Patent Publication Number: US-11034027-B2

Title: Robot assisted personnel routing

Description:
FIELD OF THE INVENTION 
     This invention relates to personnel routing and more particularly to robot assisted personnel routing. 
     BACKGROUND OF THE INVENTION 
     Ordering products over the internet for home delivery is an extremely popular way of shopping. Fulfilling such orders in a timely, accurate and efficient manner is logistically challenging to say the least. Clicking the “check out” button in a virtual shopping cart creates an “order.” The order includes a listing of items that are to be shipped to a particular address. The process of “fulfillment” involves physically taking or “picking” these items from a large warehouse, packing them, and shipping them to the designated address. An important goal of the order-fulfillment process is thus to ship as many items in as short a time as possible. 
     The order-fulfillment process typically takes place in a large warehouse that contains many products, including those listed in the order. Among the tasks of order fulfillment is therefore that of traversing the warehouse to find and collect the various items listed in an order. In addition, the products that will ultimately be shipped first need to be received in the warehouse and stored or “placed” in storage bins in an orderly fashion throughout the warehouse so they can be readily retrieved for shipping. 
     In a large warehouse, the goods that are being delivered and ordered can be stored in the warehouse very far apart from each other and dispersed among a great number of other goods. With an order-fulfillment process using only human operators to place and pick the goods requires the operators to do a great deal of walking and can be inefficient and time consuming. Since the efficiency of the fulfillment process is a function of the number of items shipped per unit time, increasing time reduces efficiency. 
     In order to increase efficiency, robots may be used to perform functions of humans or they may be used to supplement the humans&#39; activities. For example, robots may be assigned to “place” a number of items in various locations dispersed throughout the warehouse or to “pick” items from various locations for packing and shipping. The picking and placing may be done by the robot alone or with the assistance of human operators. For example, in the case of a pick operation, the human operator would pick items from shelves and place them on the robots or, in the case of a place operation, the human operator would pick items from the robot and place them on the shelves. 
     To the extent that human operators are deployed to assist robots within a shared navigational space, the human operators, absent direction, can be underutilized, thereby reducing human operator efficiency, increasing robot dwell time, and causing confusion and/or congestion within the shared navigational space. For example, a human operator may complete a task in current aisle and be ready to assist the next robot only to find that there are no robots needing assistance in sight. Such an operator could simply wait for a robot to approach or may guess and head in a particular direction hoping to locate a robot in need of assistance. However, because the operator is merely guessing or waiting, this approach is unlikely to consistently create an efficient result. Furthermore, without guidance or direction, multiple human operators may initially pursue the same robot. The operators then waste the time necessary to travel to the target robot and, once the operators realize they are pursuing the same robot, need to waste time reconciling with each other which operator will assist the target robot and, for the other human operator(s), go through the process of finding and traveling to another robot to assist. 
     BRIEF SUMMARY OF THE INVENTION 
     Provided herein are systems and methods for robot assisted personnel routing. 
     In one aspect, a robot assisted personnel routing system is provided. The system includes a plurality of autonomous robots operating within a navigational space. Each robot includes a processor. Each robot also includes a memory. The memory stores instructions that, when executed by the processor, cause the autonomous robot to detect completion of a task operation by a human operator. The memory also stores instructions that, when executed by the processor, cause the autonomous robot to receive status information corresponding to at least one other robot, the status information including at least one of a location or a wait time associated with the other robot. The memory also stores instructions that, when executed by the processor, cause the autonomous robot to determine, from the status information, at least one next task recommendation for directing the human operator to a next robot for a next task operation. The memory also stores instructions that, when executed by the processor, cause the autonomous robot to render, on a display of the robot, the at least one next task recommendation for viewing by the human operator, the next task recommendation including a location of the next robot corresponding to the next task. 
     In some embodiments, the status information includes an (x,y,z) position of the at least one other robot within the navigational space. In some embodiments, the next robot is selected by determining a minimum straight line distance to the at least one other robot. In some embodiments, the next robot is selected in response to one or more efficiency factors, including dwell time of the at least one other robot, straight-line proximity of the at least one other robot, number of human operators proximate the at least one other robot, walking distance to the at least one other robot, priority of a task associated with the at least one other robot, congestion proximate the at least one other robot, or combinations thereof. In some embodiments, the next task recommendation is rendered as an interactive graphic on the display. In some embodiments, responsive to human operator input received by the interactive graphic, an expanded graphic is rendered to one or more of provide additional information about the next task operation associated with the next task recommendation, present additional next task recommendations to the human operator, or combinations thereof. In some embodiments, the expanded graphic includes one or more additional interactive graphics. 
     In some embodiments, the interactive graphic is configured to record a selection by the human operator of a next task operation from the at least one next task recommendation. In some embodiments, the memory also stores instructions that, when executed by the processor, cause the autonomous robot to, responsive to recordation of the next task selection, designate the selected task operation as in process within the personnel routing system to avoid redundant recommendation. In some embodiments, the in process designation is removed if the selected task operation is not completed within a prescribed time limit. In some embodiments, the status information is directly received from the at least one other robot. In some embodiments, the status information is received from a robot monitoring server for monitoring robots within the navigational space, wherein the robot monitoring server is at least one of integrated with at least one of an order-server of the navigational space, integrated with a warehouse management system of the navigational space, a standalone server, a distributed system comprising the processor and the memory of at least two of the plurality of robots, or combinations thereof. In some embodiments, the navigational space is a warehouse. In some embodiments, the at least one next task operation is at least one of a pick operation, a put operation, or combinations thereof to be executed within the warehouse. 
     In another aspect, a method for robot assisted personnel routing is provided. The method includes detecting, by a processor and a memory of one of a plurality of autonomous robots operating within a navigational space, completion of a task operation by a human operator. The method also includes receiving, by a transceiver of the autonomous robot, status information corresponding to at least one other robot, the status information including at least one of a location or a wait time associated with the other robot. The method also includes determining, from the status information, at least one next task recommendation for directing the human operator to a next robot for a next task operation. The method also includes rendering, on a display of the robot, the at least one next task recommendation for viewing by the human operator, the next task recommendation including a location of the next robot corresponding to the next task. 
     In some embodiments, the status information includes an (x,y,z) position of the at least one other robot within the navigational space. In some embodiments, the method also includes selecting the next robot by determining a minimum straight line distance to the at least one other robot. In some embodiments, the method also includes selecting the next robot responsive to one or more efficiency factors, including dwell time of the at least one other robot, straight-line proximity of the at least one other robot, number of human operators proximate the at least one other robot, walking distance to the at least one other robot, priority of a task associated with the at least one other robot, congestion proximate the at least one other robot, or combinations thereof. In some embodiments, the method also includes rendering the next task recommendation as an interactive graphic on the display. In some embodiments, the method also includes rendering an expanded graphic responsive to human operator input received by the interactive graphic to one or more of provide additional information about the next task operation associated with the next task recommendation, present additional next task recommendations to the human operator, or combinations thereof. In some embodiments, the expanded graphic includes one or more additional interactive graphics. In some embodiments, the method also includes recording, by the interactive graphic, a selection by the human operator of a next task operation from the at least one next task recommendation. In some embodiments, the method also includes designating, responsive to recordation of the next task selection, the selected task operation as in process within the personnel routing system to avoid redundant recommendation. 
     These and other features of the invention will be apparent from the following detailed description and the accompanying figures, in which: 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  is a top plan view of an order-fulfillment warehouse; 
         FIG. 2A  is a front elevational view of a base of one of the robots used in the warehouse shown in  FIG. 1 ; 
         FIG. 2B  is a perspective view of a base of one of the robots used in the warehouse shown in  FIG. 1 ; 
         FIG. 3  is a perspective view of the robot in  FIGS. 2A and 2B  outfitted with an armature and parked in front of a shelf shown in  FIG. 1 ; 
         FIG. 4  is a partial map of the warehouse of  FIG. 1  created using laser radar on the robot; 
         FIG. 5  is a flow chart depicting the process for locating fiducial markers dispersed throughout the warehouse and storing fiducial marker poses; 
         FIG. 6  is a table of the fiducial identification to pose mapping; 
         FIG. 7  is a table of the bin location to fiducial identification mapping; 
         FIG. 8  is a flow chart depicting product SKU to pose mapping process; 
         FIG. 9  map of robot and human activity within a warehouse; 
         FIG. 10  is a diagram illustrating an example next pick recommendation rendered on the tablet of the robot shown in  FIG. 3 ; 
         FIG. 11  is a diagram illustrating another example next pick recommendation rendered on the tablet of the robot shown in  FIG. 3 ; 
         FIG. 12  is a block diagram of an exemplary computing system; and 
         FIG. 13  is a network diagram of an exemplary distributed network. 
     
    
    
     DETAILED DESCRIPTION OF INVENTION 
     The disclosure and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments and examples that are described and/or illustrated in the accompanying drawings and detailed in the following description. It should be noted that the features illustrated in the drawings are not necessarily drawn to scale, and features of one embodiment may be employed with other embodiments as the skilled artisan would recognize, even if not explicitly stated herein. Descriptions of well-known components and processing techniques may be omitted so as to not unnecessarily obscure the embodiments of the disclosure. The examples used herein are intended merely to facilitate an understanding of ways in which the disclosure may be practiced and to further enable those of skill in the art to practice the embodiments of the disclosure. Accordingly, the examples and embodiments herein should not be construed as limiting the scope of the disclosure. Moreover, it is noted that like reference numerals represent similar parts throughout the several views of the drawings. 
     The invention is directed to robot congestion management. Although not restricted to any particular robot application, one suitable application that the invention may be used in is order fulfillment. The use of robots in this application will be described to provide context for robot congestion management but is not limited to that application. 
     Referring to  FIG. 1 , a typical order-fulfillment warehouse  10  includes shelves  12  filled with the various items that could be included in an order. In operation, an incoming stream of orders  16  from warehouse management server  15  arrive at an order-server  14 . The order-server  14  may prioritize and group orders, among other things, for assignment to robots  18  during an induction process. As the robots are inducted by operators, at a processing station (e.g. station  100 ), the orders  16  are assigned and communicated to robots  18  wirelessly for execution. It will be understood by those skilled in the art that order server  14  may be a separate server with a discrete software system configured to interoperate with the warehouse management system server  15  and warehouse management software or the order server functionality may be integrated into the warehouse management software and run on the warehouse management server  15 . 
     In a preferred embodiment, a robot  18 , shown in  FIGS. 2A and 2B , includes an autonomous wheeled base  20  having a laser-radar  22 . The base  20  also features a transceiver (not shown) that enables the robot  18  to receive instructions from and transmit data to the order-server  14  and/or other robots, and a pair of digital optical cameras  24   a  and  24   b . The robot base also includes an electrical charging port  26  for re-charging the batteries which power autonomous wheeled base  20 . The base  20  further features a processor (not shown) that receives data from the laser-radar and cameras  24   a  and  24   b  to capture information representative of the robot&#39;s environment. There is a memory (not shown) that operates with the processor to carry out various tasks associated with navigation within the warehouse  10 , as well as to navigate to fiducial marker  30  placed on shelves  12 , as shown in  FIG. 3 . Fiducial marker  30  (e.g. a two-dimensional bar code) corresponds to bin/location of an item ordered. The navigation approach of this invention is described in detail below with respect to  FIGS. 4-8 . Fiducial markers are also used to identify charging stations according to an aspect of this invention and the navigation to such charging station fiducial markers is the same as the navigation to the bin/location of items ordered. Once the robots navigate to a charging station, a more precise navigation approach is used to dock the robot with the charging station and such a navigation approach is described below. 
     Referring again to  FIG. 2B , base  20  includes an upper surface  32  where a tote or bin could be stored to carry items. There is also shown a coupling  34  that engages any one of a plurality of interchangeable armatures  40 , one of which is shown in  FIG. 3 . The particular armature  40  in  FIG. 3  features a tote-holder  42  (in this case a shelf) for carrying a tote  44  that receives items, and a tablet holder  46  (or laptop/other user input device) for supporting a tablet  48 . In some embodiments, the armature  40  supports one or more totes for carrying items. In other embodiments, the base  20  supports one or more totes for carrying received items. As used herein, the term “tote” includes, without limitation, cargo holders, bins, cages, shelves, rods from which items can be hung, caddies, crates, racks, stands, trestle, containers, boxes, canisters, vessels, and repositories. 
     Although a robot  18  excels at moving around the warehouse  10 , with current robot technology, it is not very good at quickly and efficiently picking items from a shelf and placing them in the tote  44  due to the technical difficulties associated with robotic manipulation of objects. A more efficient way of picking items is to use a local operator  50 , which is typically human, to carry out the task of physically removing an ordered item from a shelf  12  and placing it on robot  18 , for example, in tote  44 . The robot  18  communicates the order to the local operator  50  via the tablet  48  (or laptop/other user input device), which the local operator  50  can read, or by transmitting the order to a handheld device used by the local operator  50 . 
     Upon receiving an order  16  from the order server  14 , the robot  18  proceeds to a first warehouse location, e.g. as shown in  FIG. 3 . It does so based on navigation software stored in the memory and carried out by the processor. The navigation software relies on data concerning the environment, as collected by the laser-radar  22 , an internal table in memory that identifies the fiducial identification (“ID”) of fiducial marker  30  that corresponds to a location in the warehouse  10  where a particular item can be found, and the cameras  24   a  and  24   b  to navigate. 
     Upon reaching the correct location (pose), the robot  18  parks itself in front of a shelf  12  on which the item is stored and waits for a local operator  50  to retrieve the item from the shelf  12  and place it in tote  44 . If robot  18  has other items to retrieve it proceeds to those locations. The item(s) retrieved by robot  18  are then delivered to a processing station  100 ,  FIG. 1 , where they are packed and shipped. While processing station  100  has been described with regard to this figure as being capable of inducting and unloading/packing robots, it may be configured such that robots are either inducted or unloaded/packed at a station, i.e. they may be restricted to performing a single function. 
     It will be understood by those skilled in the art that each robot may be fulfilling one or more orders and each order may consist of one or more items. Typically, some form of route optimization software would be included to increase efficiency, but this is beyond the scope of this invention and is therefore not described herein. 
     In order to simplify the description of the invention, a single robot  18  and operator  50  are described. However, as is evident from  FIG. 1 , a typical fulfillment operation includes many robots and operators working among each other in the warehouse to fill a continuous stream of orders. 
     The baseline navigation approach of this invention, as well as the semantic mapping of a SKU of an item to be retrieved to a fiducial ID/pose associated with a fiducial marker in the warehouse where the item is located, is described in detail below with respect to  FIGS. 4-8 . 
     Using one or more robots  18 , a map of the warehouse  10  must be created and the location of various fiducial markers dispersed throughout the warehouse must be determined. To do this, one or more of the robots  18  as they are navigating the warehouse they are building/updating a map  10   a ,  FIG. 4 , utilizing its laser-radar  22  and simultaneous localization and mapping (SLAM), which is a computational problem of constructing or updating a map of an unknown environment. Popular SLAM approximate solution methods include the particle filter and extended Kalman filter. The SLAM GMapping approach is the preferred approach, but any suitable SLAM approach can be used. 
     Robot  18  utilizes its laser-radar  22  to create map  10   a  of warehouse  10  as robot  18  travels throughout the space identifying, open space  112 , walls  114 , objects  116 , and other static obstacles, such as shelf  12 , in the space, based on the reflections it receives as the laser-radar scans the environment. 
     While constructing the map  10   a  (or updating it thereafter), one or more robots  18  navigates through warehouse  10  using camera  26  to scan the environment to locate fiducial markers (two-dimensional bar codes) dispersed throughout the warehouse on shelves proximate bins, such as  32  and  34 ,  FIG. 3 , in which items are stored. Robots  18  use a known starting point or origin for reference, such as origin  110 . When a fiducial marker, such as fiducial marker  30 ,  FIGS. 3 and 4 , is located by robot  18  using its camera  26 , the location in the warehouse relative to origin  110  is determined. 
     By the use of wheel encoders and heading sensors, vector  120 , and the robot&#39;s position in the warehouse  10  can be determined. Using the captured image of a fiducial marker/two-dimensional barcode and its known size, robot  18  can determine the orientation with respect to and distance from the robot of the fiducial marker/two-dimensional barcode, vector  130 . With vectors  120  and  130  known, vector  140 , between origin  110  and fiducial marker  30 , can be determined. From vector  140  and the determined orientation of the fiducial marker/two-dimensional barcode relative to robot  18 , the pose (position and orientation) defined by a quaternion (x, y, z, ω) for fiducial marker  30  can be determined. 
     Flow chart  200 ,  FIG. 5 , describing the fiducial marker location process is described. This is performed in an initial mapping mode and as robot  18  encounters new fiducial markers in the warehouse while performing picking, placing and/or other tasks. In step  202 , robot  18  using camera  26  captures an image and in step  204  searches for fiducial markers within the captured images. In step  206 , if a fiducial marker is found in the image (step  204 ) it is determined if the fiducial marker is already stored in fiducial table  300 ,  FIG. 6 , which is located in memory  34  of robot  18 . If the fiducial information is stored in memory already, the flow chart returns to step  202  to capture another image. If it is not in memory, the pose is determined according to the process described above and in step  208 , it is added to fiducial to pose lookup table  300 . 
     In look-up table  300 , which may be stored in the memory of each robot, there are included for each fiducial marker a fiducial identification, 1, 2, 3, etc., and a pose for the fiducial marker/bar code associated with each fiducial identification. The pose consists of the x,y,z coordinates in the warehouse along with the orientation or the quaternion (x,y,z, ω). 
     In another look-up Table  400 ,  FIG. 7 , which may also be stored in the memory of each robot, is a listing of bin locations (e.g.  402   a - f ) within warehouse  10 , which are correlated to particular fiducial ID&#39;s  404 , e.g. number “11”. The bin locations, in this example, consist of seven alpha-numeric characters. The first six characters (e.g. L01001) pertain to the shelf location within the warehouse and the last character (e.g. A-F) identifies the particular bin at the shelf location. In this example, there are six different bin locations associated with fiducial ID “11”. There may be one or more bins associated with each fiducial ID/marker. 
     The alpha-numeric bin locations are understandable to humans, e.g. operator  50 ,  FIG. 3 , as corresponding to a physical location in the warehouse  10  where items are stored. However, they do not have meaning to robot  18 . By mapping the locations to fiducial s, Robot  18  can determine the pose of the fiducial ID using the information in table  300 ,  FIG. 6 , and then navigate to the pose, as described herein. 
     The order fulfillment process according to this invention is depicted in flow chart  500 ,  FIG. 8 . In step  502 , from warehouse management system  15 , order server  14  obtains an order, which may consist of one or more items to be retrieved. It should be noted that the order assignment process is fairly complex and goes beyond the scope of this disclosure. One such order assignment process is described in commonly owned U.S. patent application Ser. No. 15/807,672, entitled Order Grouping in Warehouse Order Fulfillment Operations, filed on Sep. 1, 2016, which is incorporated herein by reference in its entirety. It should also be noted that robots may have tote arrays which allow a single robot to execute multiple orders, one per bin or compartment. Examples of such tote arrays are described in U.S. patent application Ser. No. 15/254,321, entitled Item Storage Array for Mobile Base in Robot Assisted Order-Fulfillment Operations, filed on Sep. 1, 2016, which is incorporated herein by reference in its entirety. 
     Continuing to refer to  FIG. 8 , in step  504  the SKU number(s) of the items is/are determined by the warehouse management system  15 , and from the SKU number(s), the bin location(s) is/are determined in step  506 . A list of bin locations for the order is then transmitted to robot  18 . In step  508 , robot  18  correlates the bin locations to fiducial ID&#39;s and from the fiducial ID&#39;s, the pose of each fiducial ID is obtained in step  510 . In step  512  the robot  18  navigates to the pose as shown in  FIG. 3 , where an operator can pick the item to be retrieved from the appropriate bin and place it on the robot. 
     Item specific information, such as SKU number and bin location, obtained by the warehouse management system  15 /order server  14 , can be transmitted to tablet  48  on robot  18  so that the operator  50  can be informed of the particular items to be retrieved when the robot arrives at each fiducial marker location. 
     With the SLAM map and the pose of the fiducial ID&#39;s known, robot  18  can readily navigate to any one of the fiducial ID&#39;s using various robot navigation techniques. The preferred approach involves setting an initial route to the fiducial marker pose given the knowledge of the open space  112  in the warehouse  10  and the walls  114 , shelves (such as shelf  12 ) and other obstacles  116 . As the robot begins to traverse the warehouse using its laser radar  26 , it determines if there are any obstacles in its path, either fixed or dynamic, such as other robots  18  and/or operators  50 , and iteratively updates its path to the pose of the fiducial marker. The robot re-plans its route about once every 50 milliseconds, constantly searching for the most efficient and effective path while avoiding obstacles. 
     With the product SKU/fiducial ID to fiducial pose mapping technique combined with the SLAM navigation technique both described herein, robots  18  are able to very efficiently and effectively navigate the warehouse space without having to use more complex navigation approaches typically used which involve grid lines and intermediate fiducial markers to determine location within the warehouse. 
     Robot Assisted Personnel Routing 
     In general, without direction, human operators  50  can be underutilized. For example, a human operator may complete a task in current aisle and be ready to assist the next robot only to find that there are no robots needing assistance in sight. Such an operator could simply wait for a robot to approach or may guess and head in a particular direction hoping to locate a robot in need of assistance. However, because the operator is merely guessing or waiting, this approach is unlikely to consistently create an efficient result. For example, without guidance or direction, multiple human operators  50  may initially pursue the same robot  18 . The operators  50  then waste the time necessary to travel to the target robot  18  and, once the operators  50  realize they are pursuing the same robot  18 , the operators  50  need to waste time reconciling amongst themselves which operator will assist the target robot and, for the other operator(s)  50 , the process of finding and traveling to another robot  18  to assist will need to be repeated. 
     Furthermore, when many robots are clustered in discrete congested locations  903 , as shown in  FIG. 9 , undirected human operators  50 , in the interests of maximizing personal task completion rate and of servicing the clustered robots  18 , may also tend to cluster in those areas in order to execute the tasks associated with those robots  18 , thereby creating a new congested area  903  and/or exacerbating an existing congestion issue. Additionally, if many human operators  50  and robots  18  are clustered together, unattended robots  918  operating in less active portions (e.g., remote area  911 ) of the navigational space can be left unassisted by human operators  50  for extended periods of time, thus causing increased dwell time for those robots  18  and further reducing efficiency. 
     In order to increase human operator  50  efficiency, reduce human operator  50  related congestion, and to mitigate dwell time of unattended robots  918 , described herein are systems and methods for personnel routing. In particular, each robot  18  can be configured to render a next task recommendation on a display (e.g., the display of the tablet  48 ) to an operator  50 . 
     As shown in  FIG. 9 , in some navigational spaces, human operators  50 , untasked human operators  950 , robots  18 , and unattended robots  918  can be located in congested areas  903 , in remote areas  911 , or more generally throughout the navigational space. As illustrated in  FIG. 9 , an untasked human operator  950  (e.g., an operator  50  that has just finished a task and is seeking a new unattended robot  918  to assist) is positioned in an aisle with no unattended robots  918  to assist. Initially, the untasked operator  950  may not know where to head next and may spend time wandering to find an unattended robot  918 . 
     In the scenario depicted in  FIG. 9 , because the untasked operator  950  cannot see any unattended robots  918 , the untasked operator  950  will likely head toward the sound of other activity to seek out unattended robots  918 . In the scenario shown in  FIG. 9 , the untasked operator is thus likely to head for the nearby congested area  903 . However, in the congested area  903  of  FIG. 9 , there are six human operators  50  and only two unattended robots  918 . Thus, the untasked operator  950  would only exacerbate congestion and add little value. 
     Meanwhile, other unattended robots  918  are located in a remote area  911  away from the congested area  903 . As shown in  FIG. 9 , such unattended robots  918  may be so remote as to have no human operators proximate thereto. Accordingly, dwell times for such robots, waiting for a human operator  50  to come assist completion of each unattended robot&#39;s  918  current task, can be elongated, thereby causing substantial inefficiency with respect to execution of that unattended robot&#39;s  918  task list. 
     In some embodiments, to provide untasked operators  950  with direction regarding where to go next, improve task completion rates for unattended robots  918 , and to manage human operator  50  related congestion, a personnel routing system is provided. In particular, as shown in  FIG. 10 , upon completion of a current task, the robot  18  can receive status information corresponding to at least one unattended robot  918  and, using the status information, can determine a next task recommendation  1001  for directing the user to a recommended unattended robot  918 . 
     In some embodiments, the robot  18  can receive the status information directly from each of the at least one unattended robot  918 . In some embodiments, the robot  18  can receive the status information from a robot monitoring server  902 . The robot monitoring server  902  can be any server or computing device capable of tracking robot and/or human operator activity within the navigational space, including, for example, the warehouse management system  15 , the order-server  14 , a standalone server, a network of servers, a cloud, a processor and memory of the robot tablet  48 , the processor and memory of the base  20  of the robot  18 , a distributed system comprising the memories and processors of at least two of the robot tablets  48  and/or bases  20 . In some embodiments, the status information can be pushed automatically from the robot monitoring server  902  to the robot  18 . In other embodiments, the status information can be sent responsive to a request from the robot  18 . 
     Upon receipt of the status information, the robot  18  can use the status information to determine one or more recommendation factors associated with each unattended robot  918 . For example, the robot  18  can use the status information to determine whether a pose location of the unattended robot  918  is in a congested state (i.e. positioned in a congested area  903 ) as described above. Additionally, in some embodiments, efficiency can be improved by minimizing a distance between the robot  18  and the recommended unattended robot  918  of the next task recommendation  1001 . In some embodiments, proximity can be determined according to, for example, a straight line distance between an (x,y,z) position of the robot  18  and an (x,y,z) position of the unattended robot  918 . In some embodiments, proximity can be determined according to a triangulation calculation between the (x,y,z) position of the robot  18  and the (x,y,z) position of at least two unattended robots  918 . In some embodiments, proximity can be determined according to a walking/traveling distance between the (x,y,z) position of the robot  18  and the (x,y,z) position of the unattended robot  918  based on known obstructions such as, for example, shelves  12  as shown in  FIG. 1  or other no-go areas associated with the SLAM map or other knowledge of the navigational space. 
     Other recommendation factors can include number of human operators  50  proximate each unattended robot  918 , a ratio of human operators  50  to unattended robots  918  proximate the each unattended robot  918 , priority of the task to be completed by each unattended robot  918 , current dwell time of the unattended robot  918 , or combinations thereof. By considering such recommendation factors, the personnel routing system can improve task completion efficiency within the navigational space. For example, such recommendation factors can permit the personnel routing system to minimize travel distance, minimize travel time, minimize likely dwell time of the recommended unattended robot  918 , avoid obstacles or congested areas, or combinations thereof. In some embodiments, consideration of multiple recommendation factors can lead to more optimal results. For example, where an unattended robot  918  is located in a congested area  903 , the personnel routing system may default to weighing against directing a human operator  50  to the congested area  903 . However, if there are insufficient human operators  50  to service the robots  18  in the congested area  903 , then the personnel routing system may determine that location of the unattended robot  918  in the congested area  903  weighs in favor of directing the human operator  50  to the congested area  903 . 
     Similarly, in some embodiments, a default preference may be to direct the human operator  50  to the nearest unattended robot. However, if the robot  18  is located within the congested area, there may be a plurality of unattended robots  918  positioned in close proximity to the robot  18  (and the attending human operator  50 ) as well as a large number of human operators  50  available to service those nearby unattended robots  918 . At the same time, there may be no (or very few) human operators  50  close enough to service other unattended robots  918  such as those located in the remote area  911 . The personnel routing system may then determine that such circumstances weigh in favor of directing the human operator  50  to the more distant unattended robots  918  located in the remote area  911 . 
     Referring again to  FIG. 10 , the robot  18  can communicate the next task recommendation  1001  to the human operator  50  by rendering the recommendation  1001  on a display  1000  (e.g., the display of the tablet  48 ) of the robot  18 . In some embodiments, the next task recommendation  1001  can be automatically rendered upon completion of a current task, rendered responsive to an input of the human operator  50 , or combinations thereof. For example, as shown in  FIG. 10 , in some embodiments, the next task recommendation  1001  can be automatically rendered as an interactive graphical object within a task completion interface  1003  indicating completion of the current task. The next task recommendation  1001  can generally include a location  1005  and/or a robot identification  1007  of the recommended unattended robot  918 . 
     The location  1005  can generally include one or more of an aisle identifier  1005   a , a stack identifier  1005   b , a shelf identifier, an (x,y,z) location, any other suitable location indicating information, or combinations thereof. In particular, in  FIG. 10 , the location  1005  includes a letter identifying a particular aisle  1005   a  and a number identifying a particular stack  1005   b . The robot identification  1007  can generally include any suitable identifier for permitting the human operator  50  to verify an identity of the recommended unattended robot  918  when approaching to perform the next task. 
     Although the next task recommendation  1001  is shown in  FIG. 10  as being a proportionally small interactive graphical object indicating the location  1005  and identity  1007  of a single recommended unattended robot  918 , it will be apparent in view of this disclosure that the next task recommendation  1001  can be rendered in any size and/or that the interactive graphical object can be configured to indicate any number of next pick recommendations  1001  each corresponding to a recommended unattended robot  918  for selection by the human operator  50 . To the extent that there are multiple next task recommendations  1001  indicated, the interactive graphical object can be configured to accept a human operator  50  input selecting which next task recommendation  1001  the human operator  50  will accept and attend to. 
     In some embodiments, the interactive graphical object can be configured such that the human operator  50  can touch or “click” the object to open a larger interactive recommendation  1101  screen. As shown in  FIG. 11 , the interactive recommendation  1101  can indicate a plurality of next task recommendations  1001  and corresponding locations  1005  and identifications  1007 . In some embodiments, the interactive recommendation  1101  can advantageously provide larger, more legible text than the smaller, partial screen shown in  FIG. 10 , especially where multiple next task recommendations  1001  are presented. 
     Furthermore, additional information can be presented to the human operator in the interactive recommendation  1101  screen. For example, as shown in  FIG. 11 , a map  1103  is rendered for each next task recommendation  1001 , thus providing the human operator  50  with visual guidance, rather than relying on the human operator&#39;s recollection of a particular facility layout. In some embodiments, each next task recommendation  1001  within the interactive recommendation  1101  screen can be rendered as a separate interactive graphical object. In some embodiments, for example, the map  1103  can be displayed as a plan view of a predetermined area surrounding the robot, which typically represents a portion of the warehouse in proximity to the robot but, in some embodiments, can include a view of the entire warehouse. In some embodiments, the overall warehouse layout may be divided into a plurality of regions and the predetermined area displayed in the map  1103  surrounding the robot could correspond to the one of the plurality of regions in which the robot is located. In some embodiments, the map  1103  can include renderings of shelving units and graphical representations of other robots within a predetermined area. 
     In some embodiments, the map  1103  can also include graphical representations of human operators  50  attending to orders associated with one or more robots  18  within the field of view of the map  1103 . In general, the map  1103  may be useful in permitting the human operator  50  determine which next pick recommendation  1001  to select based on, for example, distance to the unattended robot  918  associated with the next pick recommendation  1001  and/or number of additional unattended robots  918  proximate the unattended robot  918  associated with the next pick recommendation  1001 . 
     To the extent that there are multiple next task recommendations  1001  indicated, the interactive recommendation can be configured to accept a human operator  50  input selecting which next task recommendation  1001  the human operator  50  will accept and attend to. In such embodiments, the robot  18  can communicate the selection to the robot monitoring system  902 , thereby designating the selected task and unattended robot  918  as “in process” within the personnel routing system to avoid redundant recommendation. In such embodiments, the personnel routing system can also effect visual, audio, or other status indicator changes to the unattended robot  918  associated with the next pick recommendation  1001  selected by the operator  50  so as to indicate that the robot has already been selected for task execution. In particular, such status indicators can be provided to deter other human operators in the area from trying to assist/claim the unattended robot  918 . The status indicator may be represented by changing a color or intensity of one or more of the display of the tablet  48  of the unattended robot  918 , a graphical object rendered in the display of the tablet  48 , or one or more lights of the unattended robot  918 . The status indicator may additionally or alternatively be represented by causing one or more of the display of the tablet  48  of the unattended robot  918 , a graphical object rendered in the display of the tablet  48 , or one or more lights of the unattended robot  918  to blink, flash, or pulse. 
     In order to account for human error, unexpected events, and/or other failures to execute, the “in process” designation can, in some embodiments, be removed by the personnel routing system if the task is not completed within a prescribed time limit. 
     It will be apparent in view of this disclosure that the example personnel routing and congestion management techniques are described above for illustration purposes only and that any other beacon message, beacon configuration, receiver configuration, or proximity operation mode can be implemented in accordance with various embodiments. 
     Non-Limiting Example Computing Devices 
       FIG. 12  is a block diagram of an exemplary computing device  1210  such as can be used, or portions thereof, in accordance with various embodiments as described above with reference to  FIGS. 1-11 . The computing device  1210  includes one or more non-transitory computer-readable media for storing one or more computer-executable instructions or software for implementing exemplary embodiments. The non-transitory computer-readable media can include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (for example, one or more magnetic storage disks, one or more optical disks, one or more flash drives), and the like. For example, memory  1216  included in the computing device  1210  can store computer-readable and computer-executable instructions or software for performing the operations disclosed herein. For example, the memory can store software application  1240  which is programmed to perform various of the disclosed operations as discussed with respect to  FIGS. 1-11 . The computing device  1210  can also include configurable and/or programmable processor  1212  and associated core  1214 , and optionally, one or more additional configurable and/or programmable processing devices, e.g., processor(s)  1212 ′ and associated core (s)  1214 ′ (for example, in the case of computational devices having multiple processors/cores), for executing computer-readable and computer-executable instructions or software stored in the memory  1216  and other programs for controlling system hardware. Processor  1212  and processor(s)  1212 ′ can each be a single core processor or multiple core ( 1214  and  1214 ′) processor. 
     Virtualization can be employed in the computing device  1210  so that infrastructure and resources in the computing device can be shared dynamically. A virtual machine  1224  can be provided to handle a process running on multiple processors so that the process appears to be using only one computing resource rather than multiple computing resources. Multiple virtual machines can also be used with one processor. 
     Memory  1216  can include a computational device memory or random access memory, such as but not limited to DRAM, SRAM, EDO RAM, and the like. Memory  1216  can include other types of memory as well, or combinations thereof. 
     A user can interact with the computing device  1210  through a visual display device  1201 ,  111 A-D, such as a computer monitor, which can display one or more user interfaces  1202  that can be provided in accordance with exemplary embodiments. The computing device  1210  can include other I/O devices for receiving input from a user, for example, a keyboard or any suitable multi-point touch interface  1218 , a pointing device  1220  (e.g., a mouse). The keyboard  1218  and the pointing device  1220  can be coupled to the visual display device  1201 . The computing device  1210  can include other suitable conventional I/O peripherals. 
     The computing device  1210  can also include one or more storage devices  1234 , such as but not limited to a hard-drive, CD-ROM, or other computer readable media, for storing data and computer-readable instructions and/or software that perform operations disclosed herein. Exemplary storage device  1234  can also store one or more databases for storing any suitable information required to implement exemplary embodiments. The databases can be updated manually or automatically at any suitable time to add, delete, and/or update one or more items in the databases. 
     The computing device  1210  can include a network interface  1222  configured to interface via one or more network devices  1232  with one or more networks, for example, Local Area Network (LAN), Wide Area Network (WAN) or the Internet through a variety of connections including, but not limited to, standard telephone lines, LAN or WAN links (for example, 802.11, T1, T3, 56 kb, X.25), broadband connections (for example, ISDN, Frame Relay, ATM), wireless connections, controller area network (CAN), or some combination of any or all of the above. The network interface  1222  can include a built-in network adapter, network interface card, PCMCIA network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the computing device  1210  to any type of network capable of communication and performing the operations described herein. Moreover, the computing device  1210  can be any computational device, such as a workstation, desktop computer, server, laptop, handheld computer, tablet computer, or other form of computing or telecommunications device that is capable of communication and that has sufficient processor power and memory capacity to perform the operations described herein. 
     The computing device  1210  can run any operating system  1226 , such as any of the versions of the Microsoft® Windows® operating systems (Microsoft, Redmond, Wash.), the different releases of the Unix and Linux operating systems, any version of the MAC OS® (Apple, Inc., Cupertino, Calif.) operating system for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, or any other operating system capable of running on the computing device and performing the operations described herein. In exemplary embodiments, the operating system  1226  can be run in native mode or emulated mode. In an exemplary embodiment, the operating system  1226  can be run on one or more cloud machine instances. 
       FIG. 13  is an example computational device block diagram of certain distributed embodiments. Although  FIGS. 1-11 , and portions of the exemplary discussion above, make reference to a warehouse management system  15 , order-server  14 , or robot tracking server  902  each operating on an individual or common computing device, one will recognize that any one of the warehouse management system  15 , the order-server  14 , or the robot tracking server  902  may instead be distributed across a network  1305  in separate server systems  1301   a - d  and possibly in user systems, such as kiosk, desktop computer device  1302 , or mobile computer device  1303 . For example, the order-server  14  may be distributed amongst the tablets  48  of the robots  18 . In some distributed systems, modules of any one or more of the warehouse management system software and/or the order-server software can be separately located on server systems  1301   a - d  and can be in communication with one another across the network  1305 . 
     While the foregoing description of the invention enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiments and examples herein. The above-described embodiments of the present invention are intended to be examples only. Alterations, modifications and variations may be effected to the particular embodiments by those of skill in the art without departing from the scope of the invention, which is defined solely by the claims appended hereto. The invention is therefore not limited by the above described embodiments and examples. 
     Having described the invention, and a preferred embodiment thereof, what is claimed as new and secured by letters patent is: