KR102660168B1 - 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 task performing robot, performed by one or more processors of a computing device, is disclosed. The method includes determining effective work 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 predicting distances 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 robots.

Description

{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-performing robot, and more specifically, to a method of calculating the length of a work path of a task-performing robot to distribute target work points to the task-performing robot.

When welding spots are distributed to a robot, the robot visits all of the distributed welding spots. At this time, it is necessary to decide in what order to visit all welding points. This is a problem of finding an order in which all welding points can be visited in the shortest distance, which is the traveling salesman problem (tsp). Assuming that one robot can be distributed to up to 20 welding points, if all distances between the robot and the effective work point pairs are known, the visiting order can be easily determined using the already existing tsp solver. there is. However, in reality, the length of the path that the robot travels between two welding points cannot be known until planning, and planning and knowing in advance all the distances between pairs of robots and effective work points takes a long time to the point where it is realistically impossible. . Therefore, a method is needed to estimate the length between pairs of robots and effective work points without planning.

In general, a method for this could be to directly use the Euclidean distance. However, due to the joint limit of the actual robot, the presence of obstacles, etc., the Euclidean distance does not properly reflect the actual movement of the robot. If only the Euclidean distance is used, the actual robot expresses a very far trajectory as close. As a result, the visit order can be determined and the path planned for that visit order can be viewed as short and incorrectly distributed, which can greatly reduce the effectiveness of OLP (Off-line programming) automation.

Accordingly, there is a need for a method that reflects the actual movement of the robot and can estimate the length between pairs of robots and effective work points without overall planning.

Meanwhile, the present disclosure has been derived based at least on the technical background examined above, but the technical problem or purpose of the present disclosure is not limited to solving the problems or shortcomings examined above. In other words, in addition to the technical issues discussed above, the present disclosure can cover various technical issues related to the content to be described below.

(Patent Document 0001) Republic of Korea Patent Publication No. 10-2023-0002252 (2023.01.05)

The present disclosure aims to solve the problem of calculating the work path of a task-performing robot in order to distribute target work points to the task-performing robot.

Meanwhile, the technical problem to be achieved by the present disclosure is not limited to the technical problems mentioned above, and may include various technical problems within the scope of what is apparent to those skilled in the art from the contents described below.

A method performed by a computing device according to an embodiment of the present disclosure for realizing the above-described problem is disclosed. The method includes determining effective work 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 predicting distances 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 robots.

Alternatively, determining the effective work points of the task-performing robot may include determining the effective work points based on at least one of the posture, position, or entry availability of the task-performing robot.

Alternatively, the task performing robot includes a task performing part, and generating a plurality of candidate task paths for the task performing robot based on the determined effective task points comprises: 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 effective work points.

Alternatively, generating a plurality of candidate work paths for the task performing robot based on the modeled task performance part and the determined effective work points may comprise: the modeled task performance part and predicting one or more impossible work paths among the generated plurality of candidate work paths based on obstacle information; And it may further include 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 step of determining whether movement (Cartesian move) is possible; and generating a plurality of candidate work paths for the task-performing robot based on the determined Cartesian move possibility.

Alternatively, the step of determining whether a Cartesian move of the task-performing robot is possible with respect to any two points among the determined effective work points may include: It may include a step of determining whether linear movement is possible in the front/back, up/down, and left/right directions with respect to the two points.

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 selecting any two of the determined effective work 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 mean a point at which the work performing robot actually performs work among the determined effective work points.

According to an embodiment of the present disclosure for realizing the above-described object, a computer program stored in a computer-readable storage medium is disclosed. The computer program, when executed on one or more processors, causes the one or more processors to perform operations for calculating a work path of the task-performing robot, the operations comprising: determining effective work points of the task-performing robot; generating a plurality of candidate work paths for the work performing robot based on the determined effective work points; and predicting distances 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 robots.

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

The present disclosure can calculate the length of a work path of a task performing robot in order to distribute target work points to the task performing robot.

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

1 is a block diagram of a computing device for calculating a work path of a task-performing robot according to an embodiment of the present disclosure.
Figure 2 is a schematic diagram showing a network function according to an embodiment of the present disclosure.
Figure 3 is a flowchart showing a method of calculating a work path of a work-performing robot according to an embodiment of the present disclosure.
Figure 4 is a schematic diagram illustrating a process for determining effective work points of the task-performing robot according to an embodiment of the present disclosure.
FIG. 5A is a schematic diagram illustrating a process of modeling a task performance part according to an embodiment of the present disclosure.
FIG. 5B is a schematic diagram illustrating a process for generating a plurality of candidate work paths for a work performing robot based on a modeled work performance part and determined effective work points according to an embodiment of the present disclosure.
FIG. 6 shows the determination of whether a Cartesian move of a task-performing robot is possible for any two points among the valid work points determined according to an embodiment of the present disclosure, and the determined Cartesian move whether or not a Cartesian move is possible. This is a schematic diagram showing the process of generating a plurality of candidate work paths for a task-performing robot based on .
FIG. 7 is a schematic diagram illustrating a process for generating an interpolated trajectory of a task-performing robot for any two points among the determined effective work points, ignoring obstacle information, according to an embodiment of the present disclosure.
FIG. 8 is a schematic diagram illustrating a process for predicting the distance between effective work points determined based on a plurality of candidate work paths generated according to an embodiment of the present disclosure.
9 is a brief, general schematic diagram of an example 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 disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

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

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “a case containing only A,” “a case containing only B,” and “a case of combining A and B.”

Those skilled in the art will additionally understand that the various illustrative logical blocks, configurations, modules, circuits, means, logic, 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 software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone 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. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

In this disclosure, network function, artificial neural network, and neural network may be used interchangeably.

1 is a block diagram of a computing device for calculating a work path of a task-performing robot 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 different components for performing the computing environment of the computing device 100, and only some of the disclosed components may configure 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 be composed of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit) may include a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 130 and perform data processing 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 errors, and learns neural network models such as updating the weights 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 work together to process neural network model learning and data classification using the neural network model. Additionally, in one embodiment of the present disclosure, the processors of a plurality of computing devices can be used together to process learning of a neural network model and data classification using the neural network model. Additionally, 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, hard disk type, multimedia card micro type, or card type memory (e.g. (e.g. SD or -Only Memory), and may include at least one type of storage medium among magnetic memory, magnetic disk, and optical disk. The computing device 100 may operate in connection with web storage that performs a storage function of the memory 130 on the Internet. The description of the memory described above is merely an example, and the present disclosure is not limited thereto.

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

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), and SC-FDMA ( A variety of wireless communication systems can be used, such as Single Carrier-FDMA) and other systems.

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

Figure 2 is a schematic diagram showing 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 can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node. Nodes (or neurons) that make up neural networks may be interconnected by one or more links.

Within a neural network, one or more nodes connected through a link may form a relative input node and output node relationship. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes 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 the data of the output node may be determined based on the data input to the input node. Here, the link connecting the input node and the output node may have a weight. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

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

A neural network may consist of a set of one or more nodes. A subset of nodes that make up a neural network can form a layer. Some of the nodes constituting the neural network may form one layer based on the distances from the first input node. For example, a set of nodes with a distance n from the initial input node may constitute n layers. The distance from the initial input node can be defined by the minimum number of links that must be passed to reach the node from the initial input node. However, this definition of a layer is arbitrary for explanation purposes, and the order of a layer within a neural network may be defined in a different way than described above. For example, a layer of nodes may be defined by distance from the final output node.

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

The neural network according to an embodiment of the present disclosure is a neural network in which 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 it progresses from the input layer to the hidden layer. You 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 in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure may be 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 it progresses from the input layer to the hidden layer. You can. A neural network according to another embodiment of the present disclosure may be a neural network that is a combination of the above-described neural networks.

A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to the input layer and output layer. Deep neural networks allow you to identify latent structures in data. In other words, it is possible to identify the potential structure of a photo, text, video, voice, or music (e.g., what object is in the photo, what the content and emotion of the text are, what the content and emotion of the voice are, etc.) . Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNN), auto encoders, generative adversarial networks (GAN), and restricted Boltzmann machines (RBM). machine), deep belief network (DBN), Q network, U network, Siamese network, Generative Adversarial Network (GAN), etc. 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 to output output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be placed between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically and reduced from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform nonlinear dimensionality reduction. The number of input layers and output layers can be corresponded to the dimension after preprocessing of the input data. In an auto-encoder structure, the number of nodes in the hidden layer included in the encoder may have a structure that decreases 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, not enough information may be conveyed, so if it is higher than a certain number (e.g., more than half of the input layers, etc.) ) may be maintained.

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

Neural networks can be trained to minimize output errors. In neural network learning, learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data 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. This is the process of updating the weight of each node in the neural network through backpropagation. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (i.e., labeled learning data), and in the case of non-teacher learning, the correct answer may not be labeled in each learning data. That is, for example, in the case of teacher learning regarding data classification, the learning data may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in the case of non-teachable learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is back-propagated in the neural network in the reverse direction (i.e., from the output layer to the input layer), and the connection weight of each node in each layer of the neural network can be updated according to back-propagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of learning a neural network, a high learning rate can be used to ensure that the neural network quickly achieves a certain level of performance to increase efficiency, and in the later stages of training, a low learning rate can be used to increase accuracy.

In the learning of neural networks, the training data can generally be a subset of real data (i.e., the data to be processed using the learned neural network), and thus the error for the training data is reduced, but the error for the real data is reduced. There may be an incremental learning cycle. Overfitting is a phenomenon in which errors in actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that learned a cat by showing a yellow cat fails to recognize that it is a cat when it sees a non-yellow cat may be a type of overfitting. Overfitting can cause errors in machine learning algorithms to increase. To prevent such overfitting, various optimization methods can be used. To prevent overfitting, methods such as increasing the learning data, regularization, dropout to disable some of the network nodes during the learning process, and use of a batch normalization layer can be applied. You can.

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

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

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

The computing device 100 may generate a plurality of work paths for the work performing robot based on the effective work points determined in step S110 (S120). At this time, the task-performing robot may include a task-performing part, a robot arm, a joint, etc. For example, the computing device 100 models the task performance part and generates a plurality of candidate tasks for the task performance robot based on the modeled task performance part and the determined effective task points. You can create a route. 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 matches the predicted impossible task path to the predicted impossible task path. You can create a modified work path for this. The length of the path that the task-performing robot moves between two work points cannot be known before actual 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 task performance portion, an approximated path can be created without planning the path of the entire task performing robot. For example, if the modeled task performance part cannot move, the entire task performance robot also cannot move, so the search space of the task path is created by predicting an impossible task path based on the task performance part and obstacle information. It can be reduced. The specific process of modeling the work performance part and generating a plurality of candidate work paths for the work performance robot based on the modeled work performance part and the determined effective work points is shown in FIG. 5A below. This will be described later through Figures 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 points among the determined effective task points, and determines whether the task-performing robot can perform a Cartesian move. A plurality of candidate work paths for the task-performing robot can be generated based on whether Cartesian movement is possible. Specifically, the computing device 100 determines whether the task performing robot can move linearly in the forward/backward, up/down, and left/right directions with respect to any two points among the determined effective work points. You can judge whether or not. At this time, Cartesian movement allows the task-performing robot to move linearly in the forward/backward, up/down, and left/right directions with respect to any two points among the determined effective work points, and can move linearly in x, y, z Can refer to movement within three axes of motion. Meanwhile, it is more efficient for the computing device 100 to determine when a Cartesian movement of the task-performing robot is possible for any two of the determined effective task points than to plan the entire path length of the task-performing robot. It can be calculated as, and if the task-performing robot is capable of Cartesian movement with respect to any two of the determined effective work points, the “path distance between two valid work points” will be proportional to the Euclidean distance. It can be assumed that It is determined whether the task-performing robot is capable of Cartesian movement with respect to any two points among the determined effective work points, and the task-performing robot is based on the determined Cartesian move capability. The specific process of generating a plurality of candidate work paths for is described later with reference to FIG. 6.

According to another embodiment of the present disclosure, the computing device 100 ignores obstacle information and generates an interpolated trajectory of the task performing robot for any two points among the determined effective task points. , a plurality of candidate work paths for the task performing robot may be generated based on the generated interpolated trajectory. At this time, the interpolated path may mean a path in which obstacle information is ignored for any two of the determined effective work points and the rotational motion and joint limit of the work performing robot are taken into consideration. Additionally, the interpolated path predicts much more accurately than the Euclidean distance when the “distance of the path between two valid task points at close or intermediate distances” is predicted because the rotational motion and joint limitations of the robot performing the task can be taken into account. It can be. The specific process in which obstacle information is ignored for any two of the determined effective work points and an interpolated trajectory of the work performing robot is generated will be described later with reference to FIG. 7.

In order to distribute target work points to the task-performing robots, the computing device 100 may predict the distance between effective work points determined through step S110 based on a plurality of candidate work paths generated through step S120. (S130). At this time, the target work point may mean a point at which the work performing robot actually performs work among the determined effective work points. Meanwhile, when the target work points are distributed to the task performing robots, it must be determined in what order the work should be performed on the target work points. This is a problem of finding an order in which all target work points can be visited by the shortest distance. It belongs to TSP (traveling salesman problem). In addition, in order for the computing device 100 to determine the visit order for target work points using a TSP solver that already exists as a known technology in order to solve the traveling salesman problem (TSP), You need to know the distance. At this time, the computing device 100 may predict the distance between effective work points determined based on the generated plurality of candidate work paths, and use an already existing TSP solver based on the predicted distance. By doing so, the visiting order between the determined effective work points can be easily determined. The specific process of predicting the distance between effective work points determined based on the plurality of generated candidate work paths will be described later with reference to FIG. 8.

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

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

Meanwhile, according to an embodiment of the present disclosure, the computing device 100 determines “a valid work point at which the work-performing robot can work among work points” based on at least one of the posture, position, or entry possibility of the work-performing robot. By determining “fields (10),” the input space (search space) and search space (search space) can be reduced when calculating the work path of the task-performing robot. However, the welding points 10 and 12 are merely examples for explaining effective work points, and are not limited to welding points, and points where various tasks are performed may be included in the effective work points.

The process of modeling the task performance part of the task performance robot and generating a plurality of candidate work paths for the task performance robot based on the modeled task performance part and the determined effective work points is shown in FIG. 5A below. This will be described later through Figures 5b.

FIG. 5A is a schematic diagram illustrating a process of modeling a task performance 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, etc., and the computing device 100 may perform the task. The execution part (20) can be modeled. For example, if the task performing robot is a robot that performs welding, the computing device 100 may model the mesh 20 of the welding gun, which is the task performing part 20 . Additionally, the computing device 100 may use the modeled task performing part 20 to generate a path that is an approximation of the path of the entire task performing robot. The length of the path that the task-performing robot moves between two work points cannot be known until it is actually planned, but the entire task-performing robot, including the robot arm (21-1) and joints (21-2), etc. When modeling and planning all work paths of the modeled overall task-performing robot, the amount of computation increases significantly. Therefore, the amount of computation can be reduced by generating an approximated path using only the modeled task performance part 20.

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

The computing device 100 provides a plurality of candidate work paths 23 for the work performing robot based on the modeled work performing part 20 and the determined effective work points 10-1 and 10-2. -1, 23-2) can be generated. In addition, the computing device 100 selects one or more impossible task paths 23-1 among the generated plurality of candidate task paths based on the modeled task performance part 20 and obstacle information 22. It is possible to predict and generate 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 performance part 20 between the first effective task point 10-1 and the second effective task point 10-2. You can create a path as a candidate work path. Additionally, the computing device 100 cannot move along the generated candidate task path 23-1 because the modeled task performance part 20 is unable to move along the generated candidate task path 23-1 due to the obstacle information 22. (23-1) can be predicted as the impossible work path (23-1). Meanwhile, if the modeled task performing part 20 cannot move along the generated candidate task path 23-1, the entire task performing robot may also move along the generated candidate task path 23-1. Since this is impossible, the computing device 100 searches for a work path by predicting an impossible work path 23-1 among the generated plurality of candidate work paths based on the work performance portion 20 and the obstacle information 22. Search space can be reduced. Additionally, the computing device 100 determines that the modeled task performance part 20 for the predicted impossible task path 23-1 is not affected by the obstacle information 22 and is located at the first effective task point. A modified work path 23-2 can 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 (e.g., a point outside the obstacle) so that the modeled task performance part 20 is not affected by the obstacle information 22. And, the modeled work performance part (20) is modified to pass through “the first effective work point (10-1) -> the collision avoidance point -> the second effective work point (10-2)” Meanwhile, in FIG. 5B, the generated candidate work path, the predicted impossible work path 23-1, or the modified work path 23-2 are examples. However, various paths can be created, and not only a single path but also a plurality of paths can be created. Meanwhile, the modeled task performing part 20 is an interpolation of a task performing robot according to other embodiments of the present disclosure. It can be used in the process of creating an interpolated trajectory, and the specific process for this will be described later with reference to FIGS. 6 and 7.

Figure 6 shows the determination of whether a Cartesian move of a task-performing robot is possible for any two points among the valid work points determined according to an embodiment of the present disclosure, and the determined whether or not a Cartesian move is possible. This is a schematic diagram showing the 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 work points 10-3, 10-4, and 10-5. And, based on the determined availability of Cartesian movement, a plurality of candidate work paths for the task performing robot 30 may be generated. At this time, Cartesian movement is when the task performing robot 30 moves forward with respect to any two points (10-3, 10-4) among the determined effective work points (10-3, 10-4, 10-5). It can move in the /backward, up/down, and left/right directions, and can refer to movement within the three motion axes x, y, and z. For example, the task performing robot 30 moving between the third effective work point 10-3 and the fourth effective work point 10-4 in FIG. 6 moves only in the up/down direction to perform two valid tasks. You can move between points (10-3, 10-4). Accordingly, the computing device 100 determines that Cartesian movement 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. The path of the work performing robot 30 moving between the third effective work point 10-3 and the fourth effective work point 10-4 can be generated as a candidate work path 31. there is. On the other hand, the work performing robot 30 moving between the fourth effective work point 10-4 and the fifth effective work point 10-5 in FIG. 6 moves in the forward/backward, up/down, and left/right directions. It is not possible to move between the above two valid work points (10-4, 10-5) by moving only. Accordingly, the computing device 100 determines that Cartesian movement 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 effective work point 10-4 and the fifth effective work point 10-5 is a “candidate capable of Cartesian movement.” It may not be created as a “working path”. Meanwhile, determining when the computing device 100 is capable of Cartesian movement to any two of the determined effective work points 10-3, 10-4, and 10-5 is determined based on the path of the work 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 points among the determined effective work points, "two effective work points (10-3, 10-4) It can be assumed that the “distance of the path between” is proportional to the Euclidean distance. Therefore, when the distance of the candidate work path 31 capable of the Cartesian movement is predicted, the Euclidean distance can be used, which can be calculated more easily than the path of the task performing robot is planned, so the Cartesian movement The distance of this possible candidate work path 31 can be easily predicted. Meanwhile, the path 32 of the work performing robot 30 moving between the fourth effective work point 10-4 and the fifth effective work point 10-5 is similar to other embodiments of the present disclosure ( As an example, a candidate work path may be created through a process of generating an interpolated trajectory, and the specific process will be described later with reference to FIG. 7.

FIG. 7 is a schematic diagram illustrating a process for generating an interpolated trajectory of a task-performing robot for any two points among the determined effective work 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 at any two points (10-4, 10-5) among the determined effective task points. 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, the interpolated path 41 ignores the obstacle information 22 for any two points (10-4, 10-5) among the determined effective work points, and the rotational motion and joints of the work performing robot It may mean a path where limitations are taken into account. For example, in FIG. 7, a rotational motion is necessary for the work performing part 20 to move between the fourth effective work point 10-4 and the fifth effective work point 10-5. . Additionally, when the computing device 100 creates a path along which the work performing part 20 moves between the fourth effective work point 10-4 and the fifth effective work point 10-5, If obstacle information 22 is taken into consideration, the computing device 100 needs to create and modify a path through planning, as shown in the example of FIG. 5B. To solve this, in the embodiment of FIG. 7, the computing device 100 ignores the obstacle information 22 and determines the “posture of the work performing part 20 at the fourth effective work point 10-4.” , or position" and "the posture, or position of the work performing part 20 at the fifth effective work point 10-5", so that the work performing part 20 is at least Through this, an interpolated trajectory 41 that can move between two effective work points 10-4 and 10-5 can be created by rotating and moving. ) can be predicted much more accurately than the Euclidean distance when the “distance of the path between two valid work points at close or intermediate distances” can be predicted because the rotational motion and joint limits of the task-performing robot can be taken into account. The computing device 100 can predict the distance between effective work points determined based on the plurality of candidate work paths generated through FIGS. 5A to 7, and the specific process will be described later with reference to FIG. 8. .

FIG. 8 is a schematic diagram illustrating a process for predicting the distance between effective work points determined based on a plurality of candidate work paths generated according to an embodiment of the present disclosure.

According to one embodiment of the present disclosure, the computing device 100 is configured to distribute the target work point to the work performing robot, based on the generated plurality of candidate work paths 23-2, 31, and 41. The distances (50-1, 50-2, 50-3) between the effective work points (10-1, 10-2, 10-3, 10-4, 10-5) can be predicted. At this time, the target work point may mean a point at which the work performing robot actually performs work among the determined effective work points 10-3, 10-4, and 10-5. Meanwhile, when the target work points are distributed to the task performing robots, it must be determined in what order the work should be performed on the target work points. This is a problem of finding an order in which all target work points can be visited by the shortest distance. It belongs to TSP (traveling salesman problem). Additionally, in order for the computing device 100 to determine the visit order for target work points using a TSP solver that already exists as a known technology, the distance between work points must be known. 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 selects the third effective work point 10-3 and the fourth effective work 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 work point 10-4 and the fifth effective work point based on the “interpolated trajectory 41 generated through the example described in FIG. 7.” Additionally, the computing device 100 can predict the distance (50-2) between (10-5) based on the “method for generating an interpolated trajectory (41) described in FIG. 7 above.” Thus, a “candidate work path between the third effective work point (10-3) and the fifth valid work point (10-5)” can be created, and the created “third effective work point (10-3) and The distance (50-3) between the third effective work point (10-3) and the fifth effective work point (10-5) based on the “candidate work path between the fifth effective work 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 In this case, the target work points 10-3, 10 can be found using a known technology and an existing TSP solver. The sum of the distances (50-1) and (50-2) between -4 and 10-5 is a combination of the above predicted distances (specifically, a distance of 50-1, a distance of 50-2, or 50- Since it is the shortest distance among the distances of 3 (the sum of 2), the order of visits between the target work points is “3rd effective work point (10-3) -> 4th effective work point (10-4) -> 5th It can be determined in the following order: effective work point (10-5) or “5th effective work point (10-5) -> 4th effective work point (10-4) -> 3rd effective work point (10-3)” Accordingly, the computing device 100 determines the effective work points 10-1, 10-2, and 10-3 based on the generated plurality of candidate work paths 23-2, 31, and 41. Predict the distance (50-1, 50-2, 50-3) between 10-4, 10-5 and already exist based on the predicted distance (50-1, 50-2, 50-3) The visiting order between the determined valid work points can be easily determined by using a TSP solver. However, the example target work points (10-3, 10-4, 10-5) and predicted distances (50-1, 50-2, 50-3) 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 to enable efficient access and modification of data. Data structure can refer to the organization of data to solve a specific problem (e.g., retrieving data, storing data, or modifying data in the shortest possible time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. Logical relationships between data elements may include connection relationships between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements that are physically stored in a computer-readable storage medium (e.g., a persistent storage device). A data structure may specifically include a set of data, relationships between data, and functions or instructions applicable to the data. Effectively designed data structures allow computing devices to perform computations while minimizing the use of the computing device's resources. Specifically, computing devices can increase the efficiency of operations, reading, insertion, deletion, comparison, exchange, and search through effectively designed data structures.

Data structures can be divided into linear data structures and non-linear data structures depending on the type of data structure. A linear data structure may be a structure in which only one piece of data is connected to another piece of data. Linear data structures may include List, Stack, Queue, and Deque. A list can refer to a set of data that has an internal order. The list may include a linked list. A linked list may be a data structure in which data is connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer may contain connection information to the next or previous data. Depending on its form, a linked list can be expressed as a singly linked list, a doubly linked list, or a circularly linked list. A stack may be a data listing structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (for example, inserted or deleted) at only one end of the data structure. Data stored in the stack may have a data structure (LIFO-Last in First Out) where the later it enters, the sooner it comes out. A queue is a data listing structure that allows limited access to data. Unlike the stack, it can be a data structure (FIFO-First in First Out) where data stored later is released later. A deck can be a data structure that can process data at both ends of the data structure.

A non-linear data structure may be a structure in which multiple pieces of data are connected behind one piece of data. Nonlinear data structures may include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structure may include a tree data structure. A tree data structure may be a data structure in which there is only one path connecting two different vertices among a plurality of vertices included in the tree. In other words, it may be a data structure that does not form a loop in the graph data structure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. Below, it is described in a unified manner as a neural network. Data structures may include neural networks. And the data structure including the neural network may be stored in a computer-readable medium. Data structures including neural networks also include data preprocessed for processing by a neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may include a loss function for learning. A data structure containing a neural network may include any of the components disclosed above. In other words, 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 acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may be configured to include all or any combination of the loss function for learning. In addition to the configurations described above, a data structure containing a neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include all types of data used or generated in the computational process of a neural network and is not limited to the above. Computer-readable media may include computer-readable recording media and/or computer-readable transmission media. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node.

The data structure may include data input to the neural network. A data structure containing data input to a neural network may be stored in a computer-readable medium. Data input to the neural network may include learning data input during the neural network learning process and/or input data input to the neural network on which training has been completed. Data input to the neural network may include data that has undergone pre-processing and/or data subject to pre-processing. Preprocessing may include a data processing process to input data into a neural network. Therefore, the data structure may include data subject to preprocessing and data generated by preprocessing. The above-described 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 with the same meaning.) And the data structure including the weights of the neural network may be stored in a computer-readable medium. A neural network may include multiple weights. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. Based on the weight, the data value output from the output node can be determined. The above-described data structure is only an example and the present disclosure is not limited thereto.

As an example and not a limitation, the weights may include weights that are changed during the neural network learning process and/or weights for which neural network learning has been completed. Weights that change during the neural network learning process may include weights that change at the start of the learning cycle and/or weights that change during the learning cycle. Weights for which neural network training has been completed may include weights for which a learning cycle has been completed. Therefore, the data structure including the weights of the neural network may include weights that are changed during the neural network learning process and/or the data structure including the weights for which neural network learning has been completed. Therefore, the above-mentioned weights and/or combinations of each weight are included in the data structure including the weights of the neural network. The above-described 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 (e.g., memory, 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 a different computing device and later reorganized and used. Computing devices can transmit and receive data over a network by serializing data structures. Data structures containing the weights of a serialized neural network can be reconstructed on the same computing device or on a different 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 to increase computational efficiency while minimizing the use of computing device resources (e.g., in non-linear data structures, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree) may be included. The foregoing is merely an example and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of a neural network. And the data structure including the hyperparameters of the neural network can be stored in a computer-readable medium. A hyperparameter may be a variable that can be changed by the user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle repetitions, weight initialization (e.g., setting the range of weight values subject to weight initialization), Hidden Unit. It may include a number (e.g., number of hidden layers, number of nodes in hidden layers). The above-described data structure is only an example and the present disclosure is not limited thereto.

9 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

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

Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Additionally, those skilled in the art will understand that the methods of the present disclosure can be used on single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. It will be appreciated that other computer system configurations may be implemented, including, but not limited to, each of which may operate in connection with one or more associated devices.

The described embodiments of the disclosure can 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, and such computer-readable media includes volatile and non-volatile 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 refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, 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, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed 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 example environment 1100 is shown that implements various aspects of the present disclosure, 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 processors and other multiprocessor architectures may also be used as processing unit 1104.

System bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and 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. The basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

Computer 1102 may also include an internal hard disk drive (HDD) 1114 (e.g., 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 the disk 1122 or reading from or writing to other high-capacity optical media such as DVDs). Hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to system bus 1108 by hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to. The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) 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. For computer 1102, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those of ordinary skill in the art would also recognize zip drives, magnetic cassettes, flash memory cards, and cartridges. It will be appreciated that other types of computer-readable media may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure. .

A number of program modules may be stored in 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 on various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as mouse 1140. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 1104 through an input device interface 1142, which is often connected to the system bus 1108, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

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

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, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1102. For simplicity, only memory storage device 1150 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

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

Computer 1102 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) allows connection to the Internet, etc. without wires. Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a cell tower. Wi-Fi networks use wireless 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 wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), 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, commands, information, signals, bits, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. It can be expressed by particles 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 may be used in electronic hardware, (for convenience) It will be understood that it 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 interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art of this disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

The 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 (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). 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 illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular 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, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not limited to the embodiments presented herein but is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Claims (6)

A method for calculating a work path of a task-performing robot, performed by a computing device, comprising:
determining effective work points of the task performing robot including robotic arms, joints, and task performing parts;
Modeling only the task performing part among the robot arms, joints, and task performing parts included in the task performing robot;
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;
predicting one or more impossible work paths among the generated plurality of candidate work paths based on the modeled work performance part and obstacle information;
generating a modified work path for the predicted impossible work path; and
predicting a distance between the determined effective work points based on at least one of the generated plurality of candidate work paths or the modified work path to distribute target work points to the work performing robots;
Including,
method.
According to claim 1,
The step of determining effective work points of the work performing robot is,
determining effective work points based on at least one of the posture, position, or entry availability of the work-performing robot;
Including,
method.
delete According to claim 1,
The target work point is,
Means a point at which the task performing robot actually performs the task among the determined effective work points,
method.
A computer program stored in a computer-readable storage medium, wherein the computer program, 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:
Determining effective work points of a task performing robot including robot arms, joints, and task performing parts;
An operation of modeling only the task performing part among the robot arms, joints, and task performing parts included in the task performing robot;
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;
Predicting one or more impossible work paths among the generated plurality of candidate work paths based on the modeled work performance part and obstacle information;
generating a modified work path for the predicted impossible work path; and
predicting a distance between the determined effective work points based on at least one of the generated plurality of candidate work paths or the modified work path 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 work points of the task-performing robot, including robotic arms, joints, and task-performing parts;
Modeling only the task performance part among the robot arms, joints, and task performance parts included in the task performance robot;
generate a plurality of candidate work paths for the work performing robot based on the modeled work performance part and the determined effective work points;
predicting one or more impossible work paths among the generated plurality of candidate work paths based on the modeled work performance part and obstacle information;
generate a modified work path for the predicted impossible work path; and
configured to predict a distance between the determined effective work points based on at least one of the generated plurality of candidate work paths or the modified work path, to distribute target work points to the work performing robot.
Computing device.