Unmanned aerial vehicle ad hoc network method and system

Abstract

The invention discloses an unmanned aerial vehicle ad hoc network method and system, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: obtaining a target inspection region; determining the unmanned aerial vehicle in the target inspection area as a to-be-networked unmanned aerial vehicle; acquiring the inspection type, the residual electric quantity and the target task location distance of the unmanned aerial vehicle to be networked; obtaining the weight of each to-be-networked unmanned aerial vehicle according to the inspection type, the remaining power and the target task location distance of the to-be-networked unmanned aerial vehicle; and triggering the to-be-networked unmanned aerial vehicles to perform unmanned aerial vehicle ad hoc networking according to the weight of each to-be-networked According to the invention, the weight of each to-be-networked unmanned aerial vehicle is obtained through the inspection type, the remaining power and the target task location distance of the to-be-networked unmanned aerial vehicle, and the to-be-networked unmanned aerial vehicle is triggered to carry out unmanned aerial vehicle ad hoc networking according to the weight of each to-be-networked unmanned aerial vehicle. The relationship between the residual electric quantity of the unmanned aerial vehicle to be networked and the type of the task to be executed can be considered during networking, and the stability of the unmanned aerial vehicle ad hoc network can be ensured to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and particularly to a method and system for self-organizing network of unmanned aerial vehicles. Background Art

[0002] With the gradual development of unmanned aerial vehicles and the increasing complexity of the usage environment, people have higher and higher requirements for unmanned aerial vehicles. When performing tasks, a single unmanned aerial vehicle is already difficult to meet the task requirements, and it is an inevitable trend to perform tasks through the cooperation of unmanned aerial vehicle clusters.

[0003] Currently, most unmanned aerial vehicle clusters adopt self-organizing network technology. Unmanned aerial vehicles are resource-constrained terminals, and the clustering structure can effectively improve network performance. However, in the existing technology, once the battery power of the cluster head is too low to complete the task, it is easy to make the self-organizing network of unmanned aerial vehicles unstable, thus affecting the overall performance of the self-organizing network of unmanned aerial vehicles. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that in the target inspection area, multiple unmanned aerial vehicles need to form a network efficiently and stably to cooperate to complete tasks. Traditional methods may not fully consider the relationship between the state of unmanned aerial vehicles (such as remaining battery power) and task requirements (such as inspection type and distance to the target task location), resulting in low network formation efficiency or network instability.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for self-organizing network of unmanned aerial vehicles, which includes the following steps:

[0008] Obtain the target inspection area; judge the position of the unmanned aerial vehicle to obtain the current position information of the unmanned aerial vehicle, and obtain the unmanned aerial vehicles to be networked; obtain the inspection type, remaining battery power and distance to the target task location of the unmanned aerial vehicles to be networked; obtain the weight of each unmanned aerial vehicle to be networked according to the inspection type, remaining battery power and distance to the target task location of the unmanned aerial vehicles to be networked; trigger the unmanned aerial vehicles to be networked to perform self-organizing network of unmanned aerial vehicles according to the weight of each unmanned aerial vehicle to be networked.

[0009] As a preferred scheme of a method for self-organizing network of unmanned aerial vehicles according to the present invention, wherein: the current position information of the unmanned aerial vehicle includes:

[0010] Position 1, where Position 1 means that the unmanned aerial vehicle is within the target inspection area.

[0011] Position 2, where Position 2 means that the unmanned aerial vehicle is outside the target inspection area.

[0012] As a preferred solution of a method for self-organizing a network of unmanned aerial vehicles according to the present invention, among them: the unmanned aerial vehicle at position one is determined as the unmanned aerial vehicle to be networked.

[0013] The unmanned aerial vehicle at position two is determined as the unmanned aerial vehicle not to be networked.

[0014] The obtaining of the inspection type, remaining power, and distance to the target task location of the unmanned aerial vehicle to be networked includes sending a type request instruction to the unmanned aerial vehicles in the target inspection area, triggering the unmanned aerial vehicle to be networked to feedback the inspection type, remaining power, current position coordinates, and target task location coordinates corresponding to the inspection type, and obtaining the distance from the unmanned aerial vehicle to be networked to the target task location according to the current position coordinates of the unmanned aerial vehicle to be networked and the target task location coordinates corresponding to the inspection type.

[0015] As a preferred solution of a method for self-organizing a network of unmanned aerial vehicles according to the present invention, among them: based on the current position information of the unmanned aerial vehicle, through a learning algorithm adapted to the current position information for calculation, position information data is obtained.

[0016] When the position information is position one, the distance between the unmanned aerial vehicle to be networked and the target task location is solved through a distance algorithm to obtain the distance from the unmanned aerial vehicle to be networked to the target task location.

[0017] The distance algorithm is expressed as

[0018]

[0019] Among them, (x1, y1) are the current position coordinates of the unmanned aerial vehicle to be networked, (x2, y2) are the target task location coordinates corresponding to the inspection type, x1 represents the abscissa of the current position of the unmanned aerial vehicle to be networked, y1 represents the ordinate of the current position of the unmanned aerial vehicle to be networked, x2 represents the abscissa of the target task location corresponding to the inspection type, and y2 represents the ordinate of the target task location corresponding to the inspection type.

[0020] As a preferred solution of a method for self-organizing a network of unmanned aerial vehicles according to the present invention, among them: based on the current position information of the unmanned aerial vehicle, when the position information is position two, the unmanned aerial vehicle not to be networked is not controlled.

[0021] As a preferred solution of a method for self-organizing a network of unmanned aerial vehicles according to the present invention, among them: the obtaining of the weight of each unmanned aerial vehicle to be networked includes obtaining a type weight according to the inspection type of each unmanned aerial vehicle to be networked, obtaining a power weight according to the remaining power of each unmanned aerial vehicle to be networked, obtaining a distance weight according to the distance from each unmanned aerial vehicle to be networked to the target task location, and obtaining the weight of each unmanned aerial vehicle to be networked according to the respective type weights, power weights, and distance weights.

[0022] The obtaining of the weight of each unmanned aerial vehicle to be networked is expressed as

[0023]

[0024] Among them, Q lei represents the type weight of the UAVs to be networked, and Q ta represents the power weight of the UAVs to be networked, and Q dis represents the distance weight of the UAVs to be networked.

[0025] As a preferred solution of the UAV ad-hoc networking method described in the present invention, among them: triggering the UAVs to be networked to perform UAV ad-hoc networking includes determining the UAV with the largest weight among the UAVs to be networked as the cluster head of the clustering, and taking the UAVs to be networked within the effective communication distance of the cluster head of the clustering as the intra-cluster members to perform ad-hoc networking with the cluster head of the clustering.

[0026] In a second aspect, the present invention provides a system for a UAV ad-hoc networking method, including:

[0027] An area acquisition module, a UAV screening module, an information acquisition module, a weight calculation module, and an ad-hoc networking trigger module.

[0028] The area acquisition module is responsible for acquiring information about the target inspection area and determining the geographical scope to be inspected.

[0029] The UAV screening module is responsible for screening out the UAVs located within the target inspection area according to the matching situation between the position coordinates of the UAVs and the target inspection area, and determining them as the UAVs to be networked.

[0030] The information acquisition module is responsible for acquiring the inspection type, remaining power, current position coordinates of the UAVs to be networked, and the coordinates of the target task location, and calculating the distance between the UAVs to be networked and the target task location.

[0031] The weight calculation module is responsible for calculating the weight of each UAV to be networked according to the inspection type, remaining power, and distance from the target task location of the UAV.

[0032] The ad-hoc networking trigger module is responsible for triggering the UAVs to perform ad-hoc networking according to the weight of each UAV to be networked.

[0033] In a third aspect, the present invention provides an electronic device, including:

[0034] A memory and a processor;

[0035] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the simulation method for the gas-liquid heat transfer mechanism of the water conveyance pipeline are implemented.

[0036] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the simulation method for the gas-liquid heat transfer mechanism of the water conveyance pipeline.

[0037] Advantages of the present invention: The present invention obtains the weight of each drone to be networked based on the inspection type, remaining battery power, and distance to the target task location of the drones to be networked, and triggers the drones to perform drone ad-hoc networking according to the weight of each drone to be networked, which can take into account the relationship between the remaining battery power of the drones to be networked and the type of tasks to be executed, and can ensure the stability of the drone ad-hoc network to a certain extent. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0039] Figure 1 It is the overall flowchart of a drone ad-hoc networking method provided by the first embodiment of the present invention.

[0040] Figure 2 It is the overall framework diagram of a drone ad-hoc networking system provided by the second embodiment of the present invention. Detailed Embodiments

[0041] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed embodiments of the present invention with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0042] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides a drone ad-hoc networking method, characterized in that:

[0043] S1: Obtain the target inspection area.

[0044] S2: Determine the position of the drone, obtain the current position information of the drone, and obtain the drones to be networked.

[0045] S3: Obtain the inspection type, remaining battery power, and distance to the target task location of the drones to be networked.

[0046] S4: Obtain the weight of each drone to be networked according to the inspection type, remaining power, and distance to the target task location of the drones to be networked.

[0047] S5: Trigger the drones to be networked to perform drone ad-hoc networking according to the weight of each drone to be networked.

[0048] Most drone swarms adopt ad-hoc networking technology. Drones are resource-constrained terminals, and the clustering structure can effectively improve network performance. However, in the existing technology, once the power of the cluster head is too low to complete the task, it is easy to make the drone ad-hoc network unstable, thus affecting the overall performance of the drone ad-hoc network.

[0049] Therefore, through the steps S1-S4 above, the present invention obtains the information of drones in the target inspection area, comprehensively considers the inspection type, remaining power, and distance to the target task location, calculates the weight of each drone, and triggers drone ad-hoc networking based on the weight. This method can effectively ensure the stability of the drone ad-hoc network and is applicable to inspection tasks in complex environments.

[0050] Example 2, refer to Figure 1 , which is an embodiment of the present invention, and provides a method for drone ad-hoc networking.

[0051] In the embodiment of the present application, the target inspection area in step S1 should contain multiple drones.

[0052] In an alternative embodiment, there may also be drones outside the target inspection area, but these drones do not participate in the ad-hoc networking of the current task.

[0053] In the embodiment of the present application, the current position information of the drone in step S2 includes:

[0054] Position 1, where Position 1 means the drone is within the target inspection area;

[0055] Specifically, obtain the position coordinates of each drone in the drone swarm; match the position coordinates of each drone with the coordinate range corresponding to the target inspection area to determine the drones located in the target inspection area; determine the drones located in the target inspection area as the drones to be networked.

[0056] Position 2, where Position 2 means the drone is outside the target inspection area.

[0057] Among them, when the position coordinates of the drone belong to the coordinate range corresponding to the target inspection area, it is determined that the drone is located within the target inspection area.

[0058] In the embodiment of the present application, obtaining the inspection type, remaining power, and distance to the target task location of the drones to be networked in step S3 includes:

[0059] Send a request instruction of the sending type to the drones in the target inspection area, triggering the drones to be networked to feedback the inspection type, remaining power, current position coordinates, and target task location coordinates corresponding to the inspection type. And based on the current position coordinates of the drones to be networked and the target task location coordinates corresponding to the inspection type, obtain the distance from the target task location of the drones to be networked.

[0060] In the embodiment of the present application, in S3, based on the current position information of the drone, calculate through a learning algorithm adapted to the current position information to obtain position information data.

[0061] When the position information is position one, solve the distance between the drone to be networked and the target task location through a distance algorithm to obtain the distance from the target task location of the drone to be networked.

[0062] The distance algorithm is expressed as

[0063]

[0064] where (x1, y1) are the current position coordinates of the drone to be networked, (x2, y2) are the target task location coordinates corresponding to the inspection type, x1 represents the abscissa of the current position of the drone to be networked, y1 represents the ordinate of the current position of the drone to be networked, x2 represents the abscissa of the target task location corresponding to the inspection type, and y2 represents the ordinate of the target task location corresponding to the inspection type.

[0065] Based on the current position information of the drone, when the position information is position two, do not control the drones not to be networked.

[0066] In the embodiment of the present application, obtaining the weight of each drone to be networked in step S4 includes: obtaining a type weight according to the inspection type of each drone to be networked, obtaining a power weight according to the remaining power of each drone to be networked, obtaining a distance weight according to the distance from the target task location of each drone to be networked, and obtaining the weight of each drone to be networked according to the type weight, power weight, and distance weight respectively.

[0067] Specifically, obtain a type weight according to the inspection type of each drone to be networked. Obtain a power weight according to the remaining power of each drone to be networked. Obtain a distance weight according to the distance from the target task location of each drone to be networked. Obtain the weight of each drone to be networked according to the type weight, power weight, and distance weight respectively. In this way, the relationship between the remaining power of the drones to be networked and the type of tasks to be executed can be considered during networking, and the stability of the self-organizing network of drones can be ensured to a certain extent.

[0068] Further, obtain the type weight according to the inspection type of each drone to be networked, including: using a preset type weight database, perform a look-up operation on each inspection type to obtain the type weight corresponding to each inspection type, so as to obtain the type weight corresponding to the inspection type of each drone to be networked. Among them, the type weight database stores the corresponding relationship between the inspection type and the type weight.

[0069] Further, obtain the power weight according to the remaining power of each drone to be networked, including: using a preset power weight database, perform a look-up operation on each remaining power to obtain the power weight corresponding to each remaining power, so as to obtain the power weight corresponding to the remaining power of each drone to be networked. Among them, the power weight database stores the corresponding relationship between the remaining power and the power weight.

[0070] Further, obtain the distance weight according to the distance of the target task location of each drone to be networked, including: using a preset distance weight database, perform a look-up operation on each distance of the target task location to obtain the distance weight corresponding to each distance of the target task location, so as to obtain the distance weight corresponding to the distance of the target task location of each drone to be networked. Among them, the distance weight database stores the corresponding relationship between the distance of the target task location and the distance weight.

[0071] Obtaining the weight of each drone to be networked is expressed as

[0072]

[0073] where Q lei represents the type weight of the drone to be networked, Q ta represents the power weight of the drone to be networked, Q dis represents the distance weight of the drone to be networked.

[0074] In an alternative embodiment, the calculation of the weight is not only based on the inspection type, remaining power, and distance of the target task location, but also introduces a dynamic priority adjustment mechanism:

[0075] Dynamically adjust the weight of each drone to be networked according to factors such as the urgency of the task, the historical task completion situation of the drone, and the current environmental conditions (such as wind speed, weather).

[0076] If the task is urgent, increase the weight of the drone with a higher remaining power; if the environmental conditions are harsh, increase the weight of the drone with stronger wind resistance.

[0077] In the embodiment of the present application, triggering the drones to be networked in step S5 includes determining the drone with the largest weight among the drones to be networked as the cluster head of clustering, and using the drones to be networked within the effective communication distance of the cluster head of clustering as intra-cluster members to perform self-networking with the cluster head of clustering.

[0078] In an alternative embodiment,

[0079] Trigger the drones to perform self-networking according to the task partition and weight:

[0080] Within each task partition, determine the drone with the largest weight among the drones to be networked as the cluster head of clustering for this partition.

[0081] Use the drones to be networked within the effective communication distance of the cluster head of clustering as intra-cluster members to perform self-networking with the cluster head of clustering.

[0082] If the number of drones in a certain task partition is insufficient, allocate drones from adjacent partitions to ensure the completion of the task.

[0083] Example 3, refer to Figure 2 , which is an embodiment of the present invention, provides a system for a method of self-networking of drones, characterized in that it includes an area acquisition module 100, a drone screening module 200, an information acquisition module 300, a weight calculation module 400, and a self-networking trigger module 500.

[0084] The area acquisition module 100 is responsible for acquiring information about the target inspection area and determining the geographical scope to be inspected.

[0085] The drone screening module 200 is responsible for screening out the drones located within the target inspection area according to the matching situation between the position coordinates of the drones and the target inspection area, and determining them as the drones to be networked.

[0086] The information acquisition module 300 is responsible for acquiring the inspection type, remaining battery power, current position coordinates of the drones to be networked, and the coordinates of the target task location, and calculating the distance between the drones to be networked and the target task location.

[0087] The weight calculation module 400 is responsible for calculating the weight of each drone to be networked according to the inspection type, remaining battery power of the drone, and the distance to the target task location.

[0088] The self-networking trigger module 500 is responsible for triggering the drones to perform self-networking according to the weight of each drone to be networked.

[0089] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0090] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0091] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0092] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for self-organizing a drone network, characterized in that: include: Get the target inspection area; Determine the position of the drone, obtain the current position information of the drone, and obtain the drone to be grouped; Obtain the inspection type, remaining battery power, and distance to the target mission location of the drone to be networked; Obtain the weight of each drone to be networked according to the inspection type, remaining power, and distance to the target mission location of the drone to be networked; The drones to be networked are triggered to self-organize the network according to the weight of each drone to be networked.

2. A drone self-organizing network method as claimed in claim 1, characterized in that: The current location information of the drone includes: Position 1, where the UAV is within the target inspection area; Position two: the UAV is outside the target inspection area.

3. A drone self-organizing network method as claimed in claim 2, characterized in that: The UAV in position one is determined as the UAV to be grouped; The UAV in position 2 is determined as a UAV that is not waiting to be grouped. The method of obtaining the inspection type, remaining power and target mission location distance of the drone to be networked includes sending a type request instruction to the drones in the target inspection area, triggering the drone to be networked to feedback the inspection type, remaining power, current position coordinates, and target mission location coordinates corresponding to the inspection type, and obtaining the target mission location distance of the drone to be networked based on the current position coordinates of the drone to be networked and the target mission location coordinates corresponding to the inspection type.

4. A drone self-organizing network method as claimed in claim 3, characterized in that: Based on the current position information of the drone, the position information data is obtained by calculating through a learning algorithm adapted to the current position information; When the location information is location 1, the distance between the drone to be networked and the target mission location is solved by the distance algorithm to obtain the target mission location distance of the drone to be networked; The distance algorithm is expressed as, Among them, (x1, y1) are the current position coordinates of the drone to be networked, (x2, y2) are the coordinates of the target task location corresponding to the inspection type, x1 represents the horizontal coordinate of the current position of the drone to be networked, y1 represents the vertical coordinate of the current position of the drone to be networked, x2 represents the horizontal coordinate of the target task location corresponding to the inspection type, and y2 represents the vertical coordinate of the target task location corresponding to the inspection type.

5. A drone self-organizing network method as claimed in claim 4, characterized in that: Based on the current position information of the drone, when the position information is position 2, the drones not in the waiting group are not controlled.

6. A drone self-organizing network method as claimed in claim 5, characterized in that: The weight of each drone to be networked is obtained Including, obtaining a type weight according to the inspection type of each drone to be networked, obtaining a power weight according to the remaining power of each drone to be networked, obtaining a distance weight according to the distance of the target mission location of each drone to be networked, and obtaining the weight of each drone to be networked according to each type weight, power weight and distance weight; The weight of each drone to be networked is expressed as: Among them, Q lei Indicates the type weight of the drone to be networked, Q ta Indicates the power weight of the drone to be networked, Q dis Indicates the distance weight of the drone to be networked.

7. A drone self-organizing network method as claimed in claim 6, characterized in that: The triggering of the unmanned aerial vehicles to be networked to perform unmanned aerial vehicle self-networking includes determining the unmanned aerial vehicle to be networked with the largest weight as the cluster head of the sub-cluster, and using the unmanned aerial vehicles to be networked within the effective communication distance of the cluster head as cluster members to perform self-networking with the cluster head of the sub-cluster.

8. A system using a drone self-organizing network method as claimed in any one of claims 1 to 7, characterized in that: It comprises a region acquisition module (100), a drone screening module (200), an information acquisition module (300), a weight calculation module (400) and a self-organizing network triggering module (500); The area acquisition module (100) is responsible for acquiring information about the target inspection area and determining the geographical scope that needs to be inspected; The drone screening module (200) is responsible for screening out drones located in the target inspection area and determining them as drones to be networked according to the matching of the location coordinates of the drones and the target inspection area; The information acquisition module (300) is responsible for acquiring the inspection type, remaining power, current position coordinates and coordinates of the target task location of the unmanned aerial vehicle to be networked, and calculating the distance between the unmanned aerial vehicle to be networked and the target task location; The weight calculation module (400) is responsible for calculating the weight of each drone to be networked according to the inspection type, remaining power and distance to the target mission location of the drone; The self-organizing network triggering module (500) is responsible for triggering the drones to conduct self-organizing networks according to the weight of each drone to be networked.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a drone self-organizing network method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a drone self-organizing network method described in any one of claims 1 to 7 are implemented.

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