Warehouse checking system based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle checking, in particular to a warehouse checking system based on an unmanned aerial vehicle, which is characterized by comprising an unmanned aerial vehicle subsystem, an image recognition subsystem, a two-dimensional code checking subsystem, a path planning subsystem and an unmanned aerial vehicle positioning subsystem, and the functions of the modules are as follows: the unmanned aerial vehicle subsystem controls the flight of the unmanned aerial vehicle; the two-dimensional code checking subsystem scans the two-dimensional code of the cargo box to obtain cargo checking information; the image recognition subsystem is used for acquiring warehouse images and recognizing and positioning the cargo boxes and the positions of the two-dimensional codes on the cargo boxes; the path planning subsystem is used for acquiring a warehouse map and position distribution of containers in a warehouse and planning a route of the unmanned aerial vehicle; and the unmanned aerial vehicle positioning subsystem is used for positioning the position of the unmanned aerial vehicle in the warehouse. According to the invention, path planning is carried out on the unmanned aerial vehicle, accurate positioning is carried out on the unmanned aerial vehicle, the optimal checking path of the unmanned aerial vehicle is ensured, repeated checking and cargo omission are avoided, and the checking efficiency and the checking precision are improved.

Description

Technical Field

[0001] The present application belongs to the field of drone inventory technology, and specifically relates to a drone-based warehouse inventory system. Background Art

[0002] Introducing drones into warehouse inventory to improve inventory efficiency has become a trend in the inventory field. However, existing drone inventory solutions have problems such as fuzzy drone positioning and lack of path planning, resulting in repeated inventory or missed goods, reducing inventory speed and accuracy. Summary of the Invention

[0003] The present application provides a warehouse inventory system based on drones to solve or partially solve the problems raised in the above background technology.

[0004] This application provides a drone-based warehouse inventory system, which includes: a drone subsystem, an image recognition subsystem, a QR code inventory subsystem, a path planning subsystem, and a drone positioning subsystem. The functions of each module are as follows:

[0005] UAV subsystem, controls the flight of the UAV;

[0006] QR code inventory subsystem, scans the QR code of the cargo box to obtain cargo inventory information;

[0007] Image recognition subsystem, which acquires warehouse images, identifies and locates the cargo boxes and the QR codes on them;

[0008] The path planning subsystem obtains the warehouse map and the location distribution of cargo boxes in the warehouse, and plans the drone route;

[0009] The drone positioning subsystem locates the drone's position in the cargo hold.

[0010] Preferably, the QR code inventory subsystem includes a QR code scanning device provided on the drone;

[0011] The image recognition subsystem includes an image recognition camera provided on the drone;

[0012] The UAV positioning subsystem includes a UWB positioning module installed on the UAV and a UWB positioning base station deployed in the cargo hold;

[0013] The path planning subsystem is a path planning model deployed in the drone control module.

[0014] Preferably, the cargo inventory information at least includes the type and quantity of cargo in the cargo box.

[0015] Preferably, the UAV positioning subsystem uses the UWB positioning module to locate the arrival time difference of different base stations.

[0016] Preferably, the path planning subsystem plans the flight path of the drone in the cargo hold by using an ant colony algorithm, and the specific steps are as follows:

[0017] Step 1: Environment modeling and initialization: decompose the task area into cubic grids and assign attributes to each grid;

[0018] Step 2: Initialize the ant colony parameters, set the number of ants, number of iterations, pheromone volatility factor ρ, define the heuristic function weight α, pheromone weight β, and path cost coefficient Q;

[0019] Step 3: Set up the ant colony movement strategy. Each ant starts from the starting point and moves to the next node according to the transfer probability:

[0020]

[0021] Where ηij = 1 / dij is the heuristic factor, dij is the node spacing;

[0022] Step 4: Dynamic update of pheromones, local update after each ant movement

[0023] τ ij (t+1)=(1-ρ)·τ ij (t)+ρ·τ0

[0024] Among them, the initial pheromone concentration τ0 = 0.1;

[0025] Step 5: Global optimal path optimization. After each round of iteration, the optimal path is strengthened.

[0026]

[0027] Among them, L best is the current optimal path length;

[0028] Step 6: Iteration termination judgment: terminate early when the maximum number of iterations Tmax is reached or the optimal path length change rate is less than 1% for 10 consecutive rounds.

[0029] Compared with the prior art, this application has the following beneficial effects:

[0030] This application plans the path of the drone through the path planning subsystem and accurately positions the drone through the drone positioning subsystem, ensuring the optimal route for drone inventory, avoiding repeated inventory and missed goods, and improving inventory efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present application is further described below with reference to the accompanying drawings and examples.

[0032] Figure 1 This is a schematic diagram of the system composition of this application. DETAILED DESCRIPTION

[0033] For example, certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. This specification and claims do not use differences in names as a way to distinguish components, but use differences in the functions of the components as the criteria for distinction. For example, "including" mentioned throughout the specification and claims is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect.

[0034] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "horizontal", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only used to facilitate the description of the present application and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limiting the present application.

[0035] In this application, unless otherwise specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they can refer to fixed connection, detachable connection, or integral connection; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0036] Example 1

[0037] like Figure 1 As shown, this application provides a warehouse inventory system based on drones, which is characterized by including: drone subsystem, image recognition subsystem, QR code inventory subsystem, path planning subsystem, drone positioning subsystem, and the functions of each module are as follows:

[0038] UAV subsystem, controls the flight of the UAV;

[0039] QR code inventory subsystem, scans the QR code of the cargo box to obtain cargo inventory information;

[0040] Image recognition subsystem, which acquires warehouse images, identifies and locates the cargo boxes and the QR codes on them;

[0041] The path planning subsystem obtains the warehouse map and the location distribution of cargo boxes in the warehouse, and plans the drone route;

[0042] The drone positioning subsystem locates the drone's position in the cargo hold.

[0043] Specifically, the QR code inventory subsystem includes a QR code scanning device provided on the drone;

[0044] The image recognition subsystem includes an image recognition camera provided on the drone;

[0045] The UAV positioning subsystem includes a UWB positioning module installed on the UAV and a UWB positioning base station deployed in the cargo hold;

[0046] The path planning subsystem is a path planning model deployed in the drone control module.

[0047] Specifically, the cargo inventory information at least includes the type and quantity of cargo in the cargo box.

[0048] Specifically, the path planning subsystem plans the flight path of the drone in the cargo hold using an ant colony algorithm. The specific steps are as follows:

[0049] Step 1: Environment modeling and initialization: decompose the task area into cubic grids and assign attributes to each grid;

[0050] Step 2: Initialize the ant colony parameters, set the number of ants, number of iterations, pheromone volatility factor ρ, define the heuristic function weight α, pheromone weight β, and path cost coefficient Q;

[0051] Step 3: Set up the ant colony movement strategy. Each ant starts from the starting point and moves to the next node according to the transfer probability:

[0052]

[0053] Where ηij = 1 / dij is the heuristic factor, dij is the node spacing;

[0054] Step 4: Dynamic update of pheromones, local update after each ant movement

[0055] τ ij (t+1)=(1-ρ)·τ ij (t)+ρ·τ0

[0056] Among them, the initial pheromone concentration τ0 = 0.1;

[0057] Step 5: Global optimal path optimization. After each round of iteration, the optimal path is strengthened.

[0058]

[0059] Among them, L best is the current optimal path length;

[0060] Step 6: Iteration termination judgment: terminate early when the maximum number of iterations Tmax is reached or the optimal path length change rate is less than 1% for 10 consecutive rounds.

[0061] Specifically, the UAV positioning subsystem uses the UWB positioning module to locate the arrival time difference of different base stations.

[0062] The above describes the implementation methods of the present application in detail in conjunction with the accompanying drawings, but the present application is not limited to the above implementation methods. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the relevant technical field without departing from the purpose of the present application.

Claims

1. The warehouse inventory system based on drones is characterized by: include: The functions of the drone subsystem, image recognition subsystem, QR code inventory subsystem, path planning subsystem, and drone positioning subsystem are as follows: UAV subsystem, controls the flight of the UAV; QR code inventory subsystem, scans the QR code of the cargo box to obtain cargo inventory information; Image recognition subsystem, which acquires warehouse images, identifies and locates the cargo boxes and the QR codes on them; The path planning subsystem obtains the warehouse map and the location distribution of cargo boxes in the warehouse, and plans the drone route; The drone positioning subsystem locates the drone's position in the cargo hold.

2. The drone-based warehouse inventory system according to claim 1, characterized in that: The QR code inventory subsystem includes a QR code scanning device provided on the drone; The image recognition subsystem includes an image recognition camera provided on the drone; The UAV positioning subsystem includes a UWB positioning module installed on the UAV and a UWB positioning base station deployed in the cargo hold; The path planning subsystem is a path planning model deployed in the drone control module.

3. The drone-based warehouse inventory system according to claim 2, characterized in that: The cargo inventory information at least includes the type and quantity of cargo in the cargo box.

4. The drone-based warehouse inventory system according to claim 2, characterized in that: The UAV positioning subsystem uses the UWB positioning module to locate the arrival time difference of different base stations.

5. The drone-based warehouse inventory system according to claim 2, characterized in that: The path planning subsystem plans the flight path of the drone in the cargo hold using the ant colony algorithm. The specific steps are as follows: Step 1: Environment modeling and initialization: decompose the task area into cubic grids and assign attributes to each grid; Step 2: Initialize the ant colony parameters, set the number of ants, number of iterations, pheromone volatility factor ρ, define the heuristic function weight α, pheromone weight β, and path cost coefficient Q; Step 3: Set up the ant colony movement strategy. Each ant starts from the starting point and moves to the next node according to the transfer probability: Where ηij = 1 / dij is the heuristic factor, dij is the node spacing; Step 4: Dynamic update of pheromones, local update after each ant movement t ij (t+1)=(1-ρ)·τ ij (t)+ρ·τ0 Among them, the initial pheromone concentration τ0 = 0.1; Step 5: Global optimal path optimization. After each round of iteration, the optimal path is strengthened. Among them, L best is the current optimal path length; Step 6: Iteration termination judgment: terminate early when the maximum number of iterations Tmax is reached or the optimal path length change rate is less than 1% for 10 consecutive rounds.

Images