Method and system arrangement for optimizing production planning or planning of products and articles in production, transaction or distribution of

Abstract

In order to optimize the production planning or workshop logistics of products and items in the production, transaction or distribution of products and items (wherein (A) the problem of maintaining or keeping up-to-date the data about (i) the production planning of products and items or (ii) the logistics-related workshop in the production, transaction or distribution of products and items is solved. And (B) at least one mobile logistics robot (MLR1, MLR2, MLR3) for automating the logistics transportation of the products, the articles or the production materials of the products and the articles in the workshop (SHF) is each equipped with a sensor technique (SST) suitable for measuring or capturing and controlling the current state or change of the workshop by means of sensor data (SSD1, SSD2, SSD3), when each robot is managed by a fleet management system (FMS) for allocating or scheduling logistics tasks (LT) among at least one robot executing (exe) transport tasks (TT) of a transport task queue (TTQ), according to the invention, the transport task queue (TTQ) is maintained (mta) by an automated logistics planning system (ALPS) for planning logistics transportation to which a robot and fleet management system is assigned, it is proposed that (1) workshop master data (SHFMD) is stored (stoo) in a database (DB), which workshop master data (SHFMD) is used to plan workshop logistics and can be loaded into the system, the method comprises the steps of: (1) acquiring sensor data, (2) extracting (ext) workshop-related information (INF1SHF, INF2SHF, INF3SHF) from measurements or captures based on the sensor data, (3) submitting (sbm), by each of the at least one robot, the workshop-related information to a data fusion facility (DFF) for collecting the workshop-related information (INF1SHF, INF2SHF, INF3SHF), and (4) acquiring the workshop-related information (INF1SHF, INF2SHF, INF3SHF) by merging (mrg) the main data with the collected information in a corresponding manner. The master data is updated (upd) via a data fusion facility capable of accessing the database.

Description

Technical Field

[0001] The present invention relates to a method for optimizing production planning or workshop logistics of products and articles in the production, trading or distribution of products and articles according to the preamble of claim 1, and a system arrangement for optimizing production planning or workshop logistics of products and articles in the production, trading or distribution of products and articles according to the preamble of claim 9. Background Art

[0002] To optimize production planning or shop floor logistics for producing, trading, or distributing products and items, it's essential to have knowledge about the shop floor—specifically, the use of production equipment, the location of assets, the utilization of buffer areas, and information about the distribution of mobile equipment and human workers. Tracking this knowledge is rarely automated, and, especially in low-volume, high-mix production, this information changes rapidly as new production tasks are added.

[0003] There are existing technical solutions for automatically tracking the location of assets in a plant. These solutions are real-time location systems, often referred to as "real-time location systems" <rtls>Regarding these RTL systems, there are several existing technical implementations, such as using Bluetooth or ultra-wideband beacons and receivers. In these systems, beacons are attached to the assets and materials to be tracked. RTL systems allow having up-to-date information on the shop floor data, but at the expense of installing significant infrastructure (e.g., receivers and beacons). Alternatively, if an RTL system is not used, information about the production shop floor is kept up to date by predicting the state of the shop floor from previously known states and manually checking the actual state to correct the prediction.

[0004] US2022 / 300875A1 discloses a system having at least one factory system, which has at least multiple agents, at least multiple autonomous mobile robots, movable machinery and multiple sensors, wherein the sensors are used to generate data for use in safety-related protection of the factory system, wherein there is a local factory safety system associated with the factory system, wherein the data of the local factory safety system is stored in a first database, wherein the local factory safety system has a first data model, which has a data set of the factory system, wherein there is a decentralized public factory library system associated with the local factory safety system, wherein the factory safety system and the factory library system are connected to each other via an interface, and wherein data and data sets can be transmitted between the plant safety system and the factory library system via the interface.

[0005] CN111198530A discloses a method for clouding robots in a 5G environment, which includes multiple intelligent manufacturing cloud centers, wherein the intelligent manufacturing mechanism model of the robot is arranged in the cloud, the intelligent manufacturing mechanism model is sent to the robot, and the robot performs intelligent collaboration according to the intelligent manufacturing mechanism model. Summary of the Invention

[0006] An object of the present invention is to propose a method and system arrangement for optimizing the production planning or workshop logistics of products and articles in the production, trading or distribution of products and articles, wherein it solves the problem of maintaining or keeping up-to-date data about (i) the production planning of products and articles or (ii) the workshop related logistics in the production, trading or distribution of products and articles.

[0007] The objective involves collecting information about: (i) the production planning of products and items, or (ii) the shop floor logistics for producing, trading or distributing products and items, in particular about the equipment used, the location of assets such as tools or robots or automated machines, the utilization of buffer areas, and information about mobile equipment and human workers related to production planning or distribution in the shop floor.

[0008] With regard to the method defined in the preamble of claim 1 , the stated object is solved by the features in the characterizing clause of claim 1 .

[0009] Furthermore, with regard to the system arrangement defined in the preamble of claim 8 , the stated object is solved by the features in the characterizing portion of claim 8 .

[0010] The main idea of ​​the present invention according to claims 1 and 8 is to optimize the production planning or workshop logistics of products and articles in the production, trading or distribution of products and articles, wherein at least one mobile logistics robot used to automate the logistics transportation of products, articles or production materials of products and articles in the workshop is each equipped with sensor technology, the sensor technology including at least one sensor and a processing / control unit, the processing / control unit being suitable for measuring or capturing and controlling the current state or changes of the workshop through sensor data, when each robot is managed by a fleet management system, the fleet management system being used to allocate or schedule logistics tasks between at least one mobile logistics robot performing transportation tasks of a transportation task queue, the transportation task queue being maintained by an automated logistics planning system for planning logistics transportation to which the mobile logistics robots and the fleet management system are assigned. As key aspects of this idea, it is proposed that: (1) The workshop master data of the workshop is stored in the database of the data supervision system. The workshop master data is used to plan the workshop logistics and can be loaded into the automated logistics planning system. (2) extracting shop floor related information from the sensor-based data measurements or captures through a processing / control unit, (3) each of the at least one mobile logistics robot submits workshop-related information to a data fusion facility of a data supervision system for collecting workshop-related information, and (4) The stored shop master data is updated by merging the stored shop master data with the collected shop related information in a corresponding manner via a data fusion facility that can access a database.

[0011] The at least one mobile logistics robot is preferably an "automatic guided vehicle" <agv>" or an automated forklift (see claims 6 and 13). But of course it is generally possible that the mobile logistics robot can be any other mobile automated machine.

[0012] Furthermore, the sensor is advantageously a 2D and / or 3D camera, a 2D and / or 3D "light detection and ranging" <lidar>Sensors", Time of Flight Sensors, Stereo Cameras, RFID <rfid>" detector, a microphone or any other volume sensor for capturing noise from persons, engines and / or machine tools (see claims 7 and 14). But of course it is also generally possible that the sensor can be any other device with different detection technology.

[0013] The proposed solution differs from the current state of the art using dedicated RTLS technology and is described at the outset by: - Leverage existing fleets of mobile logistics robots to generate additional benefits; - No need to install additional sensors on the robot, as most mobile logistics robots in use are already equipped with appropriate sensors for their main function of navigating on the shop floor, - when manual updating of the master data and workshop-related information of a workshop is replaced by an automated solution as proposed according to the present invention, it is possible to save manpower when the stored workshop master data is updated by merging the stored workshop master data with the collected workshop-related information extracted from the measurement or capture based on sensor data, - When the database storing the master data is to be updated (continuously updated), better data quality is achieved, e.g. the database is more accurate and less outdated than if it is updated only when needed.

[0014] According to an advantageous development of the proposed idea (see claims 2 and 9), the workshop-related information extracted from the robot measurements is at least one of: (i) data about the utilization of the space in the workshop, (ii) data about the persons present in different areas of the workshop, and (iii) data about the location of assets in the workshop.

[0015] Regarding (i), the data relates to, for example, buffer zones, material delivery or storage, material input and output of production machines (such as machine tools) and shipping areas, as well as noise from engines and machine tools. Utilization of such areas can be achieved using 3D cameras, 3D "light detection and ranging" <lidar>sensor”, time-of-flight sensor, stereo camera or volumetric sensor (and microphone for capturing noise accordingly).

[0016] Regarding (ii), the areas around production machines, commissioning stations, inbound or outbound logistics areas or packaging stations give data on the presence of people in different areas of the shop floor, while the number of people in these areas can be determined by using 2D cameras, 3D sensors, 2D / 3D “light detection and ranging” <lidar>sensor” or microphone to estimate.

[0017] With respect to (iii), the location of the asset can be identified using a 2D camera using an object recognition algorithm or a machine-readable code (such as a QR code), a 3D camera using an object recognition algorithm, or an RFID detector for assets tagged with an RFID tag, or a microphone for capturing noise from engines and machine tools.

[0018] According to a further advantageous development of the proposed idea (see claims 3 and 10), the workshop-related information is preferably submitted to the data fusion facility continuously or ("option I") via a wireless connection from at least one mobile logistics robot, each comprising a wireless interface, to the data fusion facility or ("option II") via a wireless connection from at least one mobile logistics robot, each comprising a wireless interface, to the fleet management system, wherein the fleet management system is connected to the data fusion facility for forwarding the workshop-related information.

[0019] In addition, according to claims 1 and 8, a specific advantage is that when the collected workshop-related information submitted from at least two mobile logistics robots actually concerns the same state of the workshop, but the workshop-related information collected by at least two mobile logistics robots is different (because at least two mobile logistics robots are at different positions in the workshop), the data fusion facility derives the most likely state of the workshop by using a probabilistic state estimation technology such as a "Bayesian filter".

[0020] Furthermore, advantageously (see claims 4 and 11 supplemented by claims 5 and 12), the workshop master data is monitored by a data task scheduler assigned and able to access the database to identify specific stored workshop master data in the stored workshop master data that needs to be updated (for example, if the identified specific data has not been updated within a given time period), and a data task queue is maintained by the data task scheduler, wherein the fleet management system uses the data task queue in such a way that either the transport tasks performed by the transport task queue are prioritized with respect to whether the transport tasks performed also complete the data tasks of the data task queue, or if at least one mobile logistics robot is not occupied by a logistics task assigned or scheduled by the fleet management system, then instead the mobile logistics robot is only scheduled for the data tasks of the data task queue. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In addition to the above, advantageous further developments of the invention result from the following description of a preferred embodiment of the invention according to a single figure, which shows a system arrangement for optimizing production planning or workshop logistics of products and articles in the production, trading or distribution of products and articles. DETAILED DESCRIPTION

[0022] The figure depicts a system arrangement SAM for optimizing production planning or shop floor logistics for the production, trade, or distribution of products and articles. The system arrangement SAM includes, in addition to an automated logistics planning system ALPS, a fleet management system FMS and at least one mobile logistics robot MLR1, MLR2, and MLR3. Both the fleet management system FMS and the at least one mobile logistics robot MLR1, MLR2, and MLR3 are assigned to the automated logistics planning system ALPS. According to the depicted system arrangement SAM, for example, three mobile logistics robots—a first mobile logistics robot MLR1, a second mobile logistics robot MLR2, and a third mobile logistics robot MLR3—are available for operation in the shop floor SHF of the system arrangement SAM for logistics transport.

[0023] Mobile logistics robots MLR1, MLR2, and MLR3 are preferably "Automated Guided Vehicles" <agv>” or autonomous forklifts.

[0024] In order to automate the logistical transportation of production materials of products, articles or products and articles within the workshop SHF, (1) the three mobile logistics robots MLR1, MLR2, MLR3 are each equipped with sensor technology SST, and (2) the three mobile logistics robots MLR1, MLR2, MLR3 are managed by a fleet management system FMS, which is used to allocate or schedule logistics tasks LT among the three mobile logistics robots MLR1, MLR2, MLR3 for executing transport tasks TT of an exe transport task queue TTQ, which is maintained by an automated logistics planning system ALPS to plan logistics transportation.

[0025] Each mobile logistics robot MLR1, MLR2, and MLR3 is equipped with the sensor technology SST, which includes at least one sensor SS and a processing / control unit PCU. Each sensor SS of each mobile logistics robot MLR1, MLR2, and MLR3 is adapted to measure or capture the current state or changes in the shop floor SHF by generating sensor data SSD1, SSD2, and SSD3. Thus, the first mobile logistics robot MLR1 generates first sensor data SSD1, the second mobile logistics robot MLR2 generates second sensor data SSD2, and the third mobile logistics robot MLR3 generates third sensor data SSD3.

[0026] The sensors SS used in the sensor technology SST can advantageously be 2D and / or 3D cameras, 2D and / or 3D "light detection and ranging" sensors. <lidar>Sensors", Time of Flight Sensors, Stereo Cameras, RFID <rfid>” detectors, microphones for capturing noise from engines and machine tools, or any other volume sensors.

[0027] After those for processing sensor data and controlling the current state or change of the shop floor SHF, each processing / control unit PCU of the mobile logistics robots MLR1, MLR2, MLR3 extracts ext shop floor related information INF1 from the measurement or capture based on the sensor data SHF 、INF2 SHF 、INF3 SHF Therefore, the first mobile logistics robot MLR1 extracts the first workshop related information INF1 from the first sensor data SSD1 SHF The second mobile logistics robot MLR2 extracts the second workshop related information INF2 from the second sensor data SSD2 SHF , and the third mobile logistics robot MLR3 extracts the third workshop related information INF3 from the third sensor data SSD3 SHF .

[0028] According to Option A, the system arrangement SAM further includes a data administration system DAS, or alternatively, according to Option B, the data administration system DAS is assigned to the system arrangement SAM. In both cases, the data administration system DAS includes a database DB that stores the shop floor master data SHFMD of the shop floor SHF. This shop floor master data SHFMD is used to plan the production of products and items or shop floor logistics and can therefore be loaded into the automated logistics planning system ALPS.

[0029] The data supervision system DAS further includes a data fusion facility DFF, which is preferably assigned to and can access the database DB, and to which the mobile logistics robots MLR1, MLR2, MLR3 submit the sbm workshop related information INF1 SHF 、INF2 SHF 、INF3 SHF , used to collect workshop related information INF1 SHF 、INF2 SHF 、INF3 SHF .

[0030] Extracting shop floor related information from robot measurements INF1 SHF 、INF2 SHF 、INF3 SHF Preferably at least one of: data on the utilization of the space of the workshop SHF, data on the persons present in different areas of the workshop SHF and data on the location of assets of the workshop SHF.

[0031] To submit workshop related information INF1 SHF 、INF2 SHF 、INF3 SHF The mobile logistics robots MLR1, MLR2, and MLR3 each include a wireless interface WIF. Through this wireless interface WIF, the workshop related information INF1 SHF 、INF2 SHF 、INF3 SHF The sbm is submitted to a data fusion facility DFF which also includes a wireless interface WIF for this purpose. The submission is preferably done continuously.

[0032] In addition, workshop related information INF1 SHF 、INF2 SHF 、INF3 SHF The submission can be made according to "Option I" via a wireless connection from the mobile logistics robots MLR1, MLR2, MLR3 to the data fusion facility DFF, or according to "Option II" via a wireless connection from the mobile logistics robots MLR1, MLR2, MLR3 to the fleet management system FMS, which is connected to the data fusion facility DFF for forwarding fwd workshop related information INF1 SHF 、INF2 SHF 、INF3 SHF .

[0033] Mobile logistics robots MLR1, MLR2, and MLR3 submit SBM workshop related information INF1 to it SHF 、INF2 SHF 、INF3 SHF The data fusion facility DFF can access the database DB and integrate the stored workshop master data SHFMD with the collected workshop related information INF1 in a corresponding manner. SHF 、INF2 SHF 、INF32 SHF Merge mrg to update the workshop master data SHFMD stored in upd. Merging in a corresponding manner means collecting workshop related information INF1 SHF 、INF2 SHF 、INF3 SHF It is not merged arbitrarily, but merged purposefully, where the collected workshop related information INF1 SHF 、INF2 SHF 、INF3 SHF Data corresponding to the stored shop floor master data SHFMD.

[0034] Furthermore, the data fusion facility DFF is advantageously designed so that the collected workshop-related information INF1 submitted from two mobile logistics robots (e.g., the first mobile logistics robot MLR1 and the second mobile logistics robot MLR2 or the second mobile logistics robot MLR2 and the third mobile logistics robot MLR3 or the first mobile logistics robot MLR1 and the third mobile logistics robot MLR3) is SHF 、INF2 SHF 、INF3 SHF When the same state of the workshop SHF is actually involved, but the workshop-related information collected from the two mobile logistics robots is different (because the two mobile logistics robots are at different positions in the workshop SHF), the facility derives the most likely state of the workshop SHF by using probabilistic state estimation techniques such as "Bayesian filter".

[0035] Finally, the system arrangement SAM comprises a data task scheduler DTSD, which is preferably assigned to and has access to the database DB of the data administration system DAS. By having this access, the data task scheduler DTSD (i) monitors the shop floor master data SHFD in the mto database DB to identify specific stored shop floor master data SHFMD' that needs to be updated among the stored shop floor master data SHFMD (for example, if the identified specific data SHFMD' has not been updated within a given time period), and (ii) maintains the mtg data task queue DTQ.

[0036] The fleet management system FMS can advantageously use the data task queue DTQ usx in the following manner, namely, or The transport tasks TT executed by the transport task queue TTQ are prioritized based on whether the transport tasks TT executed also complete the data tasks DT of the cpl data task queue DTQ. or If at least one of the mobile logistics robots MLR1, MLR2, MLR3 is not occupied by a logistics task LT assigned or scheduled by the fleet management system FMS, then instead the at least one mobile logistics robot MLR1, MLR2, MLR3 is only scheduled for a data task DT of the data task queue DTQ.< / rfid> < / lidar> < / agv> < / lidar> < / lidar> < / rfid> < / lidar> < / agv> < / rtls>

Claims

1. A method for optimizing production planning or shop floor logistics for products and articles in the production, trade or distribution of products and articles, wherein: At least one mobile logistics robot (MLR1, MLR2, MLR3) for automating the logistics transportation of the product, the article or the production materials of the product and the article in the workshop (SHF) is each equipped with a sensor technology (SST), the sensor technology (SST) comprising at least one sensor (SS) and a processing / control unit (PCU), the processing / control unit (PCU) being suitable for measuring or capturing and controlling the current state or change of the workshop (SHF) through sensor data (SSD1, SSD2, SSD3). When each machine When the robot is managed by a fleet management system (FMS), the fleet management system (FMS) is used to allocate or schedule logistics tasks (LT) among the at least one mobile logistics robot (MLR1, MLR2, MLR3) performing (exe) a transport task (TT) of a transport task queue (TTQ), and the transport task queue (TTQ) is maintained (mta) by an automated logistics planning system (ALPS) for planning logistics transportation to which the mobile logistics robots (MLR1, MLR2, MLR3) and the fleet management system (FMS) are assigned. Its characteristics are: a) storing (sto) the shop floor master data (SHFMD) of the shop floor (SHF) in a database (DB) of a data supervision system (DAS), wherein the shop floor master data (SHFMD) is used to plan the production of the products and articles or the shop floor logistics and can be loaded into the automated logistics planning system (ALPS), b) extracting (ext) workshop-related information (INF1) from the measurement or capture based on sensor data by the processing / control unit (PCU) SHF 、INF2 SHF 、INF3 SHF ), c) Each of the at least one mobile logistics robot (MLR1, MLR2, MLR3) submits (sbm) the workshop related information (INF1) to the data fusion facility (DFF) of the data supervision system (DAS) SHF 、INF2 SHF 、INF3 SHF ) to collect the workshop related information (INF1 SHF 、INF2 SHF 、INF3 SHF ), d) By correspondingly linking the stored workshop master data (SHFMD) with the collected workshop related information (INF1 SHF 、INF2 SHF 、INF3 SHF ) merge (mrg), update (upd) the stored shop floor master data (SHFMD) via a data fusion facility (DFF) having access to said database (DB), Among them, the collected workshop related information (INF1 SHF 、INF2 SHF 、INF3 SHF ) actually involves the same state of the workshop (SHF), but the workshop-related information collected from the at least two mobile logistics robots is different, the data fusion facility (DFF) derives the most likely state of the workshop by using a probabilistic state estimation technology such as "Bayesian filter".

2. The method according to claim 1, characterized in that The shop floor related information (INF1) extracted (ext) from the robot measurement SHF 、INF2 SHF 、INF3 SHF ) is at least one of data about the utilization of the space of the workshop (SHF), data about the personnel present in different areas of the workshop (SHF), and data about the location of assets of the workshop (SHF).

3. The method according to claim 1 or 2, characterized in that In particular, the workshop related information (INF1) is transmitted to the data fusion facility (DFF) continuously or ("Option I") via a wireless connection from the at least one mobile logistics robot (MLR1, MLR2, MLR3) each comprising a wireless interface (WIF) to the data fusion facility (DFF), or ("Option II") via a wireless connection from the at least one mobile logistics robot (MLR1, MLR2, MLR3) each comprising a wireless interface (WIF) to the fleet management system (FMS). SHF 、INF2 SHF 、INF3 SHF ) submits (sbm) to the data fusion facility (DFF), wherein, in order to forward (fwd) the workshop related information (INF1 SHF 、INF2 SHF 、INF3 SHF ), the fleet management system (FMS) is connected to the data fusion facility (DFF).

4. The method according to claim 1, wherein - monitoring (mto) said shop floor master data (SHFD) by a data task scheduler (DTSD) assigned and having access to said database (DB) to identify specific stored shop floor master data (SHFMD') of the stored shop floor master data (SHFMD) that needs to be updated, in particular if the identified specific data (SHFMD') has not been updated within a given time period; as well as -Maintaining (mtg) a data task queue (DTQ) by the data task scheduler (DTSD).

5. The method according to claim 4, characterized in that The data task queue (DTQ) is used (usx) by the fleet management system (FMS) so that - the executed (exe) transport tasks (TT) of the transport task queue (TTQ) are prioritized with respect to whether the executed transport tasks (TT) also complete (cpl) the data tasks (DT) of the data task queue (DTQ), or -If at least one of the mobile logistics robots (MLR1, MLR2, MLR3) is not occupied by a logistics task (LT) assigned or scheduled by the fleet management system (FMS), then instead the mobile logistics robot (MLR) is only scheduled with a data task (DT) of the data task queue (DTQ).

6. The method according to claim 1, wherein The at least one mobile logistics robot (MLR1, MLR2, MLR3) is an "Automated Guided Vehicle" <agv> ” or autonomous forklifts.< / agv> 7. The method according to claim 1, wherein The sensor (SS) is a 2D and / or 3D camera, a 2D and / or 3D "light detection and ranging" <lidar>Sensors", Time of Flight Sensors, Stereo Cameras, RFID <rfid> ” detectors, microphones or any other volumetric sensors for capturing noise from people, engines and / or machine tools.< / rfid> < / lidar> 8. A system arrangement (SAM) for optimizing production planning or shop floor logistics for products and articles in the production, trade or distribution of products and articles, comprising an automated logistics planning system (ALPS) as well as a fleet management system (FMS) and at least one mobile logistics robot (MLR1, MLR2, MLR3), both of which are assigned to the automated logistics planning system (ALPS), wherein: In order to automate the logistical transport of the products, the articles or the production materials of the products and articles within a shop floor (SHF), the at least one mobile logistics robot (MLR1, MLR2, MLR3) is equipped with sensor technology (SST), the sensor technology (SST) comprising at least one sensor (SS) and a processing / control unit (PCU), the processing / control unit (PCU) being adapted to measure or capture and control the current state or changes of the shop floor (SHF) by means of sensor data (SSD1, SSD2, SSD3), when each robot is managed by a fleet management system (FMS), the fleet management system (FMS) being used to allocate or schedule logistics tasks (LT) among the at least one mobile logistics robot (MLR1, MLR2, MLR3) executing (exe) transport tasks (TT) of a transport task queue (TTQ), the transport task queue (TTQ) being maintained (mta) by the automated logistics planning system (ALPS) for planning the logistics, Its characteristics are: a) a database (DB) of a data administration system (DAS), which ("option A") is included in the system arrangement (SAM) or ("option B") is assigned to the system arrangement (SAM), the database (DB) of the data administration system (DAS) storing (sto) the shop floor master data (SHFMD) of the shop floor (SHF), the shop floor master data (SHFMD) being used to plan the production of the products and articles or the shop floor logistics and being loadable into the automated logistics planning system (ALPS), b) The processing / control unit (PCU) of each of the at least one mobile logistics robot (MLR1, MLR2, MLR3) extracts (ext) workshop-related information (INF1) from the measurement or capture based on sensor data SHF 、INF2 SHF 、INF3 SHF ), c) a data fusion facility (DFF) of the data supervision system (DAS), to which each of the at least one mobile logistics robot (MLR1, MLR2, MLR3) submits (sbm) the workshop related information (INF1) SHF 、INF2 SHF 、INF3 SHF ) for collecting the workshop related information (INF1 SHF 、INF2 SHF 、INF3 SHF ), d) By correspondingly linking the stored workshop master data (SHFMD) with the collected workshop related information (INF1 SHF 、INF2 SHF 、INF3 SHF ) merge (mrg), able to access the data fusion facility (DFF) of the database (DB) to update (upd) the stored shop floor master data (SHFMD), Among them, the collected workshop related information (INF1 SHF 、INF2 SHF 、INF3 SHF ) actually involves the same state of the workshop (SHF), but the workshop-related information collected from the at least two mobile logistics robots is different, the data fusion facility (DFF) derives the most likely state of the workshop by using a probabilistic state estimation technology such as "Bayesian filter".

9. System arrangement (SAM) according to claim 8, characterized in that The shop floor related information (INF1) extracted (ext) from the robot measurement SHF 、INF2 SHF 、INF3 SHF ) is at least one of data about the utilization of the space of the workshop (SHF), data about the personnel present in different areas of the workshop (SHF), and data about the location of assets of the workshop (SHF).

10. System arrangement (SAM) according to claim 8 or 9, characterized in that a wireless interface (WIF) between the mobile logistics robots (MLR1, MLR2, MLR3), the fleet management system (FMS) and the data fusion facility (DFF), wherein, in particular, the workshop-related information (INF1) is transmitted continuously or ("option I") via a wireless connection from the at least one mobile logistics robot (MLR1, MLR2, MLR3) to the data fusion facility (DFF), or ("option II") via a wireless connection from the mobile logistics robot (MLR1, MLR2, MLR3) to the fleet management system (FMS) SHF 、INF2 SHF 、INF3 SHF ) submits (sbm) to the data fusion facility (DFF), and the fleet management system (FMS) is connected to the data fusion facility (DFF) to forward (fwd) the workshop related information (INF1 SHF 、INF2 SHF 、INF3 SHF ).

11. System arrangement (SAM) according to one of claims 8 to 10, characterized in that A data task scheduler (DTSD) assigned and able to access the database (DB) - monitoring (mto) said shop floor master data (SHFD) to identify specific stored shop floor master data (SHFMD') of the stored shop floor master data (SHFMD) that requires updating, in particular if the identified specific data (SHFMD') has not been updated within a given period of time; and -Maintain (mtg) data task queue (DTQ).

12. System arrangement (SAM) according to claim 11, characterized in that The Fleet Management System (FMS) uses (usx) the Data Task Queue (DTQ) so that - the executed (exe) transport tasks (TT) of the transport task queue (TTQ) are prioritized with respect to whether the executed transport tasks (TT) also complete (cpl) the data tasks (DT) of the data task queue (DTQ), or -If the at least one mobile logistics robot (MLR1, MLR2, MLR3) is not occupied by a logistics task (LT) assigned or scheduled by the fleet management system (FMS), then instead the at least one mobile logistics robot (MLR) is only scheduled with a data task (DT) of the data task queue (DTQ).

13. System arrangement (SAM) according to one of claims 8 to 12, characterized in that The at least one mobile logistics robot (MLR1, MLR2, MLR3) is an "Automated Guided Vehicle" <agv> ” or autonomous forklifts.< / agv> 14. System arrangement (SAM) according to one of claims 8 to 13, characterized in that The sensor (SS) is a 2D and / or 3D camera, a 2D and / or 3D "light detection and ranging" <lidar>Sensors", Time of Flight Sensors, Stereo Cameras, RFID <rfid> ” detectors, microphones or any other volumetric sensors for capturing noise from people, engines and / or machine tools.< / rfid> < / lidar>

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