KR102566417B1 - Method for calculaing the length of work path of a task-performing robot - Google Patents

Abstract

According to one embodiment of the present disclosure, a method for calculating a work path of a robot performing a task, performed by one or more processors of a computing device, is disclosed. The method includes determining effective working points of a task performing robot; generating a plurality of candidate work paths for the task performing robot based on the determined effective work points; and estimating a distance between the determined effective work points based on the generated plurality of candidate work paths to distribute target work points to the robot.

Description

Method for calculating the length of the working path of a task-performing robot {METHOD FOR CALCULAING THE LENGTH OF WORK PATH OF A TASK-PERFORMING ROBOT}

The present disclosure relates to a method of calculating the length of a work path of a task robot, and more particularly, to a method of calculating the length of a work path of a task robot to distribute target task points to the task robot.

When welding points are distributed to a robot, the robot visits all the distributed welding points. At this time, it is necessary to decide in what order to visit them. This is a problem of finding an order in which all welding points can be visited with the shortest distance, and belongs to the tsp (traveling salesman problem). Assuming that a robot can distribute up to 20 welding points, the order of visits can be easily determined using the existing tsp solver if all the distances between the robot and the valid working point pairs are known. there is. However, in practice, the length of the path the robot will travel between the two welds cannot be known until planning is done, and planning and knowing in advance all the distances between the robot and the valid working point pair takes an unrealistically long time. . Therefore, there is a need for a method that can estimate the length between a pair of robot and effective working point without planning.

In general, as a method for this, directly using the Euclidean distance could be used. However, the Euclidean distance does not properly reflect the robot's actual movement due to the joint limit of the actual robot and the existence of obstacles. Therefore, the decision on the order of visits and furthermore, the path planned in the order of visits can be regarded as short, and erroneous distribution can be made, which can greatly reduce the effectiveness of OLP (Off-line Programming) automation.

Therefore, there is a need for a method capable of estimating the length between a pair of a robot and an effective working point without overall planning while reflecting the actual movement of the robot.

On the other hand, the present disclosure has been derived at least based on the technical background salpin above, but the technical problem or object of the present disclosure is not limited to solving the above salpin problems or disadvantages. That is, the present disclosure may cover various technical issues related to the content to be described below, in addition to the technical issues discussed above.
(Patent Document 1) KR 2023-0002252 A

An object of the present disclosure is to calculate a working path of a working robot to distribute target working points to the working robot.

On the other hand, the technical problem to be achieved by the present disclosure is not limited to the above-mentioned technical problem, and may include various technical problems within a range apparent to those skilled in the art from the contents to be described below.

A method performed by a computing device according to an embodiment of the present disclosure for realizing the above object is disclosed. The method includes determining effective working points of a robot performing a task; generating a plurality of candidate work paths for the task performing robot based on the determined effective work points; and estimating a distance between the determined effective work points based on the generated plurality of candidate work paths to distribute target work points to the robot.

Alternatively, the determining of the effective working points of the task performing robot may include determining the effective working points based on at least one of a posture, a position, or whether entry is possible of the task performing robot.

Alternatively, the task performing robot may include a task performing part, and generating a plurality of candidate work paths for the task performing robot based on the determined effective task points may include the task performing part modeling (part); and generating a plurality of candidate work paths for the work performing robot based on the modeled work performing part and the determined valid work points.

Alternatively, generating a plurality of candidate work paths for the task performing robot based on the modeled task performing part and the determined effective task points may include the modeled task performing part and predicting one or more impossible task paths among the generated plurality of candidate task paths based on obstacle information; and generating a modified work path for the predicted impossible work path.

Alternatively, generating a plurality of candidate work paths for the task performing robot based on the determined effective work points may include a Cartesian of the task performing robot for any two of the determined effective task points. Determining whether a Cartesian move is possible; and generating a plurality of candidate work paths for the work performing robot based on whether the determined Cartesian move is possible.

Alternatively, the step of determining whether or not the Cartesian move of the task performing robot is possible with respect to any two points among the determined effective task points may include allowing the task performing robot to perform any one of the determined effective task points. It may include determining whether linear movement is possible in forward/backward, up/down, and left/right directions with respect to two points of .

Alternatively, generating a plurality of candidate work paths for the task performing robot based on the determined effective work points may include ignoring obstacle information and using the determined valid work points for any two of the points. generating an interpolated trajectory of the task performing robot; and generating a plurality of candidate work paths for the task performing robot based on the generated interpolated trajectory.

Alternatively, the target work point may refer to a point at which the task performing robot actually performs a task among the determined effective task points.

A computer program stored in a computer readable storage medium is disclosed according to an embodiment of the present disclosure for realizing the above object. When the computer program is executed on one or more processors, the one or more processors cause the one or more processors to perform operations for calculating a work path of the task robot, the operations comprising: determining effective work points of the task robot; generating a plurality of candidate work paths for the task performing robot based on the determined effective work points; and estimating a distance between the determined effective work points based on the generated plurality of candidate work paths in order to distribute target work points to the work performing robot.

A computing device according to an embodiment of the present disclosure for realizing the above object is disclosed. The device may include at least one processor; and a memory, wherein the processor is configured to: determine effective working points of the robot performing the task; generating a plurality of candidate work paths for the task performing robot based on the determined effective work points; And it may be configured to predict a distance between the determined effective work points based on the generated plurality of candidate work paths in order to distribute target work points to the robot.

The present disclosure may calculate the length of the work path of the work robot to distribute target work points to the work robot.

On the other hand, the effects of the present disclosure are not limited to the effects mentioned above, and various effects may be included within a range apparent to those skilled in the art from the contents to be described below.

1 is a block diagram of a computing device for calculating a work path of a robot performing a task according to an embodiment of the present disclosure.
2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.
3 is a flowchart illustrating a method of calculating a work path of a robot performing a task according to an embodiment of the present disclosure.
4 is a schematic diagram for explaining a process of determining effective work points of the task performing robot according to an embodiment of the present disclosure.
5A is a schematic diagram for explaining a process of modeling a work performing part according to an embodiment of the present disclosure.
5B is a schematic diagram illustrating a process of generating a plurality of candidate work paths for a robot performing a task based on a modeled task performing part and determined effective task points according to an embodiment of the present disclosure.
6 is a diagram for determining whether or not a Cartesian move of a task performing robot is possible with respect to any two arbitrary points among valid work points determined according to an embodiment of the present disclosure, and whether or not the determined Cartesian move is possible It is a schematic diagram showing a process of generating a plurality of candidate work paths for a task performing robot based on.
7 is a schematic diagram illustrating a process of generating an interpolated trajectory of a robot performing a task with respect to two arbitrary points among determined valid task points, ignoring obstacle information according to an embodiment of the present disclosure.
8 is a schematic diagram illustrating a process of estimating a distance between valid working points determined based on a plurality of generated candidate working paths according to an embodiment of the present disclosure.
9 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific details.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure, processor, object, thread of execution, program, and/or computer running on a processor. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component may be distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. Components may be connected, for example, via signals with one or more packets of data (e.g., data and/or signals from one component interacting with another component in a local system, distributed system) to other systems and over a network such as the Internet. data being transmitted) may communicate via local and/or remote processes.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless otherwise specified or clear from the context, “X employs A or B” is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or, if X uses both A and B, "X uses either A or B" may apply to either of these cases. Also, the term "and/or" as used herein should be understood to refer to and include all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the features and/or components are present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements, and/or groups thereof. Also, unless otherwise specified or where the context clearly indicates that a singular form is indicated, the singular in this specification and claims should generally be construed to mean "one or more".

In addition, the term “at least one of A or B” should be interpreted as meaning “when only A is included”, “when only B is included”, and “when A and B are combined”.

Skilled artisans will further understand that the various illustrative logical blocks, components, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or both. It should be recognized that it can be implemented with combinations of To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of this disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure. The general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention is to be accorded the widest scope consistent with the principles and novel features set forth herein.

In the present disclosure, network functions, artificial neural networks, and neural networks may be used interchangeably.

1 is a block diagram of a computing device for calculating a work path of a robot performing a task according to an embodiment of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100.

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

The processor 110 may include one or more cores, and includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit), data analysis, and processors for deep learning. The processor 110 may read a computer program stored in the memory 130 and process data for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network model. The processor 110 processes input data for learning in deep learning (DL), extracts features from input data, calculates an error, and learns a neural network model such as weight update of the neural network model using backpropagation. Calculations can be performed for At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the neural network model. For example, the CPU and GPGPU can process learning of neural network models and data classification using neural network models. In addition, in an embodiment of the present disclosure, the neural network model learning and data classification using the neural network model may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU or TPU executable program.

According to an embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg, SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) -Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The above description of the memory is only an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( Various wired communication systems such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN) may be used.

In addition, the network unit 150 presented in the present disclosure includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), SC-FDMA ( Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may be configured regardless of its communication mode, such as wired and wireless, and may be configured with various communication networks such as a personal area network (PAN) and a wide area network (WAN). can In addition, the network may be the known World Wide Web (WWW), or may use a wireless transmission technology used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth. The techniques described in this disclosure may also be used in other networks mentioned above.

2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

In a neural network, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of data of the output node may be determined based on data input to the input node. Here, a link interconnecting an input node and an output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may constitute a layer. Some of the nodes constituting the neural network may form one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in a neural network may be defined in a method different from the above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node.

In the neural network according to an embodiment of the present disclosure, the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the number of nodes progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes of the input layer may be less than the number of nodes of the output layer and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure is a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the number of nodes increases from the input layer to the hidden layer. can A neural network according to another embodiment of the present disclosure may be a neural network in the form of a combination of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted boltzmann machines (RBMs). machine), a deep belief network (DBN), a Q network, a U network, a Siamese network, a Generative Adversarial Network (GAN), and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network for outputting output data similar to input data. An auto-encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between input and output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with the reduction from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to dimensions after preprocessing of input data. In the auto-encoder structure, the number of hidden layer nodes included in the encoder may decrease as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, a sufficient amount of information may not be conveyed, so more than a certain number (e.g., more than half of the input layer, etc.) ) may be maintained.

The neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

A neural network can be trained in a way that minimizes output errors. In the learning of the neural network, the learning data is repeatedly input into the neural network, the output of the neural network for the training data and the error of the target are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. It is a process of updating the weight of each node of the neural network by backpropagating in the same direction. In the case of teacher learning, the learning data in which the correct answer is labeled is used for each learning data (ie, the labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network and a label of the training data. As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with a neural network output. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate may be used in the early stage of neural network training to increase efficiency by allowing the neural network to quickly obtain a certain level of performance, and a low learning rate may be used in the late stage to increase accuracy.

In neural network learning, generally, training data can be a subset of real data (ie, data to be processed using the trained neural network), and therefore, errors for training data are reduced, but errors for real data are reduced. There may be incremental learning cycles. Overfitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing the error of machine learning algorithms. Various optimization methods can be used to prevent such overfitting. To prevent overfitting, methods such as increasing the training data, regularization, inactivating some nodes in the network during learning, and using a batch normalization layer should be applied. can

3 is a flowchart illustrating a method of calculating a work path of a robot performing a task according to an embodiment of the present disclosure.

The computing device 100 according to an embodiment of the present disclosure may directly acquire “work information for calculating a work path of a robot performing a task” or may receive it from an external system. The external system may be a server, database, or the like that stores and manages job information for calculating a job path of the job performing robot. The computing device 100 may use job information obtained directly or received from an external system as "input data for calculating a job path of a robot performing a job."

The computing device 100 may determine effective work points of the task performing robot (S110). For example, the computing device 100 may determine valid work points based on at least one of the attitude, position, or entry availability of the task performing robot. Specifically, the computing device 100 may determine effective work points at which a specific task robot can work based on at least one of the posture, position, or entry availability of the task performing robot among the task points, and the specific task performing robot may work. The robot performing the task and the valid task points may be stored as data pairs. A detailed description of this will be described below with reference to FIG. 4 .

The computing device 100 may generate a plurality of work paths for the robot performing the work based on the valid work points determined through step S110 (S120). In this case, the task performing robot may include a task performing part, a robot arm, a joint, and the like. For example, the computing device 100 models the task performing part, and a plurality of candidate tasks for the task performing robot based on the modeled task performing part and the determined effective task points. path can be created. Specifically, the computing device 100 predicts one or more impossible task paths among the generated plurality of candidate task paths based on the modeled task performance part and obstacle information, and determines the predicted impossible task path. You can create a modified working path for The length of the path that the task robot moves between the two task points is unknown until actually planning, but the computing device 100 models the task performing part included in the task performing robot, By generating a plurality of candidate work paths using only the modeled work performing parts, it is possible to generate an approximated route without planning a path of the entire work performing robot. For example, when the modeled work performing part is immovable, the entire work performing robot is also immovable, so a search space of the work path is searched by predicting an impossible work path based on the work performing part and obstacle information. can be reduced A detailed process of modeling the work performing part and generating a plurality of candidate work paths for the work performing robot based on the modeled work performing part and the determined effective work points is illustrated in FIG. 5A below. It will be described later through FIG. 5B.

According to another embodiment of the present disclosure, the computing device 100 determines whether a Cartesian move of the task performing robot is possible with respect to any two arbitrary points among the determined valid task points, A plurality of candidate work paths for the work performing robot may be generated based on whether a Cartesian move is possible. Specifically, the computing device 100 determines whether the task performing robot can move linearly in forward/backward, up/down, and left/right directions with respect to any two of the determined effective task points. can determine whether At this time, in the Cartesian movement, the task performing robot can move linearly in forward/backward, up/down, left/right directions with respect to any two of the determined effective task points, and x, y, z It may mean movement moving within three motion axes. Meanwhile, it is more efficient for the computing device 100 to determine if the task robot can perform Cartesian movement with respect to any two of the determined effective task points than planning the length of the entire path of the task robot. , and if the task performing robot is capable of Cartesian movement with respect to any two of the determined effective task points, the "distance of the path between the two effective task points" is proportional to the Euclidean distance. can be presumed to be Whether a Cartesian move of the task performing robot is possible is determined for any two of the determined valid work points, and the task performing robot is based on the determined whether or not a Cartesian move is possible. A detailed process of generating a plurality of candidate work paths for is described below with reference to FIG. 6 .

According to another embodiment of the present disclosure, the computing device 100 ignores the obstacle information and generates an interpolated trajectory of the robot performing the task with respect to any two of the determined effective task points, and , Based on the generated interpolated trajectory, a plurality of candidate task paths for the task performing robot may be generated. In this case, the interpolated path may refer to a path in which obstacle information is ignored for any two of the determined effective work points and rotational motion and joint limits of the task performing robot are considered. In addition, the interpolated path is much more accurately predicted than the Euclidean distance when the "distance of the path between two effective work points of close or intermediate distance" is predicted because the rotational motion and joint limits of the task performing robot can be considered. It can be. A detailed process of ignoring obstacle information for any two of the determined valid work points and generating an interpolated trajectory of the task performing robot will be described later with reference to FIG. 7 .

The computing device 100 may predict a distance between effective work points determined through step S110 based on a plurality of candidate work paths generated through step S120 in order to distribute target work points to the task performing robot. (S130). In this case, the target work point may refer to a point at which the task performing robot actually performs a task among the determined effective task points. On the other hand, when the target work points are distributed to the task performing robot, it is necessary to determine in what order the work should be performed on the target work points, which is a problem of finding an order in which all target work points can be visited in the shortest distance. It belongs to the traveling salesman problem (TSP). In addition, in order for the computing device 100 to determine the order of visits to target work points using an existing TSP solver as a known technique to solve the TSP (traveling salesman problem), You need to know the distance. At this time, the computing device 100 may predict the distance between the determined effective work points based on the generated plurality of candidate work paths, and use an existing TSP solver based on the predicted distance. By doing so, it is possible to easily determine the order of visits between the determined effective work points. A detailed process of estimating the distance between valid work points determined based on the generated plurality of candidate work paths will be described later with reference to FIG. 8 .

4 is a schematic diagram for explaining a process of determining effective work points of the task performing robot according to an embodiment of the present disclosure.

The computing device 100 may determine effective work points 10 at which work is possible based on at least one of the attitude, position, and availability of the work performing robot among the work points. For example, the task performing robot may include a robot performing welding, and the work points may include welding points. At this time, referring to FIG. 4, when the working point is the position of the first welding point 10, the task performing robot can enter the first welding point 10 in a workable posture 11-1. Since the first welding point 10 may exist in a position where work is possible, the first welding point 10 may be determined as an effective work point. On the other hand, when the working point is the location of the second welding point 12, the task performing robot does not have a working posture (11-2), and cannot even enter the second welding point 12. Since no workable position exists for the second welding point 12, the second welding point 12 cannot be determined as an effective working point. Additionally, there may be limitations in length or rotation of the robot arm and joints of the task performing robot. Therefore, even if the welding points 10 and 12 are too far away from the task performing robot, it is impossible to perform the task and thus cannot be determined as an effective task point.

On the other hand, according to an embodiment of the present disclosure, the computing device 100 determines “an effective work point at which the task performing robot can work among work points” based on at least one of the attitude, position, or entry possibility of the task performing robot. s 10”, it is possible to reduce the input space (search space) and search space (search space) when calculating the work path of the task performing robot. However, the welding points 10 and 12 are merely examples for explaining effective working points, and are not limited to welding points, and points where various operations are performed may be included in the effective working points.

The process of modeling a task performing part of the task performing robot and generating a plurality of candidate work paths for the task performing robot based on the modeled task performing part and the determined effective work points is illustrated in FIG. 5A below. It will be described later through FIG. 5B.

5A is a schematic diagram for explaining a process of modeling a work performing part according to an embodiment of the present disclosure.

Referring to FIG. 5A , the task performing robot may include a task performing part 20, a robot arm 21-1, a joint 21-2, and the like, and the computing device 100 may perform the task. The performance part (20) can be modeled. For example, when the task performing robot is a robot that performs welding, the computing device 100 may model a mesh 20 of a welding gun, which is the task performing part 20 . In addition, the computing device 100 may use the modeled task performing part 20 to generate an approximated path of the entire task performing robot path. Although the length of the path that the task robot moves between the two task points is not known before actually planning, the entire task robot including the robot arm 21-1 and joints 21-2, etc. When modeling and planning all work paths of the entire modeled task performing robot, the amount of calculation becomes considerably large. Therefore, by generating an approximated path using only the modeled work performing part 20, the amount of calculation can be reduced.

Meanwhile, FIG. 5B is a schematic diagram illustrating a process of generating a plurality of candidate work paths for a robot performing a task based on a modeled task performing part and determined effective task points according to an embodiment of the present disclosure. .

The computing device 100 includes a plurality of candidate work paths 23 for the work performing robot based on the modeled work performing part 20 and the determined valid work points 10-1 and 10-2. -1, 23-2) can be created. In addition, the computing device 100 selects one or more impossible task paths 23-1 from among the generated plurality of candidate task paths based on the modeled task performing part 20 and the obstacle information 22. It is possible to predict and create a modified work path 23-2 for the predicted impossible work path 23-1. For example, in FIG. 5B , the computing device 100 moves the modeled task performing part 20 between the first effective task point 10-1 and the second effective task point 10-2. path can be created as a candidate working path. Additionally, since the computing device 100 cannot move along the generated candidate working path 23-1 due to the obstacle information 22, the modeled job performing part 20 may cause the generated candidate working path. (23-1) can be predicted as an impossible task path (23-1). Meanwhile, when the modeled task performing part 20 cannot move along the generated candidate task path 23-1, the entire task performing robot also moves along the generated candidate task path 23-1. Since it is impossible, the computing device 100 searches for a task path by predicting an impossible task path 23-1 among the generated plurality of candidate task paths based on the task performing part 20 and the obstacle information 22. You can reduce the search space. Additionally, the computing device 100 determines that the modeled task performing part 20 for the predicted impossible task path 23-1 is not affected by the obstacle information 22 and the first effective task point A modified work path 23-2 may be created to move between (10-1) and the second effective work point 10-2. For example, the computing device 100 determines an arbitrary collision avoidance point (eg, a point outside the obstacle) so that the modeled task performing part 20 is not affected by the obstacle information 22. and the modeled work execution part 20 is modified to pass “the first effective work point 10-1 -> the collision avoidance point -> the second effective work point 10-2” On the other hand, the generated candidate work path, the predicted impossible work path 23-1 or the modified work path 23-2 in FIG. However, various paths may be generated, and not only a single path but also a plurality of paths may be generated. Meanwhile, the modeled task performing part 20 is an interpolated interpolation of the task performing robot according to other embodiments of the present disclosure. It can be used in the process of creating an interpolated trajectory, and a detailed process for this will be described later with reference to FIGS. 6 and 7 .

6 is a diagram for determining whether or not a Cartesian move of a task performing robot is possible with respect to any two arbitrary points among valid work points determined according to an embodiment of the present disclosure, and whether or not the determined Cartesian move is possible It is a schematic diagram showing a process of generating a plurality of candidate work paths for a task performing robot based on.

The computing device 100 determines whether a Cartesian move of the task performing robot 30 is possible with respect to any two points among the determined effective task points 10-3, 10-4, and 10-5. In addition, a plurality of candidate work paths for the work performing robot 30 may be generated based on the determined whether the Cartesian move is possible. At this time, the Cartesian movement moves the task robot 30 forward with respect to any two points 10-3 and 10-4 among the determined valid task points 10-3, 10-4 and 10-5. It can move in /back, up/down, and left/right directions, and it can mean movement within the three motion axes x, y, and z. For example, the task performing robot 30 moving between the third effective task point 10-3 and the fourth effective task point 10-4 in FIG. You can move between points 10-3 and 10-4. Accordingly, the computing device 100 determines that a Cartesian move of the task performing robot is possible with respect to the third effective work point 10-3 and the fourth effective work point 10-4. and generate a path of the task robot 30 moving between the third valid work point 10-3 and the fourth valid work point 10-4 as a candidate work path 31. there is. On the other hand, the task performing robot 30 moving between the fourth effective working point 10-4 and the fifth effective working point 10-5 of FIG. 6 is forward/backward, up/down, left/right directions. It is impossible to move between the two effective working points 10-4 and 10-5 by moving only with . Accordingly, the computing device 100 determines that the Cartesian move of the task performing robot is impossible with respect to the fourth effective work point 10-4 and the fifth effective work point 10-5. The path 32 of the task performing robot 30 moving between the fourth valid work point 10-4 and the fifth valid work point 10-5 is a "candidate capable of Cartesian movement" It may not be created as "Working Path". Meanwhile, determining when the computing device 100 can perform Cartesian movement with respect to any two points among the determined effective work points 10-3, 10-4, and 10-5 is the path of the task performing robot. can be calculated more efficiently than planning, and if the task performing robot 30 is capable of Cartesian movement with respect to any two of the determined effective work points, "two effective work points 10-3, 10-4) can be assumed to be proportional to the Euclidean distance. Therefore, when the distance of the candidate work path 31 capable of the Cartesian movement is predicted, a Euclidean distance that can be easily calculated can be used rather than planning the path of the robot performing the task, and thus the Cartesian movement This possible candidate working path 31 can easily predict the distance of the path. Meanwhile, the path 32 of the task performing robot 30 moving between the fourth effective work point 10-4 and the fifth effective work point 10-5 is different from other embodiments of the present disclosure ( Illustratively, it may be generated as a candidate working path through a process of generating an interpolated trajectory), and a detailed process will be described below with reference to FIG. 7 .

7 is a schematic diagram illustrating a process of generating an interpolated trajectory of a robot performing a task with respect to two arbitrary points among determined effective task points, ignoring obstacle information according to an embodiment of the present disclosure.

According to another embodiment of the present disclosure, the computing device 100 ignores the obstacle information 22 and performs the task for any two of the determined valid task points 10-4 and 10-5. An interpolated trajectory 41 of the robot may be generated, and a plurality of candidate work paths for the task performing robot may be generated based on the generated interpolated trajectory 41 . At this time, in the interpolated path 41, the obstacle information 22 is ignored for any two points 10-4 and 10-5 among the determined effective work points, and the rotational motion and joints of the task performing robot are ignored. It can mean a path in which limitations are considered. For example, in FIG. 7, a rotational motion is necessarily required for the work performing part 20 to move between the fourth effective working point 10-4 and the fifth effective working point 10-5. . Additionally, when the computing device 100 creates a path for the work performing part 20 to move between the fourth effective work point 10-4 and the fifth effective work point 10-5, If even the obstacle information 22 is considered, as in the example of FIG. 5B, the computing device 100 needs to create and modify a path through planning. In order to solve this problem, in the embodiment of FIG. 7 , the computing device 100 ignores the obstacle information 22 and determines the position of the work performing part 20 at the fourth effective work point 10-4. , or position” and “posture or position of the work performing part 20 at the fifth effective work point 10-5”, the work performing part 20 is at least It is possible to create an interpolated trajectory 41 that can move between the two effective work points 10-4 and 10-5 by rotating and moving of . Through this, the generated interpolated trajectory 41 ) is expected to be predicted much more accurately than the Euclidean distance when “the distance of a path between two valid work points of close or intermediate distance” is predicted because the rotational motion and joint limit of the task performing robot can be considered. The computing device 100 may predict the distance between valid work points determined based on the plurality of candidate work paths generated through FIGS. 5A to 7 , and a detailed process will be described later with reference to FIG. .

8 is a schematic diagram for explaining a process of predicting a distance between valid working points determined based on a plurality of generated candidate working paths according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 performs the determined task based on the generated plurality of candidate work paths 23-2, 31, and 41 in order to distribute target work points to the work performing robot. Distances 50-1, 50-2, and 50-3 between the effective work points 10-1, 10-2, 10-3, 10-4, and 10-5 may be predicted. In this case, the target work point may mean a point at which the task performing robot actually performs a task among the determined effective task points 10 - 3 , 10 - 4 , and 10 - 5 . On the other hand, when the target work points are distributed to the task performing robot, it is necessary to determine in what order the work should be performed on the target work points, which is a problem of finding an order in which all target work points can be visited in the shortest distance. It belongs to the traveling salesman problem (TSP). In addition, the computing device 100 needs to know the distance between the work points in order to determine the order of visits to the target work points using a known TSP solver. For example, in the embodiment of FIG. 8 , the third, fourth, and fifth effective work points 10-3, 10-4, and 10-5 may be distributed as target work points. At this time, the computing device 100 determines the third effective working point 10-3 and the fourth effective working point based on the "candidate path 31 capable of Cartesian movement generated through the example described in FIG. 6". The distance (50-1) between (10-4) can be predicted. In addition, the computing device 100 determines the fourth effective working point 10-4 and the fifth effective working point based on the “interpolated trajectory 41 generated through the example described in FIG. 7”. It is possible to predict the distance 50-2 between (10-5). Additionally, the computing device 100 is based on the “method of generating the interpolated trajectory 41 described in FIG. Thus, a "candidate working path between the third effective working point 10-3 and the fifth effective working point 10-5" can be generated, and the generated "third effective working point 10-3 and The distance 50-3 between the third effective working point 10-3 and the fifth effective working point 10-5 based on the "candidate working path between the fifth effective working point 10-5" Additionally, since the distances 50-1, 50-2, and 50-3 between the target work points 10-3, 10-4, and 10-5 are predicted, the computing device 100 can find an order in which all target work points can be visited with the shortest distance using an already existing TSP solver as a known technique. In this case, the target work points 10-3 and 10 The sum of the distances (50-1) and (50-2) between -4 and 10-5 is the combination of the predicted distances (specifically, the distance of 50-1, the distance of 50-2, or the 50- Since it is the shortest distance among the distances of 3), the order of visits between the target work points is "the third effective work point (10-3) -> the fourth effective work point (10-4) -> the fifth It may be determined in the order of the effective working point (10-5) or “the fifth effective working point (10-5) -> the fourth effective working point (10-4) -> the third effective working point (10-3)” Accordingly, the computing device 100 determines the determined valid task points 10-1, 10-2, 10-3, Predict the distances (50-1, 50-2, 50-3) between 10-4 and 10-5, and already exist based on the predicted distances (50-1, 50-2, 50-3) It is possible to easily determine the order of visits between the determined valid working points by using a TSP solver that However, the target work points 10-3, 10-4, and 10-5 and the predicted distances 50-1, 50-2, and 50-3, which are examples, are only examples, and are not limited thereto.

According to an embodiment of the present disclosure, a computer readable medium storing a data structure is disclosed. Data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. Data structure may refer to the organization of data to solve a specific problem (eg, data retrieval, data storage, data modification in the shortest time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. A logical relationship between data elements may include a connection relationship between user-defined data elements. A physical relationship between data elements may include an actual relationship between data elements physically stored in a computer-readable storage medium (eg, a persistent storage device). The data structure may specifically include a set of data, a relationship between data, and a function or command applicable to the data. Through an effectively designed data structure, a computing device can perform calculations while using minimal resources of the computing device. Specifically, the computing device can increase the efficiency of operation, reading, insertion, deletion, comparison, exchange, and search through an effectively designed data structure.

The data structure can be divided into a linear data structure and a non-linear data structure according to the shape of the data structure. A linear data structure may be a structure in which only one data is connected after one data. Linear data structures may include lists, stacks, queues, and decks. A list may refer to a series of data sets in which order exists internally. The list may include a linked list. A linked list may be a data structure in which data are connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer can contain information about connection to the next or previous data. A linked list can be expressed as a singly linked list, a doubly linked list, or a circular linked list depending on the form. A stack can be a data enumeration structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (eg, inserted or deleted) at only one end of the data structure. The data stored in the stack may be a LIFO-Last in First Out (Last in First Out) data structure. A queue is a data listing structure that allows limited access to data, and unlike a stack, it can be a data structure (FIFO-First in First Out) in which data stored later comes out later. A deck can be a data structure that can handle data from either end of the data structure.

The nonlinear data structure may be a structure in which a plurality of data are connected after one data. The non-linear data structure may include a graph data structure. A graph data structure can be defined as a vertex and an edge, and an edge can include a line connecting two different vertices. A graph data structure may include a tree data structure. The tree data structure may be a data structure in which one path connects two different vertices among a plurality of vertices included in the tree. That is, it may be a data structure that does not form a loop in a graph data structure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, a neural network is unified and described. The data structure may include a neural network. And the data structure including the neural network may be stored in a computer readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network It may include a loss function for learning of . A data structure including a neural network may include any of the components described above. That is, the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network. It may be configured to include all or any combination thereof, such as a loss function for learning of . In addition to the foregoing configurations, the data structure comprising the neural network may include any other information that determines the characteristics of the neural network. In addition, the data structure may include all types of data used or generated in the computational process of the neural network, but is not limited to the above. A computer readable medium may include a computer readable recording medium and/or a computer readable transmission medium. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes.

The data structure may include data input to the neural network. A data structure including data input to the neural network may be stored in a computer readable medium. Data input to the neural network may include training data input during a neural network learning process and/or input data input to a neural network that has been trained. Data input to the neural network may include pre-processed data and/or data subject to pre-processing. Pre-processing may include a data processing process for inputting data to a neural network. Accordingly, the data structure may include data subject to pre-processing and data generated by pre-processing. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, weights and parameters may be used in the same meaning.) Also, a data structure including weights of a neural network may be stored in a computer readable medium. A neural network may include a plurality of weights. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. A data value output from an output node may be determined based on the weight. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

As a non-limiting example, the weights may include weights that are varied during neural network training and/or weights for which neural network training has been completed. The variable weight in the neural network learning process may include a weight at the time the learning cycle starts and/or a variable weight during the learning cycle. The weights for which neural network learning has been completed may include weights for which learning cycles have been completed. Accordingly, the data structure including the weights of the neural network may include a data structure including weights that are variable during the neural network learning process and/or weights for which neural network learning is completed. Therefore, it is assumed that the above-described weights and/or combinations of weights are included in the data structure including the weights of the neural network. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer readable storage medium (eg, a memory or a hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or another computing device and later reconstructed and used. A computing device may serialize data structures to transmit and receive data over a network. The data structure including the weights of the serialized neural network may be reconstructed on the same computing device or another computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure for increasing the efficiency of operation while minimizing the resource of the computing device (for example, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree). The foregoing is only an example, and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of the neural network. Also, the data structure including the hyperparameters of the neural network may be stored in a computer readable medium. A hyperparameter may be a variable variable by a user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle iterations, weight initialization (eg, setting the range of weight values to be targeted for weight initialization), hidden unit number (eg, the number of hidden layers and the number of nodes in the hidden layer). The foregoing data structure is only an example, and the present disclosure is not limited thereto.

9 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above as being generally embodied by a computing device, those skilled in the art will understand that the present disclosure may be combined with computer executable instructions and/or other program modules that may be executed on one or more computers and/or with hardware. It will be appreciated that it can be implemented as a combination of software.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, it will be appreciated by those skilled in the art that the methods of the present disclosure may be used in single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like. It will be appreciated that other computer system configurations may be implemented, including (each of which may be operative in connection with one or more associated devices).

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Computer readable media can be any medium that can be accessed by a computer, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer readable media may include computer readable storage media and computer readable transmission media. Computer readable storage media are volatile and nonvolatile media, transitory and non-transitory, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store desired information.

A computer readable transmission medium typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Including all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information within the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer readable transmission media.

An exemplary environment 1100 implementing various aspects of the present disclosure is shown including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106 , to processing unit 1104 . Processing unit 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1104.

System bus 1108 may be any of several types of bus structures that may additionally be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, or EEPROM, and is a basic set of information that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also include an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes—a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM) for reading disc 1122 or reading from or writing to other high capacity optical media such as DVDs). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to the system bus 1108 by a hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to The interface 1124 for external drive implementation includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art can use zip drives, magnetic cassettes, flash memory cards, and cartridges. It will be appreciated that other tangible computer readable media such as , , and the like may also be used in the exemplary operating environment and that any such media may include computer executable instructions for performing the methods of the present disclosure. .

A number of program modules may be stored on the drive and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented in a variety of commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, a parallel port, IEEE 1394 serial port, game port, USB port, IR interface, may be connected by other interfaces such as the like.

A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, handheld computer, microprocessor-based entertainment device, peer device, or other common network node, and generally includes It includes many or all of the components described for, but for simplicity, only memory storage device 1150 is shown. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and corporations and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, computer 1102 connects to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communications to LAN 1152, which also includes a wireless access point installed therein to communicate with wireless adapter 1156. When used in a WAN networking environment, computer 1102 may include a modem 1158, be connected to a communicating computing device on WAN 1154, or establish communications over WAN 1154, such as over the Internet. have other means. A modem 1158, which may be internal or external and a wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored on remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between computers may be used.

Computer 1102 is any wireless device or entity that is deployed and operating in wireless communication, eg, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communication satellites, wireless detectable tags associated with It operates to communicate with arbitrary equipment or places and telephones. This includes at least Wi-Fi and Bluetooth wireless technologies. Thus, the communication may be a predefined structure as in conventional networks or simply an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet without wires. Wi-Fi is a wireless technology, such as a cell phone, that allows such devices, eg, computers, to transmit and receive data both indoors and outdoors, i.e. anywhere within coverage of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band) .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields s or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein are electronic hardware, (for convenience) , may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory devices (eg, EEPROM, cards, sticks, key drives, etc.), but are not limited thereto. Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based upon design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure, and the general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be interpreted in the widest scope consistent with the principles and novel features presented herein.

Claims (10)

As a method for calculating a work path of a robot performing a task, performed by a computing device,
determining effective working points of the robot performing the task;
determining whether Cartesian movement of the task performing robot is possible with respect to any two points among the determined effective task points;
generating a plurality of candidate work paths for the work performing robot based on whether the determined Cartesian move is possible; and
estimating a distance between the determined effective work points based on the generated plurality of candidate work paths to distribute target work points to the robot;
including,
method.
According to claim 1,
Determining the effective work points of the task performing robot,
determining effective work points based on at least one of a posture, a position, or whether entry is possible of the task performing robot;
including,
method.
delete delete delete According to claim 1,
Determining whether or not Cartesian movement of the task performing robot is possible with respect to any two points among the determined valid task points includes:
Determining whether or not the task performing robot can move linearly in forward/backward, up/down, left/right directions with respect to any two of the determined effective task points,
method.
delete According to claim 1,
The target work point is,
Means a point at which the task performing robot actually performs a task among the determined effective task points,
method.
A computer program stored on a computer-readable storage medium, which, when executed by one or more processors, causes the one or more processors to perform operations for calculating a work path of a task performing robot, the operations comprising:
an operation of determining effective work points of the task performing robot;
determining whether Cartesian movement of the task performing robot is possible with respect to any two points among the determined effective task points;
generating a plurality of candidate work paths for the work performing robot based on whether the determined Cartesian move is possible; and
estimating a distance between the determined effective work points based on the generated plurality of candidate work paths to distribute target work points to the work performing robot;
including,
A computer program stored on a computer readable storage medium.
As a computing device,
at least one processor; and
Memory
including,
The at least one processor,
determine effective working points of the task performing robot;
determining whether Cartesian movement of the task performing robot is possible with respect to any two points among the determined effective task points;
generating a plurality of candidate work paths for the work performing robot based on whether the determined Cartesian move is possible; and
And configured to predict a distance between the determined effective work points based on the generated plurality of candidate work paths, in order to distribute target work points to the work robot.
computing device.