KR20210059460A - Remote control system for automated guided vehicle for logistics automation - Google Patents

Abstract

The present invention relates to a remote control system for an automated guided vehicle (AGV) for logistics automation. The system of the present invention comprises: a logistics processing central server for generating a top-level command to instruct a given object to be carried from any point to the other point in given working space; a remote control server for receiving the top-level command to select at least one AGV in the working space, estimate a moving line for the selected AGV, and generate operation commands with regard to the moving line and operation; a communication relay device for transmitting the operation command to at least one selected AGV; and an AGV capable of traveling on the working space automatically and configured to carry a given object, wherein the AGV automatically travels along the moving line and carries the object from one point to the other point when the operation command is received.

Description

Remote control system of autonomous robot for logistics automation {REMOTE CONTROL SYSTEM FOR AUTOMATED GUIDED VEHICLE FOR LOGISTICS AUTOMATION}

The present invention relates to a remote control system of an autonomous driving robot, and more particularly, to a system capable of remotely controlling robots that autonomously travel in a field for logistics automation.

Logistics sites where autonomous robots operate for logistics automation can be warehouses where a large number of items are stored, and items stored in the warehouse are often taken out and loaded with new items, which is recognized by autonomous robots. The shape change of the surrounding environment that should be done is very severe.

Therefore, if the autonomous driving robot uses the map constructed by using the actual measurement information of the distribution site at a specific time, a situation in which it cannot identify its own location may occur when the location of the object is changed.

As a method to overcome this problem, the recently developed autonomous driving robot has a laser distance sensor that measures the distance to an object in a corresponding direction by irradiating a laser at various angles from the center of itself, and the measured It is configured to travel between objects using distance information. To this end, each autonomous robot must have a map corresponding to the site in advance, and based on the distance information measured in real time, the position of the object and its own position in the map are estimated in real time, and the estimated self position It is configured to perform autonomous driving in the space based on.

As a technology for safely driving between objects at the logistics site, Registration Patent No. 10-1912361 (Name: Autonomous driving robot for logistics automation and environmental recognition and self-location estimation method of the robot) (hereinafter referred to as'prior art '). The self-driving robot in the prior art includes: creating a grid-based measurement map in which the shape of the surrounding environment is converted into information using the measured distance information while traveling in a distribution space to be autonomously driven; Constructing a modified map by adding a virtual wall displaying an area limiting driving on the measured map; In the case of actual driving, N candidate points of their own location are set in the modified map (the N is a natural number of 1 or more), and calculated distance information calculated in a specific direction in the modified map at each of the set N candidate points and the specified The autonomous driving is performed by comparing the measured distance information in the direction, selecting one or more candidate points, and estimating the self position based on the selected candidate points.

However, the movement of such an autonomous robot must also be controlled by a remote central control system. For example, a command such as "Move object M from point A to point B" must be received from the central control system.

When giving such a command to each autonomous robot, the central control system receives information on the location of each autonomous robot, selects the optimal robot to perform the command, and transmits the command to the selected robot. Communication can take place in a way.

However, as the number of autonomous robots managed by one central control system increases, the time required for communication increases, so each central control system has a limit on the number of robots that can be managed.

An object of the present invention is to implement a remote control system capable of controlling a distribution environment by efficiently managing a plurality of autonomous robots by improving communication speed and improving information processing procedures.

The remote control system of an autonomous driving robot for logistics automation according to the present invention comprises: a logistics processing central server for generating a top command to transport a predetermined object from a certain point to another point in a predetermined work space; A remote control server configured to receive the highest command, select at least one autonomous robot in the work space, calculate a moving movement line of the selected autonomous driving robot, and generate a work command including the movement line and work details; A communication relay device that transmits the work command to at least one selected autonomous robot; The self-driving robot capable of autonomously driving the work space and configured to transport a predetermined object, and when receiving the work command, transports the object from the arbitrary point to the other point while autonomously driving along the movement line. Self-driving robot; includes.

In particular, communication between at least the remote control server and the communication relay device, or communication between the communication relay device and the autonomous driving robot, or communication from the remote control server to the autonomous driving robot, is a fifth generation mobile communication ( It is characterized in that it is implemented in a 5G) manner.

Further, the remote control server is characterized in that it is virtually implemented in a cloud provided by a cloud storage means/server provider.

According to the remote control system of the autonomous driving robot for logistics automation according to the present invention having the configuration as described above, since it is possible to quickly communicate with a plurality of autonomous robots using high-speed communication, one remote control server (RCS ), it is possible to efficiently control a number of autonomous robots.

In addition, since the remote control server can be integrated and configured, the server construction cost is reduced. In addition, it is possible to integrate and control the wide-area logistics environment.

1 is a diagram showing a configuration concept of a control system for an existing autonomous driving robot.
2 is a diagram showing a configuration concept of a remote control system for an autonomous driving robot according to the present invention.
3 is a diagram showing the configuration and functions of an autonomous driving robot according to an embodiment of the present invention.
4 is a flowchart illustrating an autonomous driving method of the autonomous driving robot.
5 is a view for explaining a method of safely autonomous driving by estimating its own position in real time in a distribution site by the autonomous driving robot illustrated in FIG. 4.

Hereinafter, a preferred embodiment of a remote control system of an autonomous driving robot for logistics automation according to the present invention will be described with reference to the accompanying drawings. For reference, the terms referring to each component of the present invention are exemplarily named in consideration of their functions, and thus the technical content of the present invention should not be predicted and limited by the term itself.

First, an existing control environment of a control system for an autonomous driving robot will be described with reference to FIG. 1. The current central control system has a remote control server (RCS server) 20 for each predetermined partitioned space at each site. The remote control server is connected by wire to a distribution processing central server (HOST) 10, which is an upper device. In addition, the remote control server 20 is wirelessly connected to one or a plurality of client devices (RCS clients) 31, 32, and 33 in the managed site.

The distribution processing central server 10 can control a plurality of remote control servers, generate the highest command to move an object from an initial point to another target point, and transmit it to a remote control server installed in a corresponding space. do.

The remote control server 20 manages the positions, movements, and tasks of a plurality of autonomous driving robots (AGVs) 101, 102, 103 in the work space to which it belongs, and provides each autonomous driving robots constituting a unit group. Send work orders individually. The remote control server 20 is capable of communicating with communication relay devices (RCS clients) arranged in various places in the work space through, for example, WiFi wireless communication.

The remote control server 20 can inquire and manage the operation status, error status, facility status, etc. of each of the autonomous driving robots being managed, and also, the work history of the autonomous driving robots 101, 102, 103 And error history can be inquired and managed. You can also monitor and adjust the status of commands in progress. In particular, the remote control server may determine a movement path to predict and avoid a collision so that a plurality of autonomous robots 101, 102, and 103 can cooperate to perform a command within a managed work space.

The communication relay devices 31, 32, and 33 directly deliver a work command provided by the remote control server 20 to each of the autonomous driving robots 101, 102, and 103. To this end, the communication relay devices 31, 32, and 33 can communicate with each of the autonomous driving robots 101, 102, and 103 through WiFi wireless communication.

The autonomous driving robots 101, 102, and 103 autonomously drive and transport a specified object from a specified initial point to a specific target point according to a work command received wirelessly.

In such a communication/network/system configuration, since one remote control server must continuously communicate with a plurality of autonomous robots in real time, there is always a load on the communication. In particular, in the current communication/network/system configuration, since the remote control server and the communication relay device, and the communication relay device and each autonomous robot are using WiFi wireless communication, a limit of communication speed is encountered, and thus, There may be a limit to the number of autonomous robots that can be managed by one remote control server.

Therefore, currently, one server for the central control system was installed and operated for each warehouse or for each partitioned space of each warehouse.

Meanwhile, a configuration concept of a remote control system for an autonomous driving robot according to the present invention will be described with reference to FIG. 2. The basic concept of the remote control system according to the present invention is to increase the speed of wireless communication from the remote control server 25 to the autonomous robot 100 so that one remote control server 25 can control more robots. In particular, by implementing the remote control server 25 in the cloud, the need to install the remote control server 25 for each work space is eliminated.

Referring to the drawings, the distribution processing central server 15 is located in a remote location, and the remote control server 25 may be virtually implemented in the cloud of a cloud storage means/server provider through a high-speed communication network.

In addition, the remote control server 25 and the communication relay devices 35, 36, and 37 installed in each work space may be connected through a high-speed communication network. The ultra-high-speed communication network in the present invention may be, for example, a 5G mobile communication (5G) system.

5G is a mobile communication technology with a maximum speed of 20 Gbps, which is 20 times faster than LTE, a fourth-generation mobile communication, and has a processing capacity of 10 Mbps/m2, which is 100 times more. In addition, the mobility is 500Km/h and the delay time is within 1ms. The strengths of 5G are ultra-low latency and ultra-connectivity, and through this, virtual reality, autonomous driving, and Internet of Things technologies, which are the core technologies of the 4th industrial revolution, can be implemented.

Therefore, according to the remote control system of the autonomous driving robot according to the present invention, compared to the existing control system, one remote control server can control up to 20 times more autonomous robots. In addition, since one remote control server implemented virtually can be used to replace a plurality of real remote control servers, the system configuration cost can be significantly reduced.

Next, a configuration of an autonomous driving robot and an autonomous driving method will be described with reference to FIGS. 3 to 5. The technical contents described herein can be understood by referring to the contents of Registration Patent No. 10-1912361.

The autonomous driving robot 100 may largely include a laser distance sensor 110, a driving means 125, an encoder 120, and a computer 150.

The autonomous driving robot 100 is for transporting an object by autonomously driving a work space, and may be implemented in a form capable of placing an object on the upper part or a form capable of towing another transport vehicle.

The laser distance sensor 110 is a means for identifying objects, objects, or obstacles in the front, rear, left, right, up and down directions of the robot 100 and measuring the distance thereto. The laser distance sensor 110 measures a distance using a laser, but this is only an example, and may include various types of distance sensors such as ultrasonic waves, sound waves, ultraviolet rays, infrared rays, and photographed images.

The general laser distance sensor 110 may be configured to irradiate a laser in a range of about 270° to the left and right of the facing direction every 0.5°, and to output distance information indicating the measured distance. However, in the present invention, as shown in Fig. 1(a), it is assumed that only distance information of left (0°), front (90°) and right (180°) in the horizontal direction is used.

The driving means 125 may include, for example, one or a plurality of wheels or caterpillar or legs, and a motor or link for rotating or driving the same.

The encoder 120 may generate movement distance information by measuring a rotation amount of a motor or a wheel and a movement distance of a leg. Alternatively, the encoder 120 may calculate the moving distance by directly analyzing an image photographed around the surrounding area.

The computer 150 receives a work instruction from a remote control server to transport a specific object from a specific point to another point. The work command may include information on a path connecting each point. The computer performs autonomous driving according to the guided path, but controls the driving in real time so as not to collide with objects and/or other autonomous driving robots in the vicinity.

To this end, the computer 150 obtains the measured distance information generated by controlling the laser distance sensor 110, controls the operation of the driving means 125 to autonomously run the robot 100, and the encoder 120 It obtains the moving distance information from and processes the environmental recognition and self-location estimation method according to the present invention to be described later to perform a function of estimating the current self-position of the autonomous driving robot 100.

1(b) shows an exemplary form of an autonomous driving robot for logistics automation according to the present invention.

The autonomous driving robot 100 may be provided with a pedestal on which an object can be placed. In the vicinity of the pedestal, a conveying mechanism for conveying or taking objects to or from the shelf may be disposed (not shown).

The laser distance sensor 110 may be disposed at least in front of the autonomous driving robot 100.

A computer may be mounted inside the autonomous driving robot 100. In another embodiment, the self-position estimation function and the driving means control function performed by the computer may be processed by the remote control server 20 connected through a network.

Next, an autonomous driving method of the autonomous driving robot will be described with reference to FIG. 4. In particular, a method of environmental recognition and self-location estimation of an autonomous driving robot will be described.

First, the environmental recognition and self-location estimation method of the autonomous driving robot 100 is a work space (S) using distance information by the laser distance sensor 110 accumulated in the autonomous driving robot 100 in an arbitrary movement process. Creating a grid-based initial measurement map related to the surrounding environment of the vehicle; Constructing a revised map by modifying and supplementing the grid-based initial measurement map based on prior information on a space where objects are frequently moved; The autonomous driving robot 100 estimates the current magnetic position by comparing the measured distance information measured in real time by the laser distance sensor 110 and the distance information calculated by referring to the modified map, and estimates the estimated magnetic position. Based on the actual work space (S) may include the step of autonomous driving inside.

The first step of the method of constructing a grid-based map and estimating its own position based on the grid-based map in order for the autonomous driving robot 100 to recognize the environment is by using a laser distance sensor during the movement process. It includes creating a grid-based initial measurement map that distinguishes and displays walls, furniture, pillars, objects, or obstacles of the surrounding environment using the measured distance information.

The initial actual survey map is a grid-based map composed of grids having an arbitrary scale. The space (S) in which the autonomous robot 100 moves is divided into small grids, and each grid includes autonomous driving such as walls, logistics equipment, and goods. The probability that there is an object to be avoided by the robot is displayed as a probability (a modified map described later may also be the same grid-based map). In this way, a map configured based on a grid can simply display the actual surrounding shape with a grid arranged in two dimensions and the value of each grid (for example, having a value such as 1 bit or 4 bits). It has the advantage of taking advantage of the laser distance measurement sensor mounted on the robot and quickly reflecting changes in the environment on the map. The value of each grid of the grid-based map composed of a two-dimensional grid may be continuously updated by accumulating distance information from the surrounding environment to each object received while the autonomous driving robot 100 moves.

In the grid-based map, each grid is represented by m, and the grids are set to consist of N _{grids of m 1} , m ₂ , ... m _N. Each grid m _i can have a probability value between 0 and 1, and this probability value can be expressed as _{p(m i ).} In the grid, the _{closer p(m i} ) is to 0, the more likely it is an empty space without obstacles, and the closer it is to 1, the more likely it is a space with obstacles.

When the position and direction of the autonomous driving robot 100 is displayed as a state variable x and the measured value of the laser distance sensor is displayed as z, the state variable and measured value accumulated up to time t are respectively displayed as x _1:t and z _1:t Can be. Therefore, the probability value of each grid of the grid-based map can be expressed as follows.

In the case of the initial state of t=1, since there is no information previously input for each grid of the grid-based map, the median value of the probability of 0.5 is designated for all grids.

Thereafter, the probability value of each grid is adjusted by applying the distance information measured while the autonomous driving robot 100 is traveling, and as a result, a measured map is generated (not shown).

The second step of the method of recognizing the environment and estimating the self-location of the autonomous driving robot includes modifying the initial measured map created by the actual measurement of the autonomous driving robot 100 in consideration of the variability of the logistics environment.

Distribution sites generally have a number of shelves supported by pillars, and items are placed on the shelves and stored. These items can be removed from the shelf at any time or supplemented even while the autonomous robot is traveling in another location. If the object moves like this, maps and objects created and stored before the object movement occurs. There will be a difference between the measured distance information after the movement of.

The autonomous driving robot 100 can move by estimating its own location by applying distance information measured in real time to a map stored therein. When applied to a map in which is stored, there is a possibility that it may not be possible to accurately identify its location on the map.

Therefore, for the area where the object may move, a virtual barrier wall (or virtual wall) based on a fixed object that does not change steadily even if the object moves from among the measured obstacles on the initial measurement map, is initially used. It can be set in addition to the actual map. The setting of the virtual wall is performed manually by the manager of the autonomous driving robot 100, or the computer 150 mounted on the autonomous driving robot 100 or the control server connected to the network connected to the autonomous driving robot 100 is self-determined. Can be done automatically by Here, the map in which the virtual wall is added to the initial actual survey map is referred to as a modified map.

In the present invention, the autonomous driving robot is preferably driven by a modified map.

The virtual wall may be set by connecting columns of shelves, for example, in a distribution environment (not shown). On the other hand, in the area where the virtual wall is set, the distance information measured by the laser distance sensor 110 and the calculated distance information calculated from the corrected map are different from each other, so that the traveling robot 100 may not be able to specify its own position. have. Therefore, the autonomous driving robot 100 must estimate its own position in a different way when moving the area.

The third step of the method for environmental recognition and self-location estimation of the autonomous driving robot is, the autonomous driving robot 100 sets and sets a plurality of candidate points for the current self-location on the modified grid-based map (i.e., modified map). Comparing the calculated distance information calculated from the candidate points with the actual distance information measured by the laser distance sensor 110 measured in real time, and driving while estimating a self position in real time based on one or more of the candidate points.

Autonomous driving based on a grid-based map of the autonomous driving robot 100 is basically based on a Monte Carlo localization method. This method uses a particle filter method as its main algorithm and includes the following steps. The method is described with reference to FIG. 5.

The autonomous driving robot 100 is initialized after power is applied (S10), and thereafter, predicts a plurality of candidate points for its own position in each control cycle (predict) (S20) → each of the candidate points Update (S30) → select one or more candidate points to estimate your position and posture (pose estimate) (S40) → continue to estimate the next position again based on the estimated position Steps of re-sampling a plurality of candidate points (S50) are performed. In particular, steps (S20) to (S50) will be repeated.

By this procedure, the robot 100 can estimate its own position on the revised map, determine the moving direction and the moving distance, etc. by referring to the revised map, and control the operation of the driving means 125 to create a working space. It will be possible to drive autonomously.

(1) Initialize step (S10)

In the initializing step, when power is applied while the autonomous driving robot 100 is stopped, the current self position is specified as a predefined initial position (eg, a charging place).

(2) Predict step (S20)

로 표시한다. When the autonomous driving robot 100 moves in a certain direction by a certain distance, the moving distance may be calculated by referring to the measured value of the encoder 120 mounted on the wheels of the autonomous driving robot 100. This calculated travel distance is called odometry information

Denoted by

라고 하면, 각 파티클은 다음과 같이 계산된다. 여기서, 노이즈가 추가됨으로써, 오도메트리 정보가 결합된 위치의 주변에 설정된 가상의 파티클들이 자기위치의 후보지점으로 설정된다. The odometry information may be combined with an initial position or a position of the autonomous driving robot 100 determined in a previous position estimation calculation period. In addition, noise by a Gaussian random function is added to a new location where odometry information is combined with the previous location to form N particles. This noise

Then, each particle is calculated as follows. Here, by adding noise, virtual particles set in the vicinity of the location where the odometry information is combined are set as candidate points of their own location.

In the case of the autonomous driving robot 100 according to an embodiment of the present invention, it is assumed that N (for example, 50) particles are used. Each particle may mean a position and posture of the autonomous driving robot 100 that have not been determined yet, but are close to an actual position.

라 하고, 그 파티클의 가중치를

라 하고, 자율주행 로봇(100)의 격자기반 지도상의 위치좌표를

,

라 하고, 평면상의 자세를

라 하면, 시간 t에서의 가중치가 포함된 파티클들 S_t를 다음과 같이 표시할 수 있다.At this time, the i-th particle at time t

And the weight of the particle

And the position coordinates on the grid-based map of the autonomous driving robot 100

,

And the posture on the plane

_{Then, particles S t} including weights at time t can be expressed as follows.

In the initial state, the position of the autonomous driving robot 100 will be a previously given position, and the weights of the initially generated particles are all the same. That is, the weights of each of the initially generated N particles, that is, 50 particles in the initial state, will all be the same.

(3) update step (S30)

Among the candidates for the position and posture of the autonomous driving robot 100 estimated in the prediction step, that is, among 50 particles, the weight of each particle is changed according to a degree corresponding to the measured value of the current laser distance sensor 110, and , Select one or more particles close to their position according to the order of weights.

The laser distance sensor 110 mounted on the autonomous driving robot 100 typically has a detection range of 180 degrees or more in the horizontal direction and spans a wide angular range of approximately 270 degrees. In addition, since such a conventional laser distance sensor 110 is measured by subdividing the entire sensing range into several to dozens per 1° angle, hundreds to thousands of distance information are generated even with one measurement over the entire sensing range. Can be. In addition, since the laser distance sensor 110 can repeat the measurement of the entire sensing range several to tens of times per second, estimating the magnetic position using all the distance information thus generated is the computer 150 of the autonomous driving robot. It becomes a big burden to

For this reason, the autonomous driving robot 100 according to an embodiment of the present invention has a left direction (eg, 0°), a front direction (eg, 90°), and a right direction (eg, 180°). Only distance information measured in three directions of) can be used.

On the other hand, in order to estimate the current position of the autonomous driving robot 100 by moving, 50 particles (particles are first generated by adding noise to a new position created by combining the next odometry information with the previous position) When the same weight is set), the position of each particle is specified in the revised map, and 3-way distance information is calculated in the revised map around the specified locations (ie, calculated distance information). 50 calculation distance information can be calculated.

Then, the autonomous driving robot 100 compares the currently measured 3-way distance information with each (for example, 50 each) calculated distance information, and according to the degree of the error (or distance difference), each particle is Adjust the weights. For example, the smaller the error, the larger the weight can be adjusted.

)과의 거리차이

은 다음과 같이 계산할 수 있다.Among the measured distance values measured by the autonomous driving robot 100 while driving, the distance in the forward direction is set as l _front , the distance in the right direction is set as l _right , and the distance in the left direction is set _{as l left.} Then, the calculated distance information (front, right, left) calculated from each particle in the modified map is set _{as vl front} , vl _{right and} vl _left. If so, the actual distance information at the position of the current autonomous driving robot 100 and an arbitrary particle (

) And distance difference

Can be calculated as follows:

When the distance difference is calculated, the set of particles S _i can be summarized as follows.

는 거리차이

에 의하여 결정된다. 거리차이가 작은 파티클은 가중치가 크고, 거리차이가 큰 파티클의 가중치가 작다. 가중치가 큰 파티클의 위치가 자율주행 로봇(100)의 현재 위치일 가능성이 크다.Next, the weights are calculated. weight

Is the distance difference

Is determined by Particles with a small distance difference have a large weight, and particles with a large distance difference have a small weight. There is a high possibility that the position of the particle having a large weight is the current position of the autonomous driving robot 100.

(4) Pose estimate step (S40)

When the weight of each particle is calculated in the update step, the top n particles having the highest weight among the N particles may be selected. n is a value smaller than N and may be about 1/10. Each position of the selected n (eg, 5) particles may be averaged, and the average value may be determined as the current position and posture of the autonomous driving robot 100. By passing through the posture estimation step, the estimation of the current position of the autonomous driving robot 100 is completed.

(5) Resampling step (S50)

Resampling is for estimating a new position (the next position from the previously estimated magnetic position) when the robot 100 further proceeds from the currently estimated magnetic position, and the next odometry information is stored in the previous position. This is the step of combining and adding noise to create new particles. In other words, in the posture estimation step, particles with low weight are deleted, and new particles are created from particles that have not been deleted because of the high weight (ie, particles used in the average calculation).

That is, the previously selected n particles are left and the remaining (Nn) particles are deleted, and odometry information and noise are combined using each of the selected n particles to create a plurality of particles, and a total of N new particles Is created. The same weight is again set for the N particles generated in this way, and is used as a new position candidate for the autonomous driving robot 100. And it repeats from the update step.

For example, when the top 5 particles with high weights are selected from the first 50 particles and the current position is determined by the average value, the remaining 45 particles are deleted. Then, odometry information of the autonomous driving robot 100 is obtained, and the obtained odometry information is combined with respect to each of the five selected particles to generate five new positions. And, by adding noise to each new position to create 10 new particles, a total of 50 particles are generated. Thereafter, the calculated distance information may be calculated from each particle, and these values may be compared with the measured distance information of the laser distance sensor 110 to adjust the weight. Finally, five particles with high weights are selected, and a new magnetic position is estimated based on them.

The autonomous driving robot 100 repeats the steps of prediction → update → posture estimation → resampling, as described above, in real time and continuously estimating its position on the corrected map, and moving direction based on the estimated self position. And autonomous driving by setting the moving distance.

A comprehensive description of the remote control system of the autonomous driving robot according to the present invention as described above is as follows.

In the past, for the control of autonomous cooperative robots, the logistics processing central server (HOST) and the remote control server (RCS server) at the top of each work space are connected by wire, and the remote control server and the communication relay device (RCS client) are Connected by wireless (WiFi). In this structure, computer hardware was required to install a remote control server for each work space, and a large cost was incurred as a license cost of a program for implementing the server function.

However, according to the present invention, a remote control server can be virtually implemented using a cloud edge server of a communication company that installs a high-speed communication network installed in each area and industrial complex. In addition, as the remote control server is built virtually in the cloud, it is possible to provide customized functions and interface to each customer and each workspace. In addition, the customer can receive the function of the RCS server only by paying a certain cost on a monthly or annual contract, thereby reducing the cost of implementing the server's HW.

In this way, by utilizing all the advantages of large capacity, ultra-low latency, and multiple access, which are the characteristics of the 5th generation communication method (5G), a real system installed in the field can be virtualized in the cloud to be wireless and remote. As a result, the program license cost can be reduced, and the cost burden when constructing a smart factory can be reduced, which is advantageous for the popularization of autonomous driving cooperative robots.

Claims (3)

A distribution processing central server for generating a top-level command for transporting a predetermined object from an arbitrary point to another point in a predetermined work space;
A remote control server configured to receive the highest command, select at least one autonomous robot in the work space, calculate a moving movement line of the selected autonomous driving robot, and generate a work command including the movement line and work details;
A communication relay device that transmits the work command to at least one selected autonomous robot;
The self-driving robot capable of autonomously driving the work space and configured to transport a predetermined object, and when receiving the work command, transports the object from the arbitrary point to the other point while autonomously driving along the movement line Autonomous driving robot; including, a remote control system of an autonomous driving robot for logistics automation. The method of claim 1,
At least communication between the remote control server and the communication relay device, or communication between the communication relay device and the autonomous driving robot, or communication from the remote control server to the autonomous driving robot, is a fifth generation mobile communication (5G) A remote control system of an autonomous driving robot for logistics automation, characterized in that it is implemented in a manner. The method according to claim 1 or 2,
The remote control server is a remote control system of an autonomous driving robot for logistics automation, characterized in that virtually implemented in a cloud provided by a cloud storage means/server provider.

Images