Patent Application
20230315098
This application claims the benefit of and priority to U.S. patent application Ser. No. 17/710,286, filed Mar. 31, 2022, which is hereby incorporated by reference in its entirety.
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Self-driving robotic vehicles are being used in warehouses to perform tasks, such as moving pallets of goods. Typically, a central controller monitors the status of all tasks for a warehouse in a database and, when necessary, instructs a robotic vehicle to move to a specified location and complete an assigned task. The robotic vehicles may be equipped with sensors that allow them to avoid running into infrastructure, other robotic vehicles, and humans, but the robotic vehicles do not purposely interact with or take instructions directly from humans. Nor do the robotic vehicles have the ability to add, delete, or change entries within the database.
The present systems and methods relate to self-driving robotic vehicles (SDRVs) that interact with humans to ascertain and serve the humans' needs. The SDRVs are components operating within systems disclosed herein to carry out methods for efficiently serving customers and, optionally, collecting payment therefrom.
Throughout this document, the SDRVs are described as hosts, servers, food runners, and/or cashiers in a restaurant environment. However, the systems and methods can be adapted for use in other settings, and nothing herein is intended to limit the present disclosure to restaurant services.
Some systems and methods disclosed herein allow SDRVs to perform a monitoring function, where an SDRV visits one or more customer locations to ascertain customer needs, essentially seeking a task to be performed by itself or another SDRV. In an aspect, a method for identifying and scheduling tasks associated with customer needs comprises receiving, at a self-driving robotic vehicle (SDRV), instructions to physically move to a customer location and to inquire at the customer location about customer needs and, in response to the inquiry, the SDRV adds one or more tasks associated with goods or services required to meet the customer needs to a task queue. In an embodiment, the SDRV performs at least one of the tasks added to the queue and/or another SDRV performs at least one of the tasks added to the queue.
Adding the one or more tasks to the task queue may be accomplished, for example, when the SDRV transmits a signal encoding the one or more tasks to a centralized controller comprising the task queue or to cloud storage comprising the task queue and/or when the SDRV stores the one or more tasks locally in a local task queue. In an embodiment, the one or more tasks added to the task queue may be prioritized within the task queue by the SDRV or a processor of the controller executing scheduling software.
In an embodiment, the step of inquiring at the customer location about customer needs comprises one or more of the SDRV presenting a visual message or a video message, the SDRV presenting an audio message, and the SDRV visually inspecting the customer location.
In an embodiment, a method may further comprise adjusting timing for subsequently scheduled tasks associated with the customer location when one or more tasks corresponding to a customer need are added to the task queue. For example, adjusting the timing for the subsequently scheduled tasks may comprise: (i) delaying some or all of the subsequently scheduled tasks by an equal amount of time; (ii) canceling one or more of the subsequently scheduled tasks; (iii) expediting one or more of the subsequently scheduled tasks; (iv) coordinating one or more of the subsequently scheduled tasks to coincide with timing of one of the tasks corresponding to the customer needs; or (iv) a combination of (i)-(iv).
In an aspect, a method of collecting payment from a customer using a self-driving robotic vehicle (SDRV) comprises instructing an SDRV to physically move to a customer location and to display payment options for an electronic funds transfer. For example, the payment options may include one or more of a bar code, a QR code, a keypad, a near field infrared receiver, a magnetic strip reader, and a URL address for a payment portal.
In an embodiment, a method of collecting payment from a customer further comprises instructing the SDRV to physically move away from the customer location after receipt of the electronic funds transfer is confirmed.
In an aspect, a method for forecasting and scheduling tasks for completion by a self-driving robotic vehicle comprises: obtaining historical frequency data for a first task performed repeatedly at a specified service location, wherein the first task occurs sporadically; calculating a first time interval between first task occurrences or a mean first task rate using the historical frequency data; determining an amount of time required for a self-driving robotic vehicle (SDRV) to complete the first task; and when the amount of time required for the SDRV to complete the first task is less than the time interval between the first task occurrences, scheduling the SDRV to perform the first task at least as frequently as the time interval between the first task occurrences.
In an embodiment, the time interval between the first task occurrences is determined from a Poisson function.
In an embodiment, a method for forecasting and scheduling tasks further comprises determining a second time interval between completion of the first task and a start of a second task, the completion of the first task and the start of the second task being dependent upon one another.
In an embodiment, an amount of time required to perform the second task depends upon a quantity associated with the first task. For example, the quantity associated with the first task may be a number of guests in a party or a number of items in an order, where an amount of time required to take or deliver orders is directly proportional to the size of the party or the size of the order.
In an embodiment, a method for forecasting and scheduling tasks further comprises steps within a method for identifying and scheduling tasks associated with customer needs described herein.
In an embodiment, a method for forecasting and scheduling tasks further comprises determining that a value of the first task has reached a threshold and calculating an expected time, based on scheduled tasks within a task queue, when the value of the first task will fall below the threshold. For example, a value of the first task may reach a threshold when all tables within a restaurant are full, and an expected wait time for additional guests may be calculated based on tasks remaining for a given customer location/table in the task queue.
In an aspect, a system for carrying out one or more of the methods disclosed herein comprises: one or more self-driving robotic vehicles (SDRVs) that provides at least one good/item or service at a customer location to meet a customer need, a task queue stored in a memory device, and a processor in communication with the task queue and the SDRV, wherein the processor executes instructions for identifying and scheduling tasks associated with customer needs, collecting payment from a customer, and/or forecasting and scheduling tasks. In an embodiment, the memory device and/or the processor is/are disposed within a centralized controller, within an SDRV, or both.
Illustrative embodiments of the present invention are described in detail below with reference to the attached drawings, wherein:
In general, the terms and phrases used herein have their art-recognized meaning, which can be found by reference to standard texts, journal references and contexts known to those skilled in the art. The following definitions are provided to clarify their specific use in the context of this description.
As used herein, a “self-driving robotic vehicle (SDRV)” is a vehicle capable of navigating routes through the use of onboard sensors that detect exogenous navigation markers or other objects within the surroundings. For example, an SDRV may contain one or more infrared, near field, or radio sensors, cameras, or other detection devices that capture data from navigation markers or other surroundings. The data or images are then analyzed by a local or remote computer processor to determine what action the SDRV should perform (e.g., stop, turn, reverse, etc.), and the processor sends instructions for carrying out the action to motive components of the SDRV. The motive components then perform the action—all without human supervision.
The terms “direct and indirect” describe the actions or physical positions of one object relative to another object. For example, an object that “directly” acts upon or touches another object does so without intervention from an intermediary. Contrarily, an object that “indirectly” acts upon or touches another object does so through an intermediary (e.g., a third object).
As used herein, the terms “processor” and “computer” and related terms, e.g., “processing device”, “computing device”, and “controller” are not limited to just those integrated circuits referred to in the art as a computer, but broadly refer to a microcontroller, a microcomputer, a programmable logic controller (PLC), an application specific integrated circuit (ASIC), and other programmable circuits, and these terms are used interchangeably herein. In the embodiments described herein, memory may include, but is not limited to, a computer-readable medium, such as a random access memory (RAM), and a computer-readable non-volatile medium, such as flash memory. Alternatively, a floppy disk, a compact disc-read only memory (CD-ROM), a magneto-optical disk (MOD), and/or a digital versatile disc (DVD) may also be used. Also, in the embodiments described herein, additional input channels may be, but are not limited to, computer peripherals associated with an operator interface such as a mouse and a keyboard. Alternatively, other computer peripherals may also be used that may include, for example, but not be limited to, a scanner. Furthermore, in the exemplary embodiment, additional output channels may include, but not be limited to, an operator interface monitor.
As used herein, the term “non-transitory computer-readable media” is intended to be representative of any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information, such as, computer-readable instructions, data structures, program modules and sub-modules, or other data in any device. Therefore, the methods described herein may be encoded as executable instructions embodied in a tangible, non-transitory, computer readable medium, including, without limitation, a storage device and a memory device. Such instructions, when executed by a processor, cause the processor to perform at least a portion of the methods described herein. Moreover, as used herein, the term “non-transitory computer-readable media” includes all tangible, computer-readable media, including, without limitation, non-transitory computer storage devices, including, without limitation, volatile and nonvolatile media, and removable and non-removable media such as a firmware, physical and virtual storage, CD-ROMs, DVDs, and any other digital source such as a network or the Internet, as well as yet to be developed digital means, with the sole exception being a transitory, propagating signal.
Exemplary systems and methods relating to SDRVs that interact with customers to identify customer needs, then schedule tasks and/or perform tasks to meet the customer needs can be seen in
Further, optional step 412 includes determining that a value of the first task has reached a threshold and calculating an expected time when the value of the first task will fall below the threshold, based on scheduled tasks within a task queue. In a restaurant environment, step 412 may be implemented, for example, as determining that all dining tables are occupied and no further customers may be seated (i.e., a threshold for the first task has been reached), and calculating the wait time for any table, or a table of a particular size, to become available based on the scheduled tasks within the task queue. In this way, accurate wait times may be provided to customers on a waiting list.
More specific non-limiting information relating to particular calculations and individual software modules is provided below.
Task Forecasting
An initial stage for a specific store/location is defined in the software structure. Using public data or machine learning data from historical tasks, a prediction traffic curve is generated at the beginning of a business cycle.
From a mathematical perspective, the arrival of a customer at a business, such as a restaurant, is a typical Poisson process where:
Thus, we can adopt the Poisson formula of Equation (1) to forecast the probability of a customer arriving at a business within a certain time period:
In Eq. (1), P is the probability, N is the forecast function, t is time, and n is customer quantity. For example, in 1 hour the probability of 3 customers arriving is described as P(N(1))=3. λ is the frequency of one event. In a restaurant model, 1/λ can stand for an appropriate time interval for escorting customers to a table.
The probability that two customers arrive within a time interval, T, that is less than or equal to t, can be described as shown in Equation (2).
P(t≤T)=1−e−λt Eq. (2)
Methods disclosed herein calculate these two probabilities to determine a time interval for a robot/SDRV to perform a given task (e.g., return to the host station to escort customers to tables in a restaurant). In this way, a robot will perform the given task (e.g., hosting/escorting) and avoid waiting idle for the given task to be required, as such time can be assigned to other tasks (e.g., food delivery or table cruising to identify customer needs). Thus, business place operation efficiency can be kept at a high level and utilization of an SDRV can be maximized.
As an example, a restaurant's visit data on a certain day of the week is shown in
In a multi tasking system, a processor will calculate time intervals for two or more tasks (e.g., hosting, delivery, cruising, payment, bussing, etc.). In an embodiment, a sequence of tasks will also be taken into consideration. For example, an escorting task will typically be followed by at least one delivery task after a cooking time.
In an embodiment, all tasks generated by the system will be written to a task queue. In an embodiment, after completing its last task, an SDRV will access the task queue and retrieve a task. In another embodiment, a centralized controller will monitor the status of all SDRVs, and schedule tasks for each SDRV.
In an embodiment, a person with access to the system can manually insert, edit, and/or delete tasks. For example, a person with access to the system may be an employee, who is optionally authenticated to the system (e.g., via password, biometrics, proximity, or otherwise). Access to the system may be via a wired connection (e.g., central computer or intranet), or a wireless connection, such as a tablet or mobile device communicating with a centralized or distributed database via the Internet). The tablet or mobile device/client device may utilize an application downloaded (e.g., from the Internet) onto the device configured with an operating system. The APP may be pre-programmed for a particular business or may be set-up to identify customer locations, such as table locations and table numbers, by a user of the device. In an embodiment, the APP may provide notice about SDRV movements.
All references cited throughout this application, for example patent documents including issued or granted patents or equivalents; patent application publications; and non-patent literature documents or other source material; are hereby incorporated by reference herein in their entireties, as though individually incorporated by reference.
The terms and expressions which have been employed herein are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the invention has been specifically disclosed by preferred embodiments, exemplary embodiments and optional features, modification and variation of the concepts herein disclosed can be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims. The specific embodiments provided herein are examples of useful embodiments of the invention and it will be apparent to one skilled in the art that the invention can be carried out using a large number of variations of the devices, device components, and method steps set forth in the present description. As will be apparent to one of skill in the art, methods and devices useful for the present methods and devices can include a large number of optional composition and processing elements and steps.
When a group of substituents is disclosed herein, it is understood that all individual members of that group and all subgroups are disclosed separately. When a Markush group or other grouping is used herein, all individual members of the group and all combinations and subcombinations possible of the group are intended to be individually included in the disclosure.
It must be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to “a processor” includes a plurality of such processors and equivalents thereof known to those skilled in the art, and so forth. As well, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising”, “including”, and “having” can be used interchangeably. The expression “of any of claims XX-YY” (wherein XX and YY refer to claim numbers) is intended to provide a multiple dependent claim in the alternative form, and in some embodiments is interchangeable with the expression “as in any one of claims XX-YY.”
Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention.
Whenever a range is given in the specification, for example, a range of integers, a temperature range, a time range, a composition range, or concentration range, all intermediate ranges and subranges, as well as all individual values included in the ranges given are intended to be included in the disclosure. As used herein, ranges specifically include the values provided as endpoint values of the range. As used herein, ranges specifically include all the integer values of the range. For example, a range of 1 to 100 specifically includes the end point values of 1 and 100. It will be understood that any subranges or individual values in a range or subrange that are included in the description herein can be excluded from the claims herein.
As used herein, “comprising” is synonymous and can be used interchangeably with “including,” “containing,” or “characterized by,” and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. As used herein, “consisting of” excludes any element, step, or ingredient not specified in the claim element. As used herein, “consisting essentially of” does not exclude materials or steps that do not materially affect the basic and novel characteristics of the claim. In each instance herein any of the terms “comprising”, “consisting essentially of” and “consisting of” can be replaced with either of the other two terms. The invention illustratively described herein suitably can be practiced in the absence of any element or elements or limitation or limitations which is/are not specifically disclosed herein.
All art-known functional equivalents of materials and methods are intended to be included in this disclosure. The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the concepts herein disclosed can be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17710286 | Mar 2022 | US |
| Child | 17856526 | US |
