Patent Publication Number: US-10769716-B2

Title: Customer assisted robot picking

Description:
FIELD OF THE INVENTION 
     This invention relates to robot picking and more particularly to customer assisted robot picking. 
     BACKGROUND OF THE INVENTION 
     Ordering products from retailers over the internet for home delivery or in-store pickup 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 or held at a retail location for pickup by the customer. The process of “fulfillment” involves physically taking or “picking” these items from a retail environment open to in-store customers, packing them, and either shipping them to the designated address or staging them at a specified on-site location for customer pickup. An important goal of the order-fulfillment process in a retail environment is thus to pick and pack as many items in as short a time as possible without negatively impacting the in-store shopping experience. 
     The order-fulfillment process typically takes place in a retail environment containing many products, including those listed in the order. Among the tasks of order fulfillment is therefore that of traversing the retail environment to find and collect the various items listed in an order. In addition, the products that will ultimately be shipped or staged first need to be received in the retail environment and stocked (placed) on retail display shelving in an orderly fashion throughout the retail environment so they can be readily retrieved both by in-store customers and by pickers fulfilling orders for shipping or in-store pickup. 
     In a retail environment, the goods that are being delivered and ordered can be stored far apart from each other and dispersed among a great number of other goods. 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 and increases the number of employees required to perform the picking tasks. 
     In retail environments, placing and picking of items is typically performed by retail employees such as surplus cashiers, floor associates, stock associates, etc. That is, generally, specialized “pickers” are not typically available in a retail environment. Thus, a high volume of in-store pickup or in-store picking of orders to be shipped can result in a need to increase staffing levels, thereby increasing costs. Alternatively, if staffing levels are not sufficiently increased, the in-store customer experience is negatively impacted due to retail employees being preoccupied with picking tasks and thus inattentive to in-store customers. 
     BRIEF SUMMARY OF THE INVENTION 
     In order to increase picking efficiency and reduce required staffing load, 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 retail environment or to “pick” items from various locations for packing and shipping or staging. 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. In order to further drive efficiency in retail environments, in-store customers can be leveraged as human operators to perform at least some of the picking tasks. 
     Provided herein are methods and systems for customer assisted robot picking for improvement of pick efficiency in a retail environment. 
     In one aspect the invention features a method for customer assisted robot picking. The method includes navigating a robot to a pose location within a retail space in proximity to an item to be picked, the retail space having items for purchase by customers. The method also includes the robot identifying, by a sensor in communication with the robot, a customer located within a zone proximate the robot. The method also includes communicating to the customer information identifying the item to be picked. The method also includes detecting presentation of the item by the customer for identification. The method also includes updating customer performance data stored in a customer account to include data corresponding to picking of the item by the customer. 
     In some embodiments, the method also includes accessing, via the robot in response to the identification of the assisting customer, the customer account. In some embodiments, the method also includes receiving, from the customer account the updated customer performance data. In some embodiments, the method also includes rendering, on an interactive display device of the robot, in response to the customer performance data, at least one graphic representation of customer assistance achievement. In some embodiments, the step of identifying also includes reading, by the sensor, an ID tag of the customer. In some embodiments, the ID tag is at least one of a passive RFID tag, an active RFID tag, a Bluetooth transceiver, or a near-field communications (NFC) beacon. In some embodiments, the sensor is at least one of an RFID reader, a Bluetooth transceiver, or a NFC transceiver. In some embodiments, the step of identifying also includes capturing, by the sensor, a facial image of the customer. In some embodiments, the step of identifying also includes comparing the captured facial image to an image recognition database. In some embodiments, the sensor is at least one of a digital camera, a digital video camera, an image sensor, a charge coupled device (CCD), or a CMOS sensor. In some embodiments, the step of identifying also includes capturing, by the sensor, at least one of a voiceprint of the customer, a retinal pattern of the customer, or a fingerprint pattern of the customer. In some embodiments, the step of identifying also includes comparing the captured at least one of a voiceprint of the customer, a retinal pattern of the customer, or a fingerprint pattern of the customer to a corresponding customer account database. In some embodiments, the sensor is at least one of an imaging device, a camera, a video camera, an audio sensor, a retinal scanner, a fingerprint scanner, an infrared scanner, a barcode scanner, or a RFID reader. In some embodiments, the step of rendering also includes displaying at least one customer reward on the interactive display device. In some embodiments, the at least one customer reward is rendered in response to a milestone achieved by the customer. In some embodiments, the milestone includes at least one of a predefined number of units picked by the customer, a predefined pick rate of the customer, or a predefined number of units picked by the customer without scanning an erroneous unit. In some embodiments, the at least one customer reward includes one or more of an earned discount, a free promotional item, a store credit, a cash back incentive, redeemable loyalty points, or combinations thereof. 
     In another aspect the invention features a system for customer assisted robot picking. The system includes a robot operating within a retail space having items for purchase by customers. The system also includes a sensor in electronic communication with the robot. The robot includes a processor. The robot also includes a memory storing instructions. The instructions, when executed by the processor, cause the robot to navigate the robot to a pose location within the retail space in proximity to an item to be picked. The instructions, when executed by the processor, also cause the robot to identify, by the sensor, a customer located within a zone proximate the robot. The instructions, when executed by the processor, also cause the robot to communicate to the customer information identifying the item to be picked. The instructions, when executed by the processor, also cause the robot to detect presentation of the item by the customer for identification. The instructions, when executed by the processor, also cause the robot to update customer performance data stored in a customer account to include data corresponding to picking of the item by the customer. 
     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 a retail environment; 
         FIG. 2A  is a front elevational view of a base of one of the robots used in the retail environment shown in  FIG. 1 ; 
         FIG. 2B  is a perspective view of a base of one of the robots used in the retail environment 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 retail environment 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 retail environment and storing fiducial marker poses; 
         FIG. 6  is a table of the fiducial identification to pose mapping; 
         FIG. 7  is a table of the item location to fiducial identification mapping; 
         FIG. 8  is a flow chart depicting product SKU to pose mapping process; 
         FIG. 9  is a block diagram illustrating an architecture of a tablet of the robot shown in  FIG. 3 ; 
         FIG. 10  is a flow-chart of a procedure executed by the tablet shown in  FIG. 9 ; 
         FIG. 11  is a block diagram illustrating an architecture of an alternative tablet of the robot shown in  FIG. 3 ; 
         FIG. 12  is a diagram illustrating an example customer assistance achievement rendered on the tablet of the robot shown in  FIG. 3 ; 
         FIG. 13  is a block diagram of an exemplary computing system implemented in the robot of  FIG. 3 ; and 
         FIG. 14  is a network diagram of an exemplary distributed network which may be utilized in a retail operation described herein. 
     
    
    
     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 customer assisted robot picking. Although not restricted to any particular application, one suitable application that the invention may be used in is order fulfillment within a retail environment. The use of robots in this application will be described to provide context for the customer assisted robot picking but is not limited to that application. The term “retail environment”, as used herein, can include any physical environment in which customers can shop for goods, products, services, or combinations thereof. For example, retail environments can include, but are not limited to, retail stores, electronics stores, department stores, “big box” retailers, hardware stores, home improvement stores, supermarkets, grocery stores, malls, outdoor markets, garden centers, any other physical environment in which customers can shop or combinations thereof. 
     Referring to  FIG. 1 , a typical retail environment  10  includes shelves  12  filled with the various items that could be included in an order. In operation, a retail management system  15  receives an incoming stream of orders  16  from customers who will pick up the orders in store and/or will get the orders shipped to a delivery address. The incoming stream of orders  16  is then sent by the retail management system  15  to 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 customers, 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 retail management system  15  and retail management software or the order server functionality may be integrated into the retail management software and run on the retail management system  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 retail environment  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 a 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 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. 
     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. 
     With current robot technology, quickly and efficiently picking items from a shelf and placing them in the tote  44  is technically challenging due to functional difficulties associated with robotic manipulation of objects. Thus, currently, a more efficient way of picking items is to use an customer  50 , 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 customer  50  via the tablet  48  (or laptop/other user input device), which the customer  50  can read, or by transmitting the order to a handheld device used by the customer  50 . 
     Upon receiving an order  16  from the order server  14 , the robot  18  proceeds to a first 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 retail environment  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 customer  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 customer  50  are described. However, as is evident from  FIG. 1 , a typical fulfillment operation includes many robots and customers working among each other in the retail environment 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 retail environment 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 retail environment  10  must be created and the location of various fiducial markers dispersed throughout the retail environment must be determined. To do this, one or more of the robots  18  as they are navigating the retail environment 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 (grid-mapping) 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 retail environment  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 retail environment  10  using camera  26  to scan the environment to locate fiducial markers (two-dimensional bar codes) dispersed throughout the retail environment on shelves proximate stock locations, 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 retail environment 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 retail environment  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 retail environment 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 retail environment 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 item locations (e.g.  402   a - f ) within retail environment  10 , which are correlated to particular fiducial ID&#39;s  404 , e.g. number “11”. The item locations, in this example, consist of seven alpha-numeric characters. The first six characters (e.g. L01001) pertain to the shelf location within the retail environment and the last character (e.g. A-F) identifies the particular item location at the shelf location. In this example, there are six different item locations associated with fiducial ID “11”. There may be one or more item locations associated with each fiducial ID/marker. 
     The alpha-numeric item locations are understandable to humans, e.g. customer  50 ,  FIG. 3 , as corresponding to a physical location in the retail environment  10  where items are stored. However, they do not have meaning to robot  18 . By mapping the locations to fiducial ID&#39;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 retail 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 item 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 retail management system  15 , and from the SKU number(s), the item location(s) is/are determined in step  506 . A list of item locations for the order is then transmitted to robot  18 . In step  508 , robot  18  correlates the item 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 customer can pick the item to be retrieved from the appropriate item location and place it on the robot. 
     Item specific information, such as SKU number and item location, obtained by the retail management system  15 /order server  14 , can be transmitted to tablet  48  on robot  18  so that the customer  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 retail environment  10  and the walls  114 , shelves (such as shelf  12 ) and other obstacles  116 . As the robot begins to traverse the retail environment 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 customers  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 retail environment space without having to use more complex navigation approaches typically used which involve grid lines and intermediate fiducial markers to determine location within the retail environment. 
     Customer Identification and Achievement Tracking 
     As explained above, typically, 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 customer  50  to retrieve the item from the shelf  12  and place it in tote  44 . Referring now to  FIGS. 9 and 10 , for each picking interaction between the robot  18  and the customer  50 , the robot  18  can be configured to identify the customer  50  and track picking performance associated with the picking interaction such as, for example, number of items picked by the customer, pick rate of the customer, and/or error rate (e.g., number of units picked by the customer without scanning an erroneous unit or percentage of erroneously scanned items). 
     In particular, once the robot  18  is parked at the correct pose location proximate the fiducial  30 , the robot  18  can interrogate a database-clock of a database in communication with the robot  18  to determine the time at which the robot  18  parked at the pose proximate the fiducial marker  30  (step  601  of method  600  of  FIG. 10 ). The robot can then create a record in the database of the arrival time at the pose (step  603 ). In some embodiments, instead of interrogating the database-clock, the robot  18  may cause a database-timer to start counting time. In either case, the goal is to determine how long the robot  18  is kept waiting. 
     In some embodiments, the database in communication with the robot  18  can be a remote standalone database. In some embodiments, the database can be incorporated into a memory of the RMS  15  or the order-server  14 . In some embodiments, the database can be incorporated into the tablet  48 . In such embodiments a tablet-processor  52  can then interrogate a tablet-clock  54  to determine the time at which robot  18  parked at the pose proximate the fiducial marker  30  (step  601  of method  600  of  FIG. 10 ). The tablet-processor  52  can then create a record  56  in a tablet-memory  58  of the arrival time at the pose (step  603 ). In some embodiments, instead of interrogating a tablet-clock  54 , the tablet-processor  52  may instead cause a tablet-timer  60  to start counting time. 
     In general, after the robot  18  is parked at the pose, the customer  50  will see the robot  18  and walk toward it. The customer  50  then inspects the tablet  48  to determine which item should be retrieved, retrieves the item from the shelf  12 , and places it on robot  18 , for example, into the tote  44 . In some embodiments, upon completion of the picking task, when the item has been placed on the robot  18 , the robot  18  can re-interrogate the database-clock or stop the database-timer to determine a dwell time spent at each pose. 
     In some embodiments, the robot  18  can include a proximity sensor  62 . In some embodiments, the proximity sensor  62  can be configured to detect any customer  50  approaching the robot  18 . As further shown in  FIG. 3 , upon entry of the customer  50  into a proximity zone  66  surrounding the robot  18 , the proximity sensor  62  can detect a tag  64  carried or worn by the customer  50  (step  605 ). Such tags  64  can include active or passive RFID tags, Bluetooth devices, near-field communications (NFC) devices; cellphones, smartphones, or any other suitable devices. 
     Referring again to  FIGS. 9 and 10 , to the extent that the customer  50  is carrying the tag  64 , the proximity sensor  62  then communicates the information concerning the tag  64  to the database (step  607 ). The database then updates the record to document identification information associated with the tag  64 . If desired, the robot can also record a time at which the customer  50  entered the zone (step  609 ). 
     The customer  50  then inspects the tablet  48  to learn which item or items should be picked. Alternatively, the robot  18  (e.g., via tablet  48 ) can transmit information concerning an item to be picked to a handheld device used by the customer  50 . The customer  50  then retrieves the item or items from the shelf  12  and places the item or items into the tote  44 , at which point the robot  18  indicates task completion and either re-interrogates the database-clock or stops the database-timer to determine dwell time of the robot  18  at that pose. The customer  50  then leaves the zone  66 . 
     In some embodiments, the pose location of the robot  18  can be positioned such that the customer  50  does not have to leave the zone  66  to retrieve the item. To that end, and more generally, the size of zone  66  can vary depending on the particular application. For example, in some embodiments the zone  66  can be approximately one to two meters in diameter centered on the location of robot  18 . 
     If desired, the proximity sensor  62  can detect the departure of the customer  50  (and, if applicable, the accompanying tag  64 ) from the zone  66  (step  611 ) and update the record  56  to reflect the time of departure (step  613 ). After the customer  50  leaves the zone  66 , the robot  18  then moves on to its next destination (step  615 ), which could be another shelf  12  or a packing station for check-out. 
     In other embodiments, shown in  FIG. 11 , the customer  50  does not need to carry an identifying tag  64  for the robot  48  to detect the customer  50  within the zone  66 . Instead, the tablet  48  is coupled to an on-board identification system  86 . For example, as shown in  FIG. 11 , the on-board identification system  86  includes an identification system  88  configured to receive identifying information from a user recognition device  90  and further configured to consult an identification database  92  to identify the customer  50 . For example, in some embodiments, the user recognition device  90  can include one or more of an imaging device (e.g., having an image sensor such as a charge coupled device (CCD) or a CMOS sensor), a camera, a video camera, an audio sensor, a retinal scanner, a fingerprint scanner, an infrared scanner, a barcode scanner, or combinations thereof. In some embodiments, the identification database  92  can include a facial recognition database, a retinal database, a voice pattern database, a fingerprint database, a barcode database, or combinations thereof. 
     Regardless of the customer identification methodology, the robot  18  can associate the pick and any associated customer performance data to a corresponding customer account. The data collected by the tablet  48  can then be transmitted to the retail management system  15  and/or the order-server  14  either in real time as it is acquired or periodically for association with customer performance data stored in the customer account. The data thus collected provides a basis for rewarding performance of the customer  50  as well as any other customers that have interacted with the robot  18 . In general, customer performance data such as number of items picked by the customer  50 , pick rate of the customer  50 , and/or error rate (e.g., number of units picked by the customer  50  without scanning an erroneous unit or percentage of erroneously scanned items) can be used in connection with a customer incentive reward program. 
     In addition to evaluating performance, data collected by the tablet  48 , in particular, customer identification data, can be used by retail management system  15  for security purposes to determine if customer  50  is an authorized assisting customer, is authorized to operate in a particular region of the retail environment, or for a particular customer. Moreover, the identification data can be used to set preferences for customer  50 , such as language used by tablet  48 . 
     On a system wide basis, data corresponding to a plurality of interactions between a plurality of robots  18  and a plurality of customers  50  (e.g., as in a retail environment having a fleet of robots  18  each interacting with a plurality of retail environment picker customers  50  throughout various locations within the retail environment). Thus, for example, all of the other robots  18 , as depicted in  FIG. 1 , also collect data from customers  50  with which they interact and transmit the data to management server  84 . This data is thus available to management to assist with setting in-store staffing levels, permit informed assessment and adjustment of incentive rewards offered to customers  50 , and track earned value associated with customer assisted picking. 
     The data collected by robot  18  and transmitted to retail management system  15  indicative of customer activity includes information regarding one or more of the following: the amount of time for a customer to enter the zone  66  after the robot  18  arrives at the pose, the amount of time customer  50  takes to exit zone  66  after the customer  50  enters the zone, the amount of time the customer  50  takes to perform a defined function, such as picking an item from shelf  12  and placing on the robot  18  or picking an item from robot  18  and placing it on shelf  12 , and how many, if any, erroneous items are scanned by the customer  50 . 
     By use of such data, the retail management system  15  can be configured to track customer efficiency based at least in part on the information collected indicative of customer activity. The management system  15  may be configured to maintain retail environment statistics based at least in part on this information. Customer efficiency and other statistics collected/computed may be may be used as an incentive to increase customer performance or in other ways by management. For example, to the extent that a particular pose is associated with abnormally long time for customers to perform a picking function, abnormally long time between customer entry and exit from the zone  66 , or abnormally long time between arrival at the pose and customer entry of the zone  66 , the management system  15  and/or order-server  14  can update the pose location to improve proximity to the corresponding shelf locations and/or to improve robot visibility. 
     Customer Incentive Reward Program 
     As explained above, in retail environments, placing and picking of items is typically performed by retail employees such as surplus cashiers, floor associates, stock associates, etc. Thus, a high volume of in-store pickup or in-store picking of orders to be shipped can result in a need to increase staffing levels, thereby increasing costs, and/or negatively impacting the in-store customer experience due to retail employees being preoccupied with picking tasks and thus inattentive to in-store customers. In order to reduce such added costs and keep retail employees available to in-store customers, in some embodiments, customer assisted picking using the robots  18  can be implemented. In order to incentivize customers  50  to participate in the customer assisted picking program, in some embodiments, a customer incentive reward program can be implemented. 
     As shown in  FIG. 12 , the customer incentive reward program, in some embodiments, can be presented to the customer  50  at least partially via a display  700  of the tablet  48 . In particular, as shown for example in  FIG. 12 , the display  700  can include one or more indicators of current employee performance corresponding to the customer  50 . Such performance indicators can include, for example, badges  701 , a performance meter  703 , a performance goal  705 , a listing of earned incentives  707 , or combinations thereof. It will further be apparent that the display  700  elements shown in  FIG. 12  are for illustrative purposes only and that additional text data, numerical data, alternative graphics, or other customer performance related objects can be provided to the customer in some embodiments. For example, the customer  50 , in some embodiments, can query one or more of an all-time highest pick rate (units per hour) achieved by any customer in the retail environment, an all-time highest pick rate achieved by the customer  50 , a highest pick rate achieved by any customer  50  in the retail environment for a day, a week, a month, a quarter, a year, or any other temporal window, a highest pick rate achieved by the customer for a day, a week, a month, a quarter, a year, or any other temporal window, a highest number of units picked by any customer in the retail environment in an hour, a day, a week, a month, a quarter, a year, or any other temporal window, a highest number of units picked by the customer  50  in an hour, a day, a week, a month, a quarter, a year, or any other temporal window, average customer  50  pick rate, all time number of units picked by the customer  50 , average pick rate of all customers  50 , total units picked in the retail environment by all customers  50 , whether all-time or in a day, a week, a month, a quarter, a year, or any other temporal window, average aggregate pick rate in the retail environment of all customers  50 , whether all-time or in a day, a week, a month, a quarter, a year, or any other temporal window, or any other suitable performance data. 
     The badges  701 , in some embodiments, can be awarded to the customer  50  upon achievement of one or more milestones. Milestones can include, for example, a number of units picked (e.g., 25, 50, 100, 1,000, 10,000, or any other number of units) by the customer  50 , the customer  50  maintaining a predefined pick rate for one or more predetermined time periods, achievement of a personal best pick rate by the customer  50 , conducting error free picking (e.g., not picking an erroneous item) by the customer  50  for a predetermined amount of time, or any other suitable milestones or achievements. 
     The performance meter  703 , in some embodiments, can indicate customer  50  performance relative to one or more of customer-specific goals or standards, peer performance, or combinations thereof. For example, to reach a milestone or incentive, a customer  50  may have a target of picking 100 units in a month, which can be indicated as “average” or middle performance relative to other customer pickers (e.g., “AVERAGE” on the performance meter  703  shown in  FIG. 12 ). The performance meter  703  can then, based on the customer&#39;s  50  actual number of units picked, indicate whether the performance is “BAD”, “POOR”, “AVERAGE”, “GOOD”, or “EXCELLENT”. For example, in some embodiments, BAD can be any number of units less than 25, POOR can be any number of units between 25 and 74, AVERAGE can be any number of units between 75 and 124, GOOD can be any number of units between 125 and 149, and EXCELLENT can be any number of units greater than 150. However, it will be apparent in view of this disclosure that the performance meter  703  can be any suitable graphic (e.g., a dial meter as shown, a segmented bar, a solid bar, or any other suitable graphic) and can include color, grayscale, text, images, or any number and combination thereof to convey a performance status of the customer  50 . It will be further apparent in view of this disclosure that, although shown as including five performance categories, labeled as “BAD”, “POOR”, “AVERAGE”, “GOOD”, and “EXCELLENT”, the performance meter  703  can have any number of segments, categories, other performance indicators, or combinations thereof and that those segments, categories, other performance indicators, or combinations thereof can be unlabeled or labeled with any suitable label desired. 
     The performance goal  705  can be used to a goal performance level for reaching a next milestone and/or indicate recent performance achievement. For example, as shown in  FIG. 12 , the customer  50  has achieved “gold level” by picking 100 items and can achieve the next level by reaching 200 picked items. However, it will be apparent in view of this disclosure that any type and gradation of performance level can be used in accordance with various embodiments. 
     The customer assisted picking program, via the customer account, can further provide a reward/award mechanism for recognizing customer achievements. Such rewards can be presented by the robot  18  via the listing of earned incentives  707  displayed on the tablet  48 . The listing  707  can provide the customer  50  with a list of incentive rewards associated with the performance level achieved by the customer  50 . For example, as shown in  FIG. 12 , the customer has earned 25% off the customer&#39;s  50  next purchase, a $5 gift card, and 5% off all purchases next month. More generally, customer rewards can include one or more of an earned discount, a free promotional item, a store credit, a cash back incentive, redeemable loyalty points, or combinations thereof. However, it will be apparent in view of this disclosure that any reward structure can be used in connection with various embodiments. 
     Although described herein as being displayed on a tablet  48  of the robot  18 , it will be apparent in view of this disclosure that customer performance data and outcomes can be displayed on any suitable device including a display. For example, the total number of customer assisted picks performed in a specified time period, in some embodiments, can be presented on one or more large displays located in and around the retail environment so that customers and employees can observe aggregated retail environment-wide performance without needing to query a robot  18 . Additionally, in some embodiments, the robot  18  and/or tablet  48  may be in communication with a handheld or wearable device (e.g., a mobile phone, smart watch, augmented reality glasses, handheld scanner, other suitable devices, or combinations thereof), which can be used to display or otherwise communicate (e.g., via audio or video messages) customer performance data and outcomes to the customer  50 . 
     Non-Limiting Example Computing Devices 
       FIG. 13  is a block diagram of an exemplary computing device  810  such as can be used, or portions thereof, in accordance with various embodiments as described above with reference to  FIGS. 1-12 . The computing device  810  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  816  included in the computing device  810  can store computer-readable and computer-executable instructions or software for performing the operations disclosed herein. For example, the memory can store software application  840  which is programmed to perform various of the disclosed operations as discussed with respect to  FIGS. 1-12 . The computing device  810  can also include configurable and/or programmable processor  812  and associated core  814 , and optionally, one or more additional configurable and/or programmable processing devices, e.g., processor(s)  812 ′ and associated core (s)  814 ′ (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  816  and other programs for controlling system hardware. Processor  812  and processor(s)  812 ′ can each be a single core processor or multiple core ( 814  and  814 ′) processor. 
     Virtualization can be employed in the computing device  810  so that infrastructure and resources in the computing device can be shared dynamically. A virtual machine  824  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  816  can include a computational device memory or random access memory, such as but not limited to DRAM, SRAM, EDO RAM, and the like. Memory  816  can include other types of memory as well, or combinations thereof. 
     A user can interact with the computing device  810  through a visual display device  801 , such as a computer monitor, which can display one or more user interfaces  802  that can be provided in accordance with exemplary embodiments. The computing device  810  can include other I/O devices for receiving input from a user, for example, a keyboard or any suitable multi-point touch interface  818 , a pointing device  820  (e.g., a mouse). The keyboard  818  and the pointing device  820  can be coupled to the visual display device  801 . The computing device  810  can include other suitable conventional I/O peripherals. 
     The computing device  810  can also include one or more storage devices  834 , 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  834  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  810  can include a network interface  822  configured to interface via one or more network devices  832  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  822  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  810  to any type of network capable of communication and performing the operations described herein. Moreover, the computing device  810  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  810  can run any operating system  826 , 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  826  can be run in native mode or emulated mode. In an exemplary embodiment, the operating system  826  can be run on one or more cloud machine instances. 
       FIG. 14  is an example computational device block diagram of certain distributed embodiments. Although  FIGS. 1-12 , and portions of the exemplary discussion above, make reference to a retail management system  15  and an order-server  14  each operating on an individual or common computing device, one will recognize that any one of the retail management system  15 , the order-server  14 , and/or the zone server may instead be distributed across a network  905  in separate server systems  901   a - d  and possibly in user systems, such as kiosk, desktop computer device  902 , or mobile computer device  903 . For example, the order-server  14  and/or the zone server may be distributed amongst the tablets  48  of the robots  18 . In some distributed systems, modules of any one or more of the retail environment management system software, the order-server software, and the zone engine can be separately located on server systems  901   a - d  and can be in communication with one another across the network  905 . 
     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.