken into account by this unit. After planning the route, the robot then moves along this route thus planned.According to the invention, while the robot moves along the route, it records sensor data from its environment using at least one sensor and stores at least some, preferably all, of this sensor data. Such sensors can comprise, for example, sensors for detecting the spatial environment, such as radar or lidar sensors. The sensors can also comprise sensors for detecting devices. Here, among other things, sensors are advantageous which are configured to detect and / or read out markings on devices. Such markings, also called tags, can be e.g. QR codes, wherein the sensor would then be a QR code reader, or e.g. RFID transponders, wherein the sensor could then be an RFID reader. Other examples of such sensors are temperature sensors, brightness sensors, radioactivity sensors, and many others that can record conditions in the environment of the robot that may be relevant for the respective transport tasks. If, for example, an object to be transported is not allowed to be exposed to specific temperatures, the sensor can comprise a temperature sensor and the route can be planned in the planning step such that regions with temperatures outside the permitted temperature range are avoided.According to the invention, the robot takes into account the sensor data recorded in at least one preceding transport task at least partially in at least one future transport task. In this case, not all sensor data of the respectively immediately preceding transport task need be taken into account. Instead, individual sensor data of transport tasks that are further past can also be taken into account if this is appropriate for the transport task. If, for example, the robot transports different substances in different transport tasks, it may be expedient, when planning a new transport task, to take into account sensor data of one or more past transport tasks in which the same substance was transported as that transported in the transport task currently to be planned. A later transport task is thus planned based on the information about this respective later transport task and at least some sensor data that were stored during at least one earlier transport task. The fact that this information is taken into account or that the route is planned on the basis of this information also means here that this information has an influence on the execution of the transport task, preferably also on the route. This can preferably mean that if the sensor data were different, the transport task would also be carried out differently.In an advantageous embodiment of the invention, the robot can compare at least a portion of the sensor data recorded by the sensor with corresponding stored sensor data and / or previously stored data from a later, preferably from a second of the plurality of transport tasks. If the recorded data are not yet present, the robot can then store these, preferably in a database in which previously recorded sensor data have also been stored. If the recorded sensor data do not match corresponding stored sensor data or corresponding pre-stored data, the robot can replace the stored data with the data recorded by the sensor. This can also be done advantageously in the database. In this way, it can be ensured that, on the one hand, all relevant data are known to the robot and, on the other hand, the data are updated if circumstances change. For example, the robot can move on the basis of a map which was determined here by the sensor as sensor data and / or was at least partially stored in advance. During travel, the robot can recognize new devices to which it could, for example, dock. It can then read out, for example, a marking of such a new device and query information about the device, e.g. about a communication point, which would likewise be considered here as a sensor. For updating, the robot can then store the new device and its properties as sensor data. If, on the other hand, in this example, the robot determines, for example, that the spatial plan of the room has changed, for example by means of a LIDAR, then it can replace the corresponding area on the stored map with the new information. In a further example, the robot could also identify hazardous areas, for example areas that are too warm or too cold, and could likewise store this information. In later transport tasks, he could avoid these areas if he is transporting an object that is not to be exposed to these conditions.In an advantageous embodiment of the method according to the invention, the robot can plan the route in the planning step taking into account at least one property of the object to be transported by the mobile robot. The property of the object can be supplied to the robot, for example, as part of the information about the transport task. However, it is also possible for the robot to independently determine such a property, for example by suitable sensors or by the sample being provided with a marking which the robot can read using a suitable device. Here too, QR codes and / or RFID markings are possible, for example. An example of such a method procedure would be that the robot fetches a toxic sample at a laboratory workstation, for example a trigger, and then plans the route such that it is as short as possible and leads through areas with sufficiently high air exchange. He could also choose the route such that it only passes through rooms in which no personnel are working if the test piece could pose a risk.Properties of the object may advantageously comprise one or more selected from the following group: a content of the object which is a vessel, an admissible maximum speed, an admissible maximum acceleration, a fill level of a content of the object which is a vessel, a required or desirable storage condition and / or environmental conditions of the object, a protection requirement against access to the object by third parties and / or the like. For example, it can be taken into account whether the object as a vessel is open or closed, whether there is a risk that a liquid in the vessel may slosh over or come against a lid of the vessel, whether there is a risk that liquid is transferred from one vessel into another vessel and thus mixing or contamination occurs, and / or the like. If the object contains, for example, cell samples, it is important that these are stored at 37° C. In this case, this property can be taken into account in the selection of the route, so that the cells are not damaged. A route can also be planned in such a way that substances which influence one another are not transported together or are stored next to one another in a dedicated hotel. If an object is to be protected by third party access, the route can be planned so that it does not pass through areas in which unauthorized personnel are present.In an advantageous embodiment of the invention, the object can be sensitive to at least one environmental parameter outside a desired range. The object can thus suffer damage here if it is exposed to environmental conditions in which the corresponding parameter is outside a desired range. Such environmental parameters can be, for example, temperature, humidity, purity class and / or protection stage or safety stage. In the planning step, the route can then be planned in such a way that it extends only in regions in which the at least one environmental parameter lies within the desired region. If, for example, the object is a container with a sample of bacteria which cannot be transported outside S2 laboratories, then in the planning step the route can be planned in such a way that the object is transported only inside laboratories with at least the protection level S2.In a further advantageous embodiment of the invention, the transport task can serve for processing the object transported by the mobile robot by one or more devices. The object can thus be transported by the robot to the at least one device or from device to device. The route would then be planned accordingly to the device or between the devices. For example, the transport task can have several steps, such as "move from the incubator to the microscope, liquid handler and back again.". A transport task could also be "more microscopy sample XY every 6 hours (wherein it is stored where the sample is located, where the next device is standing and the like) and automatically return the sample.". Another example would be a command such as "transport the samples of the current day to the warehouse every free day at 18 AM.". Another example could be "thaw all half-full pipette boxes in the prints against new boxes.".In an advantageous embodiment, as already mentioned, the sensor can be a sensor for detecting devices. In this case, the sensor can be particularly advantageously a sensor for detecting at least one marking on at least one of the devices. The sensor may scan the marker and derive sensor data from the scan. Suitable markings are, for example, QR codes and / or RFID markings. The sensor would then be a corresponding sensor for reading out this marking.The robot can then advantageously plan the route in the planning step such that the object is transported at least to a device suitable for processing, which device has optionally been recognized as a new device by the sensor data. The object can then be processed by at least one of the devices during the processing of the respective transport task. Sensor data which can be recorded by the at least one sensor can comprise, inter alia, for example a variable selected from the following, wherein the sensor is then in each case a suitable sensor for detecting these variables: presence, identity, type, manufacturer, design, software control, at least one offered function, load, at least one reservation, at least one maintenance cycle, at least one location, at least one transfer position, at least one standby state and / or at least one operating state of at least one of the devices by means of which a processing step is carried out on the object. Information can alternatively or additionally be present for determining one or more of the following variables: at least one environmental parameter, at least one temperature, at least one humidity, at least one purity class, at least one required protection level and / or at least one geometry of a room in which the robot moves.The following circumstances can be determined, for example, by the at least one sensor: it can be determined, for example, whether a device is occupied by ongoing processes or personnel. For example, it may also be determined whether a device is already heated, or is in a fault condition, or is uncalibrated, or is not ready to accept a sample. Advantageously, the method according to the invention can permit general requests in the processing of which new devices can be integrated independently. If, for example, it is formulated as part of a transport task that a sample is to be weighed, the robot can travel independently to the next newly recognized scale.In an advantageous embodiment of the method according to the invention, the robot can modify the route for at least one of the transport tasks during the processing of this transport task on the basis of sensor data recorded during the processing of this transport task. For example, the robot may recognize an object such as a chair that blocks the intended route. The robot may then modify the route to bypass the object. It is also possible that the robot recognizes that it is a moving object and modifies the transport task so that it pushes the object aside. A further example can be that the robot recognizes hazardous areas and leaves them independently, for example by safety doors.In a further example, the robot can recognize a microtiter plate on a table and optionally verify it, e.g. by means of a barcode. It can then establish, for example, that this is the microtiter plate which it is intended to transport to an incubator, for example, for processing the transport task. It can then accommodate these and adapt the transport task such that the microtiter plate is transported to the incubator.Possible objects are in principle all objects which can be transported for processing the transport tasks as illustrated. A method for controlling a mobile robot in a laboratory is particularly advantageous. Alternatively, however, the robot could also be set up, for example, for transporting medicaments or meals in a hotel or a hospital.Examples of typical objects which can be transported in the method according to the invention are disposables, labware, microtiter plates, samples. Typical transport tasks, for example with the aforementioned objects, can be the execution of at least one biological, chemical or physical examination of a material in the object or of a material of the object. Such objects and transport tasks are typical in laboratories.In an advantageous embodiment of the invention, the steps of the method which follow the reception of the transport task can be repeated one or more times for the same transport task or can be carried out multiple times depending on previously defined conditions. Such a configuration is suitable in particular for having tedious routine activities carried out by the robot.An advantageous embodiment of the invention can provide for optimizing the planned route in the course of a plurality of executions of a specific transport task or a plurality of transport tasks. In such a configuration, the robot can plan the route in at least one of the transport tasks on the basis of previous processings of transport tasks such that the planned route comes closer to a predefined optimum with respect to at least one property than routes of the previous processings of transport tasks. This optimization is possible in a particularly advantageous manner if a plurality of identical transport tasks are carried out or the same transport task is carried out a plurality of times, for example when implementing routine activities.Advantageously, the robot can have a storage device in which the transported object is stored during the transport under the control of at least one condition. For example, this condition can be at least one selected from the following: temperature, air humidity and / or purity class.In an advantageous embodiment, the at least one property of the object, for example its weight, its nature and / or its composition, can represent a risk or risk for parts of the environment. The robot can then advantageously plan the route such that it extends only in regions for which the corresponding properties of the object represent no risk or an acceptable risk.A further advantageous embodiment of the invention provides that a three-dimensional scene is generated in advance from a space in which a given transport task is to be processed. This can then be combined with a model recorded by the robot as sensor data, for example a three-dimensional model, and the route can be planned in the planning step based on this combination.According to the invention, there is also provided a mobile robot including a robot body and a moving device for moving the robot body. According to the invention, the following are arranged in and / or on the robot body: a communication interface which is configured to receive information about the respective transport task, at least one sensor which is configured to record sensor data from a surrounding area of the mobile robot, a database which is configured to store the sensor data recorded by the sensor over the course of a plurality of, i.e. at least two, transport tasks, a planning unit which is configured to predict a route from the transport task and sensor data recorded by the at least one sensor and / or stored in the database, and a control unit which is configured to control the movement device such that it moves the robot body along the route. Because in particular the planning unit is arranged in or on the robot body, i.e. is part of the robot, it is independent of external controls to which the robot would have to maintain communication. As a result, the robot can also move safely in areas which are shielded from, for example, electromagnetic radiation, such as laboratories in which a larger amount of radioactivity arises. The arrangement of the database, the planning unit and the control unit on or in the robot body results in them moving together with the robot body when it moves by means of the movement device.The sensor can be, for example, one or more selected from the group comprising: a 1D code scanner, a 2D code scanner, a stereo video camera, a video camera for code and / or object recognition, a temperature sensor, an air humidity sensor, a distance sensor, an air pressure sensor, an acceleration sensor, an ultrasonic sensor, a radar sensor, a LIDAR sensor, a Bluetooth sensor, a 5G or RFID localization sensor, a sonar, an infrared sensor, a 3D sensor system, a weight sensor, an acceleration sensor and / or a force sensor.The robot is particularly advantageously configured to carry out the method described above for controlling a mobile robot.Advantageously, the robot can also comprise one or more of the following units:• A knowledge management unit, which may be a module for managing environmental knowledge. This can ensure connection to an information system, update the internal database and / or provide information to external systems.• An internal database capable of storing information for later processing.• A process manager with which external tasks can be managed. This can, for example, convert the transport tasks into an internal format and / or pass on a process progress to an external system.• A task generator, which is a module that generates environmental information, accurate instructions for the robot, and other devices from the transport tasks.• a time scheduler which monitors e.g. the task to be performed and ensures proper temporal execution thereof.• A device control which can control external devices, e.g. via a digital interface.• A mobile robot controller controlling the mobile robot.• a task optimizer that optimizes execution of the transport task after each task and learns how tasks can be performed better. In this way, it can support the generation of new tasks in the task generator.The control of the mobile robot can be executed in a computer-implemented manner. For this purpose, the mobile robot can have at least one control unit which is configured to carry out the receiving and transmitting steps such as the planning steps. Data, such as the sensor data, may be stored in at least one storage unit.The invention also relates to a computer program product comprising a program with instructions which, when the program is executed by a mobile robot, cause the latter to execute the method according to the invention.The invention is to be explained below by way of example with reference to a few figures. Identical reference numerals identify identical or corresponding features. The features described in the examples can also be realized independently of the example and can be combined between the examples.It shows: FIGS. 1A, 1B and 1C show a sequence of a method according to the invention, FIGS. 2A and 2B show a sequence for processing a transport task, FIG. 3 shows a structure of a mobile robot according to the invention, FIG. 4 : shows a sequence for executing a transport task, FIG. 5 : possible communication paths and data flows during the method according to the invention, FIG. 6 : shows a situation in which a mobile robot according to the invention carries out automated recognition of an object and a table by means of tags, FIG. 7 shows an algorithm for optimizing and updating the position of markings,FIGS. 1A, 1B and 1C show, by way of example, a sequence of a method according to the invention in which a mobile robot processes a plurality of transport tasks in which an object is transported in each case. The robot processes the transport tasks in each case in that, before the beginning of the respective transport task, it receives information about the respective transport task, in a planning step plans a route for the respective transport task based on the information about the respective transport task, and then moves along this route, wherein the robot, while moving along the route, acquires sensor data from its environment with at least one sensor and stores at least some of the data, wherein, from a second transport task in the planning step, the robot plans the route for the respective transport task based at least on the information about the respective transport task and at least some sensor data stored during the processing of at least one earlier transport task.The method shown starts with a step 1, Information files are then initially loaded into a memory of the robot (step 2), such data 3 can originate, for example, from a building information model (BIM) or a plan of the room, for example as a PDF, architectural plan or the like.In a step 4, a data format of the data 3 loaded into the robot can optionally be determined. If the data are those from a building information model, then in step 5 relevant information such as doors, rooms etc. can be converted into a target 3D format. If the data are present as PDF or 2D data, 3D data can be generated in step 6. Here, for example, basic information such as building numbers, floors, scales, etc. can be detected (step 6a). Spaces and Flure can be detected (6b). Some elements may be detected via classification, such as walls, doors, escape routes (step 6c). A 3D base model per space may then be rendered (step 6d). Both the data generated in step 5 and in step 6 can then be stored in a database 7. The data thus generated can then be displayed from steps 5 and 6 and / or from database 7 in step 8 as a basic model per room, and a user can be input for adaptation with further information.In step 9, self-learning or auto-teaching with position detection then takes place. For this purpose, the robot can pass through steps 9 a, 9 b, 9 cand 9 d. In this case, in step 9 a, sensors of the robot are queried. In step 9 b, the robot can execute a rotational and / or travel movement. In step 9 c, the sensor data for a point cloud can be merged or painted. In step 9 d, the sensor data is then matched to the currently available basic models. It can now be checked in step 10 whether the position fits or not. If it does not match, the method can either return to step 9 and the described process can be carried out again or the user can carry out the positioning manually in step 11.In step 9, the user thus starts the self-learning process via the interface. The mobile robot then attempts to detect its own location via the current sensor data and the already generated specific features. This location can then optionally be confirmed or corrected by the user. If no information on the building information model or construction plan has been transferred in step 2, the user can define the position of the mobile robot by hand.If the position is fixed, a three-dimensional detection of the space and its devices can take place in step 12. For this purpose, steps 12 ato 12 f, for example, can be carried out. For example, in step 12 a, a travel movement of the robot through the space can take place. The robot therefore travels independently through the room or rooms and records data with its sensors (for example RFID sensor, etc.) (step 12 b). Such data may include, for example, walls, desks, doors, chairs, appliances, etc. These can then be stored in the internal building / room plan. The acquired sensor data may be added to the base model (painted (merged) with the base model) in step 12 c. In step 12 d, the sensor data can then be differentiated in the basic model. In step 12 e, remaining point clouds can be identified as objects that have not yet been stored. In step 12 f, point clouds can then be detected and a comparison can be made in the database for device models.As the robot moves in space (12a), the sensor data is acquired (12b). This data (e.g., point cloud) is matched to the 3D scene (generated 3D model). Here, walls can be clearly referenced as walls, tables as tables, etc. If a device is standing on a table, the outline of the device is mapped as a point cloud, for example. If this device is integrated in the 3D scene, it can be directly assigned. Once the device has been newly placed on the table, this point cloud is not classified as a table or a wall. In this case, this point cloud is referred to as an unknown object and it must be matched to device models (CAD files, 3D Model,... ) in a database. If a match results in this comparison, the device was successfully identified.During the acquisition of sensor data in steps 12 and 12 ato 12 f, the user can optionally control the movement of the robot at any time via an interface, i.e. in particular the direction of his movement, his speed etc.After training the room or all rooms, the user may then have the option of viewing, editing and confirming the information. This can be done, for example, by augmented reality using a tablet or the like (step 13). This can end the learning of the room or building.Depending on the situation, the method can now be continued in 14 in order to train devices. If models of the devices are present in a database 7 and if this database is accessible to the mobile robot, the models can be matched to the recognized devices and transfer positions for the objects resulting therefrom can be derived. The accessibility of these devices can then be checked and, in the case of non-accessibility, the device can be classified or marked accordingly. This is illustrated in steps 15a and 15b.It is also possible to identify devices on the basis of a marker (step 16 a). In step 16 b, the transfer position can then be queried based on the device model or stored with information.It is also possible that a device with an unknown transfer position is detected (step 17). Either a marker can then be attached to the device, the transfer position can be manually taught (step 18 a), and the transfer position thus defined can be stored in the marker or in the database (step 18 b), or the transfer position can be detected by markers or by object detection by means of a sensor system (e.g. optically by radar etc.) (step 19). If the devices have been recognized and the transfer position has been taught, then the teaching of the devices is completed in 20.There may be different methods depending on whether models of the devices are stored or not. If one or all models are not stored, the user can automatically learn at least one device. For this purpose, the user can drive the mobile robot, for example, in front of the device. In the corresponding space model, the device may be selected and the user may add information such as name, name, manufacturer, interface function, functionalities, etc. The user may then manually open the handover position and place a training object on the handover position. This object can now be recognized by the mobile robot. If a manipulator is present, it can be moved to a height predetermined by the device for this purpose in order to identify the transfer position. This can then be stored in the device module.In steps 18 aand 18 b, the robot can search for markers attached to the device by means of further sensors and these can then be classified by the user as additional information for the device. Correspondingly classified information can then be stored with the device model when the process is ended.FIGS. 2A and 2B show, by way of example, a sequence for processing a transport task. The sequence starts with step 21, and in step 22, first, a job assignment takes place, for example via a graphical interface (GUI). The job can optionally be confirmed in step 22a and incorporated into a job list in step 22b.In step 23, the process planning then follows, which will be described in detail below. The process is then carried out in step 24, and an update, for example of weights for the route planning, is carried out in step 25.In the example shown, the process planning 23 contains the following substeps:The process planning initially contains a route planning 23 a, which will be described in more detail below. Moreover, a transport time can be estimated in step 23 band resources for the process can be scheduled in step 23 c. This can comprise, in particular, reservations for the devices 23 ca. The information obtained in this way can then be transferred in step 23 dinto a processing list.The issued job can be included in a job list (queue) (step 22b). Based on information about the object to be transported, such as weight, size, chemical properties, toxicity, etc., as well as additional information of the order, such as prioritization, conditions to be observed, access restrictions and the destination / destination, the route can be planned taking into account the danger and safety areas, the volume of people in the rooms, and the like, as well as the time required for this. In addition, the devices involved and their utilization, including reservations of the devices for manual operation by users, can be taken into account. Based on the route plan obtained in step 23, devices can then be reserved accordingly and the route can be included as a transport task in the processing list 23 band a confirmation of the order can be fed back.Reference numeral 26 shows a diagram which identifies route planning which is possible in step 23 aby way of example. In this connection, S1 to Sn identify laboratories, F1 to Fm identify flure and L1 to Lp identify stores. Wabx are weight values of the respective routes.An example of such a transport task is to be reproduced below. In this case, a sample is to be transported from laboratory S1 to laboratory S5. There are thus the following possible routes1) S1-F1-F2-S52) S1-F1-L1-L2-F2-S53) S1-S2-S3-S4-S5.Let us now take as an example the time of transport Täusag, 12 o'clock. Assume that too great a load exists on route 1) at F1-F2. Route 2) can be chosen for non-toxic samples, for example, it can be assumed that the maximum velocity from L1 to L2 is much greater than the maximum velocity from S1 to S5. Route 3) could be suitable for toxic samples, for example.As weights of the respective routes Wabx, for example, the sum of distance, load, dependence on time, maximum speed, etc. can be selected.The load (t) for route Wf 12 could be, for example, as follows:Load=5 at 6 o'clock<t<11 o'clock,Load=10 at 11 o'clock<t<13 o'clock,Load=3 at 13 o'clock<t<18 o'clock,Load=1 at 18 o'clock<t<6 o'clock,Load=1 at idle day 18 o'clock<t<mon day 6 o'clock.The routes may also have clearance values depending on properties of the object. For example, the release for Wsf, Wlf and Bf may be as follows:Release = -1 at property = toxic when included in weight.As a filter setting in a database, clearance toxic=1 could mean that the route is returned with clearance.The route can be prioritized for a plurality of samples by specifying the properties. For example, toxic priority may mean 10, i.e., when a toxic sample is transported together with others, the toxic sample may determine the route as long as it is on the mobile robot. Because a toxic sample is transported with high priority, the route is selected such that it meets primarily the properties of the toxic sample, i.e., for example, in particular that no danger occurs due to the route.The route can be calculated as a function of the respective algorithm, e.g. by means of a Dijkstra algorithm.The process execution 24 can then take place, for example, as follows. The mobile robot can pick up the object to be transported from a respective transfer station and carry out the transport along the already planned route. In this case, the time actually required can be matched to the calculated anticipated required time. In the case of deviations that exceed a threshold value, the weighting of the respective route section can be adapted. This adjustment can take place not only with respect to the required time, but also with respect to further parameters, for example, which can be assigned to the route sections as meta information, such as energy consumption for a route, access restrictions, environmental parameters in the route profile, general prioritization, route length, permitted / possible travel speed, number of obstacles, etc.This information can be constantly updated by processing the orders and then be included in the calculation of the route.If a device to be loaded is occupied by a user in the meantime, for example, another device can be automatically selected or the object can be correspondingly safely stored again so that the samples are not destroyed.By continuously recording and checking data of the sensors in alignment with the own created model, the user can be informed as soon as a learned device is no longer located in the area of the mobile robot.In addition, loads on the devices can be analyzed, in order to be able to optimize the load on the devices in future orders.By data acquisition of the sensors and object recognition, the occurrence of persons and obstacles on the routes of the robot can also be detected and stored as a criterion with weighting at the planning unit.FIG. 3 shows, by way of example, a schematic structure of a mobile robot according to the invention. This has a robot body 31 and a movement device (not shown here) with which the robot body 31 can be moved. In the robot body 31, a communication interface 32 ais provided, which is configured to receive information about a respective transport task. This information may contain knowledge 33, which may be derived, for example, from a data record or database 33 aand / or from a building information model, building information model 31 b.The communication interface 32 may also enable communication with process description software 34, but does not mean a scheduler and the like.The mobile robot also has a sensor 35 in the robot body 31, which is configured to record sensor data from a surrounding area of the mobile robot or of the robot body 31.In addition, the robot has an internal database 36 in the robot body 31, which database is configured to store the sensor data recorded by the sensor 35 over a plurality of transport tasks.In addition, the robot also has a planning unit 37 in the robot body 31, which planning unit is configured to predict a route from the transport task and from the sensor data recorded by the at least one sensor 35 and / or stored in the database 36.In the example shown, the planning unit contains a task generator 37a (task generator 37a) and a task optimizer 37b (task optimizer 37b), which communicate with one another.The robot further includes, in or on the robot body 31, a control unit 38 configured to control the traveling device so that the robot body 31 travels along the scheduled route.In the example shown, the robot in the robot body 31 also has a control device 39 and a scheduler 40 (time scheduler). These can together with the task optimizer 37 bcalculate an optimization of the route over the course of a plurality of transport tasks.FIG. 4 shows, by way of example, a sequence for executing a transport task. Materials are provided for a labeling step in step 41. It is then checked in step 42 which materials (type, quantity) are required. Step 43 also determines which devices are required (i.e., function and availability) and step 44 checks which environmental parameters are to be observed. In addition, information for bar codes is determined in step 49.If it has been checked in step 42 which materials are required, it is possible in step 45 to check where these materials are present in sufficient quantity and in step 46 to determine whether different storage locations have to be approached in order to obtain the materials. Using this information as well as the devices determined in step 43 and the possibly defined environmental parameters in step 44, a route can then be determined in step 47. This route can be optimized in step 48. Based on the optimized route and the information for barcodes ascertained in step 49, commands can then be generated in step 50, which are executed in step 51.An abstract transport task may e.g. look as follows: 1. a laboratory schedules his experiments for the next day. 2. a process software generates a task plan from the planning, including the available device classes. 3. the next day the scheduled tasks are executed by the process software. 4. the mobile robot performs the individual tasks using the information available in the knowledge database to available devices and their control, labware and samples. 5. a task can have several sub-tasks, for example, such as, for example, the collection of a consumable with samples, a selection of a suitable analysis device, transport, evaluation and further transport.An advantage of this procedure is that it is not necessary to always monitor and keep ready the availability of device functionalities at the upper level. The mobile robot can itself decide which device is being operated, taking into account the necessary functions and / or the required device classes. Only software is required for task (process) description, a task manager software is no longer required.A consumable-specific transport task is described below by way of example. 1. environmental conditions / parameters as a function of a transport goods (consumable sample) are detected and / or evaluated. 2. for example, I. Adjusted route planning (e.g., no transport through public areas for data protection-sensitive sample material). Environmental parameters: Non-departure from clean room conditions or conditioned areas or interior / exterior areas; Non-driving over of protection areas depending on the transport goods. III. Adjusted Acceleration Velocity Parameters to Avoid Spilling or Loading of Samples. Closing the on-board hotel in sensitive samples against leaving them open in non-critical. V. The geometry of the consumers can result in the use of another mobile robot that has suitable transport properties.In this way, the robot can behave in an adapted manner during the transport of samples and / or labware and the like.FIG. 5 shows, by way of example, possible communication paths and data flows during the method according to the invention. Here, 51 devices with tags 51 a, 52 identify a laboratory, 53 identify a laboratory, 54 identify task management software such as schedulers, PMS, 55 identify environmental sensors, 56 identify database / data record, 57 identify knowledge, 58 identify digital representation of the laboratory and 59 identify consumers with or without content (e.g. reagents, cell culture / medium, solids, etc.). In this environment, the mobile robot 31 moves with the sensor 35.FIG. 6 shows, by way of example, a situation in which a mobile robot 31 according to the invention carries out an automated identification of an object 63 by means of a tag 63 aand a table 64 by means of a tag 64 a.For this purpose, the robot 31 has a transport device 65, by means of which it is mobile in a laboratory. It also has a first sensor 61 athat is configured to read out a manipulation TAG (man TAG) 63 aof an interaction object 63. In the example shown, the sensor 61 ais located at a distance d 1 from day 63 a. The sensor 61 acan be a distance sensor with which the distance d 1 to the man-tag 63 acan be determined. A manipulator 62 can be controlled via the distance sensor 61 aand the tag 63 ato grasp the interaction object 63. At the same time, the robot 31 can navigate with respect to the table 64 by means of a navigation tag (Nav tag) 64 a. For this purpose, for example, a navigation sensor 61 bdetermines the distance d 3 from the table 64. The navigation tag 64 acan be assigned a spatial position P i which has a distance d 2 from the table 64 in the horizontal direction. This point P i can be the point to which the robot 31 must travel in order to be able to grasp the interaction object 63 with the manipulator 62. Control at this point may be by the second sensor reading the navigation tag 64 a.Simultaneous localization and mapping (simultaneous localization and mapping) in the laboratory (laboratory SLAM) can be carried out as follows: 1. the mobile robot travels through a laboratory 2. during travel (as a task for teaching or travel within the framework of a task), it monitors the environment 3 by means of a sensor system. if a new device is detected during travel, the robot scans the attached day and transmits this information to the task manager SW 4. If it is detected that a device has been removed from the laboratory, this information is transmitted to the task manager SW and a decision is made there. a. This can mean that the device concerned is removed immediately from the system, b. or is deemed to be missing, so that after a time X it is removed if it should not appear again, c. or that the mobile robot is given the task of checking whether the device is actually not present. d. However, it is also possible to let the laboratory party check the presence of the device.The mobile robot, in this example, optionally locates newly added equipment in the laboratory. The user does not need to learn or otherwise enter a device with respect to its position. The evaluation of the tag (i.e. the unique device identification) makes it possible to know from a knowledge database (DB) (e.g. manufacturer DB, company DB, laboratory DB, etc.) how the device (e.g. what type, which manufacturer, which the device can, etc.) can be operated. The robot can notify it to another system (e.g., task manager software).Advantages are in particular the following: Effort for training is reduced, inventory lists can be automatically created, building plans (e.g. BIM) can be automatically updated, automatic assignment of individuals of a device parkour to laboratories is possible, devices registered in laboratories can be sorted out automatically, disappeared / remote devices can be recognized and lead to an evaluation, e.g. as a reference to a laboratory.FIG. 7 shows, by way of example, an algorithm for optimizing and updating the positions of markings (as also referred to above as tags). In step 71, a predefined absolute preliminary position for the marking 64 ais predefined. Together with a learned position from a learning step 77, a desired position P i in front of the marking 64 ais obtained in 72. This can be calculated individually in 79, for example from a predetermined relative preposition to the marking 64 ain 80.From the desired position determined in 72 in front of the marking 64 a, an approached position 73 (P i) can then be determined, at which a recognition of the marking 64 ais possible. The marking 64a is then identified in step 74. If the marking 64 ais recognized, the method proceeds to step 78, where the relative position of the robot P i relative to the marking 64 ais calculated. Based on this, a target approach can then take place in step 81 and docking can take place. The relative position from step 78 can also be used in step 82 for recalculating the desired position before the day 64 awhen the absolute position of the robot deviates due to recovery or the relative position deviates due to a displaced day.If the day 64a was not recognized in step 74, a recovery procedure 75 for finding the marking can be started. If the marking 64 ais detected here, the method can proceed to step 78. If the marking 64 ais not recognized here too, an error message can be output, for example, as the next escalating stage in step 76, or the method can be terminated.Continuous autonomous learning of device transfer positions with the aid of laboratory SLAM can be implemented, for example, as follows. 1. tags 63 a, 64 aare detected by the mobile robot 31 by means of sensors during the initial driving and also during operation. If a tag 63 ais detected that it has not yet been linked to this laboratory in the database, the task management software or the mobile robot checks whether sufficient information regarding the device is present on the basis of the data position. a. If sufficient information regarding the device is present, the task management software can access the device. e.g. lack of information such as the accessibility of the device's own transfer positions relative to the tag, then these are requested by the laboratory-> Manuelles teaching (training) of devices c. If a tag of a specific tag class is detected, it is related to the associated tags in its environment (e.g. navigation tag by manipulation tag by nest tag). If the tag cluster is incomplete based on the information from the database, the near field of the tag is searched for associated tags and the associated entry in the database is marked accordingly if the data record should be further incomplete.An advantage of this is that training by a laboratory staff is omitted or at least becomes simpler. On the next or also on each start-up, the preposition for starting up a device can be updated / optimized (see algorithm).An external update of the mapped / mapped laboratory can take place, for example, as follows: 1. the laboratory currently held by Lab SLAM can be updated proactively from the external. 2. the laboratory plan with the devices created there can be depicted in a digital representation. 3. The laboratory updates the digital representation by adding or removing devices. 4. the localization of the new devices based on the digital representation is transferred to the mobile robot. 5. the localization of the device is initially roughly detected by the mobile robot. However, in the task management software, the device is already managed based on its information and can be used for scheduling if the information is complete. 6.If the mobile robot passes the newly added device, it will transmit the additional information that it can acquire during the course of the LabLAM to the database. 7. if the mobile robot receives the task of operating the added device, the information that is then still missing is acquired on the basis of the tags and added to the data record in the database.Advantages are as follows: Devices need not be taught and one need not wait for the device to be accidentally discovered within the continuous teaching. This is particularly useful when the added device is located in an area that is rarely detected or not detected by the sensor system of the mobile robot.

Claims

Method for controlling a mobile robot (31), wherein the robot (31) processes a plurality of transport tasks in which at least one object (63) is transported in each case, wherein the robot (31) processes the transport tasks in each case by receiving information about the respective transport task before the start of the respective transport task, and in a planning step, based on the information about the respective transport task, plans a route for the respective transport task, and then moves along this route, wherein the robot (31), while moving along the route, uses at least one sensor (61a, 61b) to record sensor data from its environment and stores at least some of the sensor data, wherein the robot (31) plans the route for the respective transport task from a second of the plurality of transport tasks in the planning step at least on the basis of the information about the respective transport task and at least some sensor data stored during the processing of at least one earlier transport task, wherein the robot (31) plans the route in the planning step taking into account properties of an object (63) to be transported by the mobile robot (31).Method according to one of the preceding claims, wherein the robot (31), starting from a later, preferably a second of the plurality of transport tasks, compares at least a part of the sensor data recorded by the sensor with corresponding stored sensor data and stores the recorded sensor data in a database if they are not yet present and / or replaces the corresponding stored sensor data with the sensor data recorded by the sensor if they do not match.The method according to the preceding claim, wherein the characteristics of the object (63) comprise one or more selected from the following: a content of the object (63) being a vessel, a permissible maximum speed, a permissible maximum acceleration, a fill level of a content of the object (63) being a vessel, a required or desirable storage condition and / or environmental condition of the object (63), a protection need against access to the object (63) by third parties.Method according to one of the preceding claims, wherein the object (63) is sensitive to at least one environmental parameter outside a target range, wherein preferably the environmental parameter is at least one selected from temperature, air humidity and / or purity class, and wherein in the planning step the route is planned such that it runs only in regions in which the at least one environmental parameter lies within the target range.Method according to one of the preceding claims, wherein the transport task serves for processing an object (63) transported by the mobile robot (31) by one or more devices.Method according to one of the preceding claims, wherein the at least one sensor (61a, 61b) is a sensor for detecting devices, wherein preferably the sensor (61a, 61b) scans at least one marking (63a, 64a) on at least one of the devices and derives the sensor data from this scan.Method according to one of the preceding claims, wherein the robot (31) plans the route in the planning step such that the object (63) is transported to at least one device suitable for processing, which device has preferably been recognized as a new device by the sensor data, wherein the object is processed by at least one of the devices during the processing of the respective transport task.Method according to one of the preceding claims, wherein the sensor data recorded by the at least one sensor (61a, 61b) comprise one or more selected from the following: a presence, an identity, a type, a manufacturer, a design, a software-related control, at least one offered function, a utilization, at least one reservation, at least one maintenance cycle, at least one location, at least one transfer position, at least one standby state and / or at least one operating state of at least one of the devices, by means of which a processing step is carried out on the object, at least one environmental parameter, at least one temperature, at least one humidity, at least one purity class, and / or at least one geometry of a space in which the robot (31) moves.Method according to one of the preceding claims, wherein the robot (31) modifies the route for at least one of the transport tasks during the processing of this transport task on the basis of sensor data recorded during the processing of this transport task.The method according to any one of the preceding claims, wherein the object is a disposable, labware, a microtiter plate and / or a sample and the transport task is an execution of a biological, chemical or physical investigation of material in or from the object.The method according to any of the preceding claims, wherein the steps of the method subsequent to receiving the transport task are repeated one or more times for the same transport task or are performed multiple times depending on predetermined conditions.Method according to one of the preceding claims, wherein the robot (31) plans the route in at least one of the transport tasks on the basis of preceding manipulations of transport tasks in such a way that the planned route comes closer to a predefined optimum with respect to at least one property than routes of the preceding manipulations of transport tasks.Method according to any one of the preceding claims, wherein the robot (31) comprises a storage device in which the transported object is stored during the transport under the control of at least one condition, wherein preferably the at least one condition is at least one selected from the following: temperature, air humidity and / or purity class.Method according to any of the preceding claims, wherein at least one property of the object (63), for example its weight, its nature and / or its composition, represents a risk or risk for parts of the environment, and wherein the robot (31) plans the route so that it only runs in areas for which the corresponding properties of the object (63) do not represent any or an acceptable risk.Method according to one of the preceding claims, wherein a previously generated 3D scene of a room in which the respective transport task is processed is combined with a 3D model recorded by the robot (31) as sensor data and the route is planned on the basis of this combination.A mobile robot (31), comprising a robot body, a moving device (65) with which the robot body can be moved, wherein the following are arranged in and / or on the robot body: a communication interface which is configured to receive information about a respective transport task, at least one sensor (61a, 61b) which is configured to record sensor data from a surrounding area of the mobile robot (31), a database which is configured to store the sensor data recorded by the sensor (61a, 61b) about the course of a plurality of transport tasks, a planning unit which is configured to predict a route from the transport task and sensor data recorded by the at least one sensor (61a, 61b) and / or stored in the database, a control unit which is configured to control the moving device in such a way, said robot body moving along said route, said mobile robot (31) being adapted to perform a method according to any of claims 1 to 15.The mobile robot according to the preceding claim, wherein the at least one sensor (61a, 61b) is one or more selected from the group comprising: a 1D code scanner, a 2D code scanner, a stereo video camera, a video camera for code and / or object recognition, a temperature sensor, a humidity sensor, a distance sensor, an air pressure sensor, an acceleration sensor, an ultrasonic sensor, a radar sensor, a LIDAR sensor, a Bluetooth, 5G or RFID localization sensor, a sonar, an infrared sensor, a 3D sensor system, a weight sensor, an acceleration sensor, a force sensor.A computer program product comprising instructions which, when the program is executed by a mobile robot (31), cause the latter to carry out the method according to any one of claims 1 to 15.

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