METHODS AND SYSTEMS FOR VEHICLE AND DRONE BASED DELIVERY SYSTEM

Embodiments for delivering goods to customers by a processor are described. A plurality of goods to be loaded onto a delivery vehicle are selected based on customer-associated information. A delivery route for the delivery vehicle is determined based on the customer-associated information. A customer order for at least one of the selected plurality of goods is received. The at least one of the selected plurality of goods is caused to be delivered from the delivery vehicle on the delivery route to the customer using a drone.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to computing systems, and more particularly, to various embodiments for delivering products to customers using vehicle and drone based delivery systems.

Description of the Related Art

One of the problems expected with the practical implementation of drone (e.g., unmanned aerial vehicle (UAV)) based delivery systems is that the current state of the art drones have limited carrying (or payload) capacity. If multiple products are to be delivered, depending on the size and weight of the products, the drone may have to travel between the warehouse and delivery point several times, or alternatively, multiple drones may have to be used for a single delivery. This problem will most likely be exacerbated by the relatively limited range of the drones.

These problems are expected to increase as drone based delivery systems become more commonly used. As such, there will be a need to reduce delivery times and otherwise increase the efficiency of such delivery systems.

SUMMARY OF THE INVENTION

Various embodiments for delivering goods to customers by a processor are described. In one embodiment, by way of example only, a method for delivering goods to customers, again by a processor, is provided. A plurality of goods to be loaded onto a delivery vehicle are selected based on customer-associated information. A delivery route for the delivery vehicle is determined based on the customer-associated information. A customer order for at least one of the selected plurality of goods is received. The at least one of the selected plurality of goods is caused to be delivered from the delivery vehicle on the delivery route to the customer using a drone.

DETAILED DESCRIPTION OF THE DRAWINGS

As previously indicated, as the use of drone based delivery systems increases, the limited carrying capacity and range of the drones is likely to result in undesirable delivery delays and other inefficiencies. This will most likely be a particular problem when customers order multiple products, as it may result in the drone having to travel multiple times between the warehouse and the delivery point and/or the use of multiple drones for a single order. Although delivery delays may be acceptable for larger, more expensive goods, ideally customers will be able to order, for example, smaller, daily necessities and have them delivered on the same day.

For example, in the event a customer breaks his or her sunglasses, which he/she is accustomed to wearing when driving an automobile, ideally he or she would be able to order a new pair and have them delivered directly to their residence within, for example, an hour, to reduce the likelihood that they would have to drive without sunglasses, which may particularly be an issue on bright, sunny days. As another example, in the case of a single parent who requires a necessity for an infant, such as diapers, it would be extremely helpful to the parent if he or she could order diapers and have them delivered directly to their residence in a timely manner, as opposed to them having to leave the house with their infant. Although such delivery systems are anticipated, the overall delivery time and efficiency of the systems seems dubious when a single customer orders both items because, as described above, multiple trips by the drone and/or multiple drones may be required.

In view of the foregoing, a need exists for drone based delivery systems in which overall efficiency is optimized, thereby reducing delivery times, particularly in the cases of multiple-product orders.

To address these needs, the methods and systems of the present invention use, for example, various information about customers in a particular geographic region to select what goods (or products) to load onto one or more delivery vehicles (e.g., ground vehicles, such as driverless trucks) that will be deployed in that geographic region. In other words, goods are loaded onto the delivery vehicle(s) in a particular region based on predicted (or estimated or anticipated) customer orders in that region. In one example, also based on the information about the customers, perhaps in combination with orders that have already been placed, a delivery route for the delivery vehicle(s) is determined in such a way to reduce overall delivery times for the anticipated orders, perhaps as well as the goods associated with previously placed orders, when the goods are delivered to the customers from the delivery vehicle(s) using drones (e.g., unmanned aerial vehicles (UAVs)).

In one example, after an initial delivery route for the delivery vehicle(s) is determined, in response to receiving one or more customer orders for the goods loaded on the delivery vehicle(s), the delivery route(s) is altered to further maximize delivery efficiency (e.g., reduce delivery times as much as possible).

Depending on the goods ordered, as well as their current locations (i.e., the location(s) of the delivery vehicle(s) on which the goods are stored), a single drone may retrieve one product from one delivery vehicle, and then travel to other delivery vehicles to retrieve other products, before making the delivery at the delivery point (e.g., the customer's shipping address). In some embodiments, the drones are stored on the delivery vehicles when not in use, but it is also contemplated that the drones may be stored at other locations, such as the customers' residences (i.e., the drone used to deliver the order may belong to the customer).

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

In the context of the present invention, and as one of skill in the art will appreciate, various components depicted inFIG. 1may be located in, for example, personal computer systems, hand-held or laptop devices, and network PCs. However, in some embodiments, some of the components depicted inFIG. 1may be located in a delivery vehicle (e.g., a driverless ground vehicle) and/or a drone (e.g., UAV). For example, some of the processing and data storage capabilities associated with mechanisms of the illustrated embodiments may take place locally via local processing components, while the same components are connected via a network to remotely located, distributed computing data processing and storage components to accomplish various purposes of the present invention. Again, as will be appreciated by one of ordinary skill in the art, the present illustration is intended to convey only a subset of what may be an entire connected network of distributed computing components that accomplish various inventive aspects collectively.

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and, in the context of the illustrated embodiments of the present invention, various route determination workloads and functions96for determining initial delivery routes of both delivery vehicles and drones, as well as altering the routes in real-time in response to receiving customer orders. One of ordinary skill in the art will appreciate that the image processing workloads and functions96may also work in conjunction with other portions of the various abstractions layers, such as those in hardware and software60, virtualization70, management80, and other workloads90(such as data analytics processing94, for example) to accomplish the various purposes of the illustrated embodiments of the present invention.

As previously mentioned, the methods and systems of the illustrated embodiments provide novel approaches for delivering goods to customers. The methods and systems include a data collection aspect, where a variety of information (i.e., customer-associated information) may be collected about customers (and/or potential customers) for goods in, for example, a particular geographic region. The information may include and/or be based on, for example, the internet browsing data and social media interaction associated with the customers, as well as the customers' age, gender, purchase history, spending habits, and participation in a buying program/club. Additionally, the information may include, for example, the current date/season and weather data (e.g., temperature, chance of participation, etc.).

Based on the collected data, a delivery vehicle (or mobile warehouse) is loaded with a plurality of selected goods that are considered likely to be purchased by customers in the geographic region in which the delivery vehicle is deployed. In one example, the customer-associated information is also used to determine an initial route that is used by the delivery vehicle. The initial delivery route may be chosen in such a way to maximize the efficiency of the delivery of goods of estimated orders. In one example, the initial route is also based on orders that have already been placed by customers in that particular region. It should be understood that the initial route may include simply positioning the delivery vehicle at a particular location in the region, as opposed to having the delivery vehicle in constant or near constant motion along a specified route in the region. Additionally, other information, such as road information (e.g., maps) and traffic information (e.g., traffic events, alerts, and the like), may be used further optimize the delivery route.

Referring toFIG. 4, a map100of a particular geographic region, having multiple roads/roadways, is shown. A delivery route (e.g., an initial delivery route)102is indicated on the map100as following selected roads in what is essentially a loop around the region. As described above, the delivery route102may be determined based on various information about customers (or potential customers), as well as information about the roads/traffic and orders that have already been received and processed. Although only one delivery route102is shown, it should be understood that multiple delivery routes may be simultaneously used within a particular region (i.e., multiple delivery vehicles may be simultaneously deployed and dispersed within the region).

When a customer order is received (and/or processed), one or more drones are used to transport the ordered goods from the delivery vehicle to the customer (e.g., the customer's shipping address). The drone(s) may be stored on the delivery vehicle when not in use. In this regard, although not shown, the delivery vehicle may have storage bays for the drone and/or landing areas for the drones. The delivery vehicles may also be equipped with conveyer belt-like systems and/or other automated mechanisms to transfer the ordered products to a location suitable to be retrieved by the drones (e.g., the landing areas), perhaps in a particular order (e.g., based on the order in which the orders were placed/received and/or the order in which the products will be retrieved by the drones) so that the products are ready to be picked up by the drones without any delay.

In one example, the drone is stored at the customer's shipping address (e.g., the drone is owned by the customer) and is deployed from the customer's address, retrieves the ordered goods from the delivery vehicle, and returns to the customer's address. Also, for multiple product orders in which the ordered products are stored on different delivery vehicles, a drone may retrieve one product from one delivery vehicle, carry it to another delivery truck, retrieve a second (or third, etc,) product from another delivery vehicle, and then deliver multiple products to the delivery point at once. Further, in situations in which the ordered product(s) is relatively far from the delivery point, a drone may retrieve a product from one delivery truck and carry it to another delivery truck, where is it retrieved by another drone to be taken to the delivery point.

In one example, if the drone encounters a technical issue while attempting to deliver a product, the drone may return to the delivery vehicle from which the product was picked up (or another delivery vehicle), and the system will schedule a new delivery with another drone. Additionally, the drones may be used to transfer products from one delivery vehicle to another based on dynamic need (e.g., predicted inventory and/or purchasing patterns that suggest particular products are popular in particular areas). In this manner, inventory may be moved between delivery vehicles based on predicted future orders.

In one example, when customer orders are received after the delivery vehicle has already been deployed (or at least after the initial delivery route is determined), the delivery route is altered in order to reduce delivery times and/or otherwise optimize delivery efficiency. For example,FIG. 5shows the same map as was shown inFIG. 4but with an altered delivery route104(i.e., altered from the initial delivery route102shown inFIG. 4). In the examples shown inFIG. 4andFIG. 5, the changes to the delivery route may be made in response to, for example, customer orders with delivery points (e.g., shipping addresses) in the upper, left and lower, right portions of the region shown in the map100. It should be understood that the changes to the delivery route may be made in “real-time” in response customer orders that are received. As such, the delivery route may be updated (or altered) repeatedly and/or continuously while the delivery vehicle is deployed.

FIG. 6shows a graphical illustration of a method that, in one example, is used to determine the delivery route(s) (e.g., the initial delivery route and/or the altered delivery route) of the delivery vehicle(s). In one example, a “least squares fitting” method is used to determine the delivery route(s), as will be appreciated by one skilled in the art. As depicted inFIG. 6, each dot106may be considered to represent a customer order (e.g., a predicted customer order or a previously received customer order), or more particularly, represents the shipping address, or delivery point, associated with a customer order. Line108may be considered to represent the “least squares fitting” line generated based on the customer orders. Line110may be considered to represent the delivery route that is determined to be the most effective with respect to overall delivery time, efficiency, etc. As such, line108may be considered to represent the “ideal” delivery route based on the customer orders (i.e., predicted and/or received) at any given time. However, because of various factors, such as the layout of roads, traffic conditions, etc., the actual delivery route (line110) may not perfectly follow the ideal delivery route (line108). Again, it should be understood that the delivery route (line110) may be dynamically adjusted in real-time in response to updates made to the customer orders (dots106).

FIG. 7illustrates an example of a map interface screen112that may be displayed to a user (e.g., a potential customer) via, for example, a cellular telephone, a desktop computer, or laptop computer in order to facilitate customer orders. In the depicted embodiment, the map interface screen112includes a map114(e.g., similar to map100shown inFIG. 4andFIG. 5), a shopping cart window116, and an order button118. As inFIG. 4andFIG. 5, the map114represents a particular geographic area, such as the region in which the user lives. In the depicted embodiment, on the map114are displayed delivery vehicle icons120that represent, for example, the real-time locations of delivery vehicles in the region represented by the map114. In one example, drone icons122are also displayed on the map114, which likewise represent the real-time locations of the drones in the region.

In one example, the user may select one of the delivery vehicles by “clicking” or “mousing over” one of the delivery vehicle icons120, and in response, an available product window124is displayed (e.g., as a pop-window) on the map114near the respective delivery vehicle icon120. In the available product window124, products (or icons representative of those products and/or an alphanumeric list of those products)126are displayed, which are available for sale and loaded on that particular delivery vehicle. In some embodiments, the user may “drag and drop” products126from the available product window124into the shopping cart window116, which will then appear in the shopping cart window116as selected products128(e.g., by displaying products icons and/or an alphanumeric list of the products in the shopping cart window116). It should be understood that different delivery vehicles in the region may be loaded with different products/goods. As such, the user may similarly select the other delivery vehicle icons120on the map114for additional products they wish to order.

When the user has all of the desired products in the shopping cart window116, the user may click or select the order button118. In some embodiments, additional steps may be required to complete the order (e.g., arranging for payment, verifying shipping address, etc.). After the user places the order, and the order is received and processed, the ordered goods maybe delivered to the customer as described above (e.g., transported from the delivery vehicle to the delivery point via a drone).

In one example, after placing the order, or perhaps before the order is placed with one or more selected products in the shopping cart window116, the user is provided with an indication of recommended (or suggested) products that, for example, are available on the same delivery vehicle from which their current order will originate. The suggested products may be based in part on, for example, the size and weight of the product(s) already ordered and payload space and capacity of the drone that will deliver the product(s). Additionally, the suggested products may be based on information associated with that particular customer (e.g., previous orders, browsing history, etc.) and/or other information (e.g., weather conditions, time of day, date, etc.). Although not shown, the indication of the suggested products may be provided via, for example, a pop-up window on the map interface screen112(e.g., similar to the available product window124), a subsequent screen (e.g., during arrangement for payment), a personal message (e.g., email or text message), etc.

Turning toFIG. 8, a flowchart diagram of an exemplary method800for delivering goods to customers, in accordance with various aspects of the present invention, is illustrated. Method800begins (step802) with the selection of a plurality of goods to be loaded onto a delivery vehicle(s) in the manner(s) described above, such as based on customer-associated information (step804). A delivery route (e.g., an initial delivery route) for the delivery vehicle(s) is then determined as described above, such as based on customer-associated information and/or the selected goods (step806). As described above, the selected goods and the (initial) delivery route may also be based on customer orders that have already been placed.

Still referring toFIG. 8, a customer order for one or more of the selected goods loaded on the delivery vehicle(s) is then received, perhaps after the delivery vehicle(s) have been deployed (step808). The delivery route is then altered based on the customer order (step810). The goods associated with the customer order are then delivered to the customer using, for example, drones, as described above (step812). If additional customer orders are received, method800returns to step810where the delivery route is (again) altered based on the newly received customer orders (step814). If no additional customer orders are received, method800ends (step816), with, for example, the delivery truck continuing on the current delivery route and/or returning to the warehouse or station from which it originated.

It should be understood that in some embodiments the methods for delivering goods to customers described herein may not include all of the steps depicted inFIG. 8. As such, referring toFIG. 9, a flowchart diagram of an exemplary method900, having fewer steps than is depicted inFIG. 8, for delivering goods to customers, in accordance with various aspects of the present invention, is illustrated. Method900begins (step902) with the selection of a plurality of goods to be loaded onto a delivery vehicle(s) in the manner(s) described above, such as based on customer-associated information (step904). A delivery route for the delivery vehicle(s) is then determined as described above, such as based on customer-associated information and/or the selected goods (step906). As described above, the selected goods and the (initial) delivery route may also be based on customer orders that have already been placed. A customer order for one or more of the selected goods loaded on the delivery vehicle(s) is then received, perhaps after the delivery vehicle(s) have been deployed (step908). The goods associated with the customer order are then delivered to the customer using, for example, drones, as described above (step910). Method900ends (step912), with, for example, the delivery truck continuing on the current delivery route and/or returning to the warehouse or station from which it originated.