KR20210081578A - Multi-leg robot - Google Patents

Abstract

The present invention relates to a multi-legged robot, comprising: a left joint to which a left leg is connected; a left body to which the left joint is connected; a right joint to which a right leg is connected; a right body to which the right joint is connected; and an adjustment mechanism connected to the left body and the right body, respectively, to adjust a distance between the left body and the right body. A distance between a pair of legs of the robot can be easily changed.

Description

Multi-leg robot

The present invention relates to a multi-legged robot capable of walking with a plurality of legs.

A robot is a machine that automatically processes or operates a given task based on its own capabilities, and the application fields of the robot can be broadly classified into industrial, medical, space, submarine, etc., and can be used in various fields.

The robot may include a walking robot capable of walking with a plurality of legs, and an example of such a walking robot may be a quadrupedal walking robot disclosed in Republic of Korea Patent Publication No. 10-1908215 (October 16, 2018 announcement). The quadruped walking robot calculates the center of gravity, sets the setting area that is the range of the allowable angle of the center of gravity, obtains whether the center of gravity is located within the setting area, and controls the center of gravity to be included in the setting area can be

Republic of Korea Patent Publication No. 10-1908215 (Announced on October 16, 2018)

An object of the present invention is to provide a multi-legged robot capable of easily changing the height of the center of gravity and the distance between the pair of legs.

Another object of the present invention is to provide a multi-legged robot capable of minimizing the gap between a pair of legs while lowering the height of the center of gravity.

A multi-legged robot according to an embodiment of the present invention includes: a left joint to which a left leg is connected; a left body to which the left joint is connected; the right joint to which the right leg is connected; a right body to which the right joint is connected; It may include an adjustment mechanism connected to the left body and the right body, respectively, to adjust the distance between the left body and the right body.

In the multi-legged robot, the front assembly and the rear assembly may be connected by a pair of side frames.

Each of the front assembly and the rear assembly includes a left joint; left body; right joint; It may include a right body and an adjustment mechanism.

The pair of side frames may include a left frame coupled to the left body and a right frame coupled to the right body.

The adjustment mechanism may include a pair of link assemblies connected to each of the left body and the right body.

A pair of link assemblies may be connected to the left body and the right body to be vertically spaced apart from each other. The pair of link assemblies may include a lower link assembly and an upper link assembly. The overall length of the lower link assembly may be longer than the overall length of the upper link assembly.

A link driving mechanism for bending or unfolding the pair of link assemblies may be mounted on the pair of link assemblies. The link drive mechanism may constitute an adjustment mechanism.

The adjusting mechanism includes an actuator installed on any one of the pair of link assemblies; a screw rotated by an actuator; It may include an elevating body provided on the other one of the pair of link assemblies and engaged with the screw to move up and down along the screw. The actuator, the screw and the elevating body may constitute a link driving mechanism.

Each of the pair of link assemblies may include a first link, a second link rotatably connected to the first link and the left body, and a third link rotatably connected to the first link and the right body.

The actuator may be installed in the lower link assembly, and the elevating body may be installed in the upper link assembly.

The actuator may be installed on the first link of the lower link assembly, and the elevating body may be installed on the first link of the upper link assembly.

The actuator may be installed under the first link of the lower link assembly, and the screw may pass through the first link of each of the pair of link assemblies.

The multi-legged robot may further include a processor for controlling actuators of each of the front assembly and the rear assembly.

The actuator may be controlled in a plurality of modes, and the plurality of modes include a first mode in which the left body and the right body are spaced apart by a first distance, and a second mode in which the left body and the right body are spaced apart by a second distance shorter than the first distance. may include.

The first mode may be implemented when the multi-legged robot walks at a low speed less than a set speed.

The second mode may be implemented when the multi-legged robot walks at a high speed over a set speed.

According to an embodiment of the present invention, the gap between the pair of legs can be widened or narrowed, so that it is possible to stably walk in a narrow space.

In addition, the height of the center of gravity can be easily adjusted, and more stable walking is possible.

In addition, since the height of the center of gravity of the multi-legged robot can be lowered during high-speed walking, more stable high-speed walking is possible and the possibility of overturning during high-speed walking can be minimized.

1 is a diagram showing an AI device including a robot system according to an embodiment of the present invention;
2 is a diagram showing an AI server connected to a robot system according to an embodiment of the present invention;
3 is a diagram showing an AI system including a robot system according to an embodiment of the present invention;
4 is a perspective view of a multi-legged robot according to an embodiment of the present invention;
5 is a perspective view when the outer body shown in FIG. 4 is separated;
6 is a perspective view of a front assembly according to an embodiment of the present invention;
7 is a front view when the gap between the left leg and the right leg is wide according to an embodiment of the present invention;
8 is a front view showing the adjustment mechanism when the interval between the left leg and the right leg is wide according to an embodiment of the present invention;
9 is a front view when the gap between the left leg and the right leg is narrow according to an embodiment of the present invention;
10 is a plan view when the multi-legged robot according to an embodiment of the present invention walks in the first mode;
11 is a plan view when the multi-legged robot according to an embodiment of the present invention walks in a second mode;
12 is a plan view when the multi-legged robot according to an embodiment of the present invention is in a third mode.

Hereinafter, specific embodiments of the present invention will be described in detail with drawings.

<Robot>

A robot can mean a machine that automatically handles or operates a task given by its own capabilities. In particular, a robot having a function of recognizing an environment and performing an operation by self-judgment may be called an intelligent robot.

Robots can be classified into industrial, medical, home, military, etc. depending on the purpose or field of use.

The robot may be provided with a driving unit including an actuator or a motor to perform various physical operations such as moving the robot joints. In addition, the movable robot includes a wheel, a brake, a propeller, and the like in the driving unit, and can travel on the ground or fly in the air through the driving unit.

<Artificial Intelligence (AI)>

Artificial intelligence refers to a field that studies artificial intelligence or methodologies that can make it, and machine learning refers to a field that defines various problems dealt with in the field of artificial intelligence and studies methodologies to solve them. do. Machine learning is also defined as an algorithm that improves the performance of a certain task through constant experience.

An artificial neural network (ANN) is a model used in machine learning, and may refer to an overall model having problem-solving ability, which is composed of artificial neurons (nodes) that form a network by combining synapses. An artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process that updates model parameters, and an activation function that generates an output value.

The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include neurons and synapses connecting neurons. In the artificial neural network, each neuron may output a function value of an activation function for input signals, weights, and biases input through synapses.

Model parameters refer to parameters determined through learning, and include the weight of synaptic connections and the bias of neurons. In addition, the hyperparameter refers to a parameter to be set before learning in a machine learning algorithm, and includes a learning rate, the number of iterations, a mini-batch size, an initialization function, and the like.

The purpose of learning the artificial neural network can be seen as determining the model parameters that minimize the loss function. The loss function may be used as an index for determining optimal model parameters in the learning process of the artificial neural network.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.

Supervised learning refers to a method of training an artificial neural network in a state where a label for training data is given, and the label is the correct answer (or result value) that the artificial neural network should infer when the training data is input to the artificial neural network. can mean Unsupervised learning may refer to a method of training an artificial neural network in a state where no labels are given for training data. Reinforcement learning can refer to a learning method in which an agent defined in an environment learns to select an action or sequence of actions that maximizes the cumulative reward in each state.

Among artificial neural networks, machine learning implemented as a deep neural network (DNN) including a plurality of hidden layers is also called deep learning (deep learning), and deep learning is a part of machine learning. Hereinafter, machine learning is used in a sense including deep learning.

<Self-Driving>

Autonomous driving refers to a technology that drives by itself, and an autonomous driving vehicle refers to a vehicle that runs without a user's manipulation or with a minimal user's manipulation.

For example, autonomous driving includes technology for maintaining a driving lane, technology for automatically adjusting speed such as adaptive cruise control, technology for automatically driving along a predetermined route, technology for automatically setting a route when a destination is set, etc. All of these can be included.

The vehicle includes a vehicle having only an internal combustion engine, a hybrid vehicle having both an internal combustion engine and an electric motor, and an electric vehicle having only an electric motor, and may include not only automobiles, but also trains, motorcycles, and the like.

In this case, the autonomous vehicle may be viewed as a robot having an autonomous driving function.

1 is a diagram illustrating an AI device including a robot system according to an embodiment of the present invention.

AI device 100 is TV, projector, mobile phone, smartphone, desktop computer, notebook computer, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), navigation, tablet PC, wearable device, set-top box (STB) ), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, etc., may be implemented as a fixed device or a movable device.

Referring to FIG. 1 , the AI device 100 includes a communicator 110 , an input interface 120 , a learning processor 130 , a sensor 140 , an output interface 150 , a memory 170 and a processor 180 , etc. may include.

The communicator 110 may transmit/receive data to and from external devices such as other AI devices 100a to 100e or the AI server 500 using wired/wireless communication technology. For example, the communicator 110 may transmit and receive sensor information, a user input, a learning model, a control signal, and the like with external devices.

At this time, the communication technology used by the communicator 110 includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity) ), Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.

The input interface 120 may acquire various types of data.

In this case, the input interface 120 may include a camera for inputting an image signal, a microphone for receiving an audio signal, a user input interface for receiving information from a user, and the like. Here, the camera or microphone may be treated as a sensor, and a signal obtained from the camera or microphone may be referred to as sensing data or sensor information.

The input interface 120 may acquire training data for model training and input data to be used when acquiring an output using the training model. The input interface 120 may acquire raw input data, and in this case, the processor 180 or the learning processor 130 may extract an input feature by preprocessing the input data.

The learning processor 130 may train a model composed of an artificial neural network by using the training data. Here, the learned artificial neural network may be referred to as a learning model. The learning model may be used to infer a result value with respect to new input data other than the training data, and the inferred value may be used as a basis for a decision to perform a certain operation.

At this time, the learning processor 130 may perform AI processing together with the learning processor 540 of the AI server 500 .

In this case, the learning processor 130 may include a memory integrated or implemented in the AI device 100 . Alternatively, the learning processor 130 may be implemented using the memory 170 , an external memory directly coupled to the AI device 100 , or a memory maintained in an external device.

The sensor 140 may acquire at least one of internal information of the AI device 100 , surrounding environment information of the AI device 100 , and user information by using various sensors.

At this time, sensors included in the sensor 140 include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, radar, etc.

The output interface 150 may generate an output related to visual, auditory, tactile, or the like.

In this case, the output interface 150 may include a display unit that outputs visual information, a speaker that outputs auditory information, and a haptic module that outputs tactile information.

The memory 170 may store data supporting various functions of the AI device 100 . For example, the memory 170 may store input data obtained from the input interface 120 , learning data, a learning model, a learning history, and the like.

The processor 180 may determine at least one executable operation of the AI device 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the processor 180 may control the components of the AI device 100 to perform the determined operation.

To this end, the processor 180 may request, search, receive, or utilize the data of the learning processor 130 or the memory 170, and may perform a predicted operation or an operation determined to be desirable among the at least one executable operation. It is possible to control the components of the AI device 100 to execute.

In this case, when the connection of the external device is required to perform the determined operation, the processor 180 may generate a control signal for controlling the corresponding external device and transmit the generated control signal to the corresponding external device.

The processor 180 may obtain intention information with respect to a user input and determine a user's requirement based on the obtained intention information.

In this case, the processor 180 uses at least one of a speech to text (STT) engine for converting a voice input into a character string or a natural language processing (NLP) engine for obtaining intention information of a natural language. Intention information corresponding to the input may be obtained.

At this time, at least one of the STT engine and the NLP engine may be configured as an artificial neural network, at least a part of which is learned according to a machine learning algorithm. And, at least one of the STT engine or the NLP engine is learned by the learning processor 130 or learned by the learning processor 540 of the AI server 500, or learned by distributed processing thereof. it could be

The processor 180 collects history information including the user's feedback on the operation contents or operation of the AI device 100 and stores it in the memory 170 or the learning processor 130, or the AI server 500 It can be transmitted to an external device. The collected historical information may be used to update the learning model.

The processor 180 may control at least some of the components of the AI device 100 in order to drive an application program stored in the memory 170 . Furthermore, in order to drive the application program, the processor 180 may operate two or more of the components included in the AI device 100 in combination with each other.

2 is a diagram illustrating an AI server connected to a robot system according to an embodiment of the present invention.

Referring to FIG. 2 , the AI server 500 may refer to a device that trains an artificial neural network using a machine learning algorithm or uses a learned artificial neural network. Here, the AI server 500 may be configured with a plurality of servers to perform distributed processing, and may be defined as a 5G network. In this case, the AI server 500 may be included as a part of the AI device 100 to perform at least a part of AI processing together.

The AI server 500 may include a communicator 510 , a memory 530 , a learning processor 540 , and a processor 520 .

The communicator 510 may transmit/receive data to and from an external device such as the AI device 100 .

The memory 530 may include a model storage unit 531 . The model storage unit 531 may store a model (or artificial neural network, 531a) being trained or learned through the learning processor 540 .

The learning processor 540 may train the artificial neural network 531a using the training data. The learning model may be used while being mounted on the AI server 500 of the artificial neural network, or may be used while being mounted on an external device such as the AI device 100 .

The learning model may be implemented in hardware, software, or a combination of hardware and software. When a part or all of the learning model is implemented in software, one or more instructions constituting the learning model may be stored in the memory 530 .

The processor 520 may infer a result value with respect to new input data using the learning model, and may generate a response or a control command based on the inferred result value.

3 is a diagram illustrating an AI system including a robot system according to an embodiment of the present invention.

Referring to FIG. 3 , the AI system 1 includes at least one of an AI server 500 , a robot 100a , an autonomous vehicle 100b , an XR device 100c , a smartphone 100d , or a home appliance 100e . It is connected to the cloud network 10 . Here, the robot 100a to which the AI technology is applied, the autonomous driving vehicle 100b, the XR device 100c, the smart phone 100d, or the home appliance 100e may be referred to as AI devices 100a to 100e.

The cloud network 10 may constitute a part of the cloud computing infrastructure or may refer to a network existing in the cloud computing infrastructure. Here, the cloud network 10 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

That is, each of the devices 100a to 100e and 500 constituting the AI system 1 may be connected to each other through the cloud network 10 . In particular, each of the devices 100a to 100e and 500 may communicate with each other through the base station, but may also directly communicate with each other without passing through the base station.

The AI server 500 may include a server performing AI processing and a server performing an operation on big data.

The AI server 500 includes at least one of the AI devices constituting the AI system 1, such as a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e, and It is connected through the cloud network 10 and may help at least a part of AI processing of the connected AI devices 100a to 100e.

In this case, the AI server 500 may train the artificial neural network according to a machine learning algorithm on behalf of the AI devices 100a to 100e, and directly store the learning model or transmit it to the AI devices 100a to 100e.

At this time, the AI server 500 receives input data from the AI devices 100a to 100e, infers a result value with respect to the received input data using a learning model, and provides a response or control command based on the inferred result value. It can be generated and transmitted to the AI devices 100a to 100e.

Alternatively, the AI devices 100a to 100e may infer a result value with respect to input data using a direct learning model, and generate a response or a control command based on the inferred result value.

Hereinafter, various embodiments of the AI devices 100a to 100e to which the above-described technology is applied will be described. Here, the AI devices 100a to 100e shown in FIG. 3 can be viewed as specific examples of the AI device 100 shown in FIG. 1 .

<AI+Robot>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology is applied.

The robot 100a may include a robot control module for controlling an operation, and the robot control module may mean a software module or a chip implemented as hardware.

The robot 100a obtains state information of the robot 100a by using sensor information obtained from various types of sensors, detects (recognizes) the surrounding environment and objects, generates map data, moves path and travels A plan may be determined, a response to a user interaction may be determined, or an action may be determined.

Here, the robot 100a may use sensor information obtained from at least one sensor among LiDAR, radar, and camera to determine a movement path and a travel plan.

The robot 100a may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the robot 100a may recognize a surrounding environment and an object using a learning model, and may determine an operation using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the robot 100a or learned from an external device such as the AI server 500 .

At this time, the robot 100a may perform an operation by generating a result using a direct learning model, but transmits sensor information to an external device such as the AI server 500 and receives the result generated accordingly to perform the operation You may.

The robot 100a determines a movement path and travel plan using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to apply the determined movement path and travel plan. Accordingly, the robot 100a may be driven.

The map data may include object identification information for various objects disposed in a space in which the robot 100a moves. For example, the map data may include object identification information for fixed objects such as walls and doors and movable objects such as flowerpots and desks. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

In addition, the robot 100a may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the robot 100a may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

<AI+Robot+Autonomous Driving>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology and autonomous driving technology are applied.

The robot 100a to which AI technology and autonomous driving technology are applied may mean a robot having an autonomous driving function or a robot 100a that interacts with the autonomous driving vehicle 100b.

The robot 100a having an autonomous driving function may collectively refer to devices that move by themselves according to a given movement line without user's control, or move by determining a movement line by themselves.

The robot 100a with the autonomous driving function and the autonomous driving vehicle 100b may use a common sensing method to determine one or more of a moving route or a driving plan. For example, the robot 100a having an autonomous driving function and the autonomous driving vehicle 100b may determine one or more of a moving route or a driving plan by using information sensed through lidar, radar, and camera.

The robot 100a interacting with the autonomous driving vehicle 100b exists separately from the autonomous driving vehicle 100b and is linked to the autonomous driving function inside the autonomous driving vehicle 100b or connected to the autonomous driving vehicle 100b. An operation associated with the user on board may be performed.

At this time, the robot 100a interacting with the autonomous driving vehicle 100b acquires sensor information on behalf of the autonomous driving vehicle 100b and provides it to the autonomous driving vehicle 100b, or obtains sensor information and obtains information about the surrounding environment or By generating object information and providing it to the autonomous driving vehicle 100b, the autonomous driving function of the autonomous driving vehicle 100b may be controlled or supported.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may monitor a user riding in the autonomous driving vehicle 100b or control a function of the autonomous driving vehicle 100b through interaction with the user. . For example, when it is determined that the driver is in a drowsy state, the robot 100a may activate an autonomous driving function of the autonomous driving vehicle 100b or assist in controlling a driving unit of the autonomous driving vehicle 100b. Here, the function of the autonomous driving vehicle 100b controlled by the robot 100a may include not only an autonomous driving function, but also a function provided by a navigation system or an audio system provided in the autonomous driving vehicle 100b.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may provide information or assist a function to the autonomous driving vehicle 100b from the outside of the autonomous driving vehicle 100b. For example, the robot 100a may provide traffic information including signal information to the autonomous driving vehicle 100b, such as a smart traffic light, or interact with the autonomous driving vehicle 100b, such as an automatic electric charger for an electric vehicle. You can also automatically connect an electric charger to the charging port.

Hereinafter, the robot 100a will be described as an example of a multi-legged robot (eg, a dog-horse type robot, etc.) capable of walking with a plurality of legs.

4 is a perspective view of a multi-legged robot according to an embodiment of the present invention, FIG. 5 is a perspective view when the outer body shown in FIG. 4 is separated, and FIG. 6 is a perspective view of a front assembly according to an embodiment of the present invention.

The multi-legged robot may include a body 200 and a plurality of legs 320 , 330 , 340 and 350 connected to the body 200 .

The body 200 may be elongated in the walking direction of the multi-legged robot. The body 200 may be composed of a combination of a plurality of members. The body 200 may be equipped with an outer body 202 that forms the outer shape of the multi-legged robot, and a driving mechanism capable of independently operating each of the plurality of legs 320 , 330 , 340 and 350 .

The drive mechanism may include an actuator, such as a motor, coupled to each of the legs 320 , 330 , 340 , 350 , such actuators having a one-to-one correspondence with the legs 320 , 330 , 340 , 350 . can be

A carrier on which an article is placed may be disposed on the upper portion of the body 200 , and the multi-legged robot may transport the article. A gripper capable of holding an article may be disposed on the body 200 , and the multi-legged robot may transport the article while the gripper holds the article.

The plurality of legs 320 , 330 , 340 , and 350 may be dispersedly connected to the body 200 . Each of the plurality of legs 320 , 330 , 340 , and 350 may be connected to the body 200 with multiple degrees of freedom.

Each of the plurality of legs 320 , 330 , 340 , and 350 may be connected to the body 200 through a joint. The legs 320, 330, 340, and 350 and the joints may have a 1:1 correspondence.

The plurality of legs 320 , 330 , 340 , and 350 may include a pair of left legs 320 , 330 and a pair of right legs 340 and 350 . The plurality of legs 320 , 330 , 340 , and 350 may include a pair of front legs 320 , 340 and a pair of hind legs 330 and 350 . The plurality of legs 320 , 330 , 340 , and 350 may include a left front leg 320 , a left hind leg 330 , a right front leg 340 , and a right hind leg 350 . In this case, the multi-legged robot may be a quadrupedal robot having four legs. Hereinafter, a quadrupedal walking robot will be described as an example. However, the present invention is not limited to a quadrupedal walking robot having four legs, but can also be applied to a multi-legged walking robot having two, six, or eight legs.

The multi-legged robot may include a front assembly (F) and a rear assembly (R).

As shown in FIG. 5 , the front assembly F and the rear assembly R may be connected by a pair of side frames 210 and 220 .

A front portion of each of the pair of side frames 210 and 220 may be connected to the front assembly F, and a rear portion of each of the pair of side frames 210 and 220 may be connected to the rear assembly R.

The front assembly F and the rear assembly R may be spaced apart from each other in the front-rear direction X while being connected to the pair of side frames 210 and 220 .

The pair of side frames 210 and 220 may be a part of the body 200 , and the front assembly F and the rear assembly R may be connected through the body 200 .

The pair of side frames 210 and 220 may be arranged to be movable in the left and right directions inside the outer body 202 . The pair of side frames 210 and 220 may be connected to the outer body 202 by a connector.

When the width in the left-right direction Y of each of the front assembly F and the rear assembly R is shortened, the distance between the pair of side frames 210 and 220 may be reduced. When the width in the left-right direction Y of each of the front assembly F and the rear assembly R is increased, the distance between the pair of side frames 210 and 220 may be increased.

The pair of side frames 210 may include a left frame 210 coupled to a left body to be described later, and a right frame 220 coupled to a right body to be described later.

The multi-legged robot may include a left joint to which the left legs 320 and 330 are connected. A pair of left joints may be provided, and a pair of left joints 350 and 360 includes an anterior left joint 350 to which the left forelimb 320 is connected, and a rear left joint 360 to which the left hind leg 330 is connected. may include. The front left joint 350 and the rear left joint 360 may be configured in the same way.

The multi-legged robot may include a right joint to which the right legs 320 and 330 are connected. A pair of right joints may be provided, and a pair of right joints 370 and 380 are a front right joint 370 to which the right front leg 340 is connected, and a right rear joint 380 to which the right hind leg 350 is connected. may include. The front right joint 370 and the rear right joint 380 may be configured in the same way.

5 and 6 , the multi-legged robot may include a left body to which a left joint is connected. A pair of left bodies may be provided, and a pair of left bodies 390 and 400 are the front left body 390 to which the front left joint 350 is connected, and the rear left body to which the rear left joint 360 is connected ( 400) may be included. The front left body 390 and the rear left body 400 may have the same configuration. The pair of left bodies 390 and 400 may be connected to the left frame 210 , and the pair of left bodies 390 and 400 and the left frame 210 may be integrated.

5 and 6 , the multifoot joint may include a right body to which the right joint is connected. A pair of right bodies may be provided, and a pair of right bodies 410 and 420 are a front right body 410 to which the front right joint 370 is connected, and a rear right body to which the rear right joint 380 is connected ( 420) may be included. The front right body 410 and the rear right body 420 may have the same configuration. The pair of right bodies 410 and 420 may be connected to the right frame 220 , and the pair of right bodies 410 and 420 and the right frame 220 may be integrated.

5 and 6 , the multi-legged robot may include an adjustment mechanism for adjusting the distance between the left body and the right body. The adjustment mechanism may be provided for each of the front assembly F and the rear assembly R, respectively. A pair of adjustment mechanisms may be provided for the multi-legged robot. The pair of adjustment mechanisms 430 and 440 may include a front adjustment mechanism 430 and a rear adjustment mechanism 440 .

The front adjustment mechanism 430 may adjust the distance between the left body and the right body of the front assembly F. The front adjustment mechanism 430 may adjust the distance between the front left body 390 and the front right body 410 .

The rear adjustment mechanism 440 may adjust the distance between the left body and the right body of the rear assembly R. The rear adjustment mechanism 440 may adjust the distance between the rear left body 400 and the rear right body 420 .

The front adjustment mechanism 430 and the rear adjustment mechanism 440 may have the same configuration.

Each of the front assembly (F) and the rear assembly (L) may include a left joint, a right joint, a left body, a right body, and an adjustment mechanism, and the front assembly (F) and the rear assembly (L) are front and rear It may be arranged symmetrically, and each configuration thereof may be the same.

Hereinafter, for convenience of explanation, the common configuration of the front left joint 350 and the rear left joint 360 will be referred to as the left joint 350 and will be described as the front right joint 370 and the rear right joint 380 . ) will be described as the right joint 370 for the common configuration.

In addition, the common configuration of the front left body 390 and the rear left body 400 will be referred to as the left body 390 and will be described with respect to the front right body 410 and the rear right body 420 , the right body 410 . ) to be described.

And, the common configuration of the front adjustment mechanism 430 and the rear adjustment mechanism 440 will be described as the adjustment mechanism 430 .

The adjustment mechanism 430 may include a pair of link assemblies UL (LL) connected to each of the left body 390 and the right body 410 . The pair of link assemblies UL (LL) may be connected to the left body 390 and the right body 410 to be vertically spaced apart from each other. The pair of link assemblies UL (LL) may include a lower link assembly LL and an upper link assembly UL.

7 is a front view when the interval between the left leg and the right leg is wide according to an embodiment of the present invention, and FIG. 8 is an adjustment mechanism when the interval between the left leg and the right leg is wide according to the embodiment of the present invention It is a front view shown, and FIG. 9 is a front view when the distance between the left leg and the right leg is narrow according to an embodiment of the present invention.

7 to 9, the adjustment mechanism 430 may be connected to the left body 390 and the right body 410, respectively, and the left body 390 and the right body 410 as shown in FIG. Together, they can be pushed away from each other. The adjusting mechanism 430 may pull the left body 390 and the right body 410 in a direction toward each other as shown in FIG. 9 .

The lower link assembly LL may be connected to each of the left body 390 and the right body 410 lower than the upper link assembly UL. The overall length of the lower link assembly LL may be longer than the overall length of the upper link assembly UL.

Each of the left portion of the lower link assembly LL and the left portion of the upper link assembly UL may be rotatably connected to the left body 390 , and the left portion of the lower link assembly LL is connected to the left body 390 . The height H1 of the connection part P1 may be lower than the height H2 of the connection part P2 in which the left side of the affine link assembly UL is connected to the left body 390 .

Each of the right portion of the lower link assembly LL and the right portion of the upper link assembly UL may be rotatably connected to the right body 410 , and the right portion of the lower link assembly LL may be connected to the right body 410 . A height H3 of the connecting portion P3 may be lower than a height H4 of the connecting portion P4 connected to the right body 410 of the right side of the affine link assembly UL.

A link driving mechanism 450 for bending or unfolding the pair of link assemblies UL (LL) may be mounted on the pair of link assemblies (UL) (LL).

The adjustment mechanism 430 may include an actuator 460 , a screw 470 , and an elevating body 480 .

The actuator 460, the screw 470, and the elevating body 480 may constitute a link driving mechanism 450, and the link driving mechanism 450 connects a pair of link assemblies UL and LL. In addition, each of the pair of link assemblies (UL) (LL) may be bent or unfolded.

The actuator 460 may be installed in any one of a pair of link assemblies (UL) (LL).

The screw 470 may be rotated by an actuator 460 .

The lifting body 480 may be provided on the other one of the pair of link assemblies UL (LL), and may be engaged with the screw 470 to be lifted along the screw 470 when the screw 470 rotates. .

Each of the pair of link assemblies LL (UL) includes a first link, a first link and a second link rotatably connected to the left body, and a third link rotatably connected to the first link and the right body. can The second link and the third link may be spaced apart with the first link therebetween, and may be symmetrical to each other.

The pair of link assemblies LL (UL) will be described with different reference numerals for each of the first link, the second link, and the third link.

The lower link assembly LL includes a first link 481 , a second link 482 rotatably connected to the first link 481 and the left body 390 , the first link 481 and the right body ( A third link 483 rotatably connected to 410 may be included.

The first link 481 of the lower link assembly LL may be a center lower link 481 , and the second link 482 of the lower link assembly LL may be a left lower link 482 , and a lower link. The third link 483 of the assembly LL may be a right lower link 483 .

The upper link assembly (UL) includes a first link 491 , a second link 492 rotatably connected to the first link 491 and the left body 390 , the first link 491 and the right body ( A third link 493 rotatably connected to 410 may be included.

The first link 491 of the upper link assembly UL may be a center upper link 491 , and the second link 492 of the upper link assembly UL may be a left upper link 492 , and an upper link. The third link 493 of the assembly UL may be a right upper link 493 .

The actuator 460 may be installed in the lower link assembly LL. The actuator 460 may be installed on the first link 481 of the lower link assembly LL. The actuator 460 may be installed under the first link 481 of the lower link assembly LL.

A lower portion of the screw 470 may be connected to the actuator 460, and the screw 470 may be elongated in the vertical direction Z. The screw 470 is a pair of link assemblies LL (UL). Each of the first links 481 and 491 may pass through.

The lifting body 480 may be installed on the upper link assembly UL. The lifting body 480 may be installed on the first link 491 of the upper link assembly UL, and may be raised and lowered along the screw 470 together with the first link 491 of the upper link assembly UL. .

When the actuator 460 rotates the screw 470 in one of the clockwise and counterclockwise directions, the elevating body 480 may be raised upwardly from the outside of the screw 470, and the elevating body 480 and the upper The first link 491 of the link assembly UL may be raised as shown in FIG. 7 . When the first link 491 of the upper link assembly (UL) is raised, the upper link assembly (UL) is spread in the left and right direction (Y), the first link 491 may be raised, the left body (390) The gap between the and the right body 410 may be widened by the first gap G1 . When the gap between the left body 390 and the right body 410 is widened by the first gap G1, the lower link assembly LL may be spread along the left body 390 and the right body 410, The first link 481 may be raised.

When the left body 390 and the right body 410 are separated by a first interval G1, the height H5 of the center of gravity C of the multi-legged robot is the first link 491 of the upper link assembly UL. The first link 481 of the lower link assembly LL may be raised as well as the first link 481 of the lower link assembly LL is raised.

On the other hand, the actuator 460 may rotate the screw 470 in the opposite direction to when the lifting body 480 is raised, and the lifting body 480 may be lowered from the outside of the screw 470 in the downward direction, The lifting body 480 and the first link 491 of the upper link assembly UL may be lowered as shown in FIG. 9 . When the first link 491 of the upper link assembly (UL) is lowered, the upper link assembly (UL) is folded in the left-right direction (Y), the first link 491 can be lowered, the left body (390) The distance between the right body 410 and the right body 410 may be narrowed by the second gap G2. When the gap between the left body 390 and the right body 410 is reduced by the second gap G2, the lower link assembly LL may be folded along the left body 390 and the right body 410, and , the first link 481 may be lowered.

When the left body 390 and the right body 410 are collected by the second interval G2, the height H6 of the center of gravity C of the multi-legged robot is the first link 491 of the upper link assembly UL. As well as being lowered, the first link 481 of the lower link assembly LL may be lowered by being lowered.

That is, according to the direction in which the actuator 460 rotates the screw 470, the height (H5) (H6) of the gap (G1) (G2) and the center of gravity (C) between the left body 390 and the right body 410 . ) can be adjusted individually.

The multi-legged robot may include a processor 180 , and in the prosensor 180 , the front assembly F may control the actuators 460 of each of the rear assemblies R.

The actuator 460 may be controlled by the processor 180 in a plurality of modes.

10 is a plan view when the multi-legged robot according to an embodiment of the present invention walks in a first mode, and FIG. 11 is a plan view when the multi-legged robot according to an embodiment of the present invention walks in a second mode.

The plurality of modes of the actuator 460 may include a first mode in which the left body 390 and the right body 410 are spaced apart by a first distance (G1, see FIG. 7 ). In the first mode, the height (H5, see FIG. 7) of the center of gravity (C) of the multi-legged robot may be high, and each of the pair of front legs 320 and 340 and the pair of hind legs 330 and 350 is As shown in FIG. 10 , they may be spaced apart by a third distance G2.

The plurality of modes of the actuator 460 may include a second mode in which the left body 390 and the right body 410 are spaced apart by a second distance G2 (refer to FIG. 9 ) shorter than the first distance G1. In the second mode, the height of the center of gravity of the multi-legged robot (H6, see FIG. 9) may be lower than the height of the center of gravity (H5) of the multi-legged robot in the first mode, and a pair of forelimbs 320 and 340 and Each of the pair of hind legs 330 and 350 may be spaced apart by a fourth distance G4 as shown in FIG. 11 .

As shown in FIGS. 10 and 11, the multi-legged robot can walk forward while moving its position to the front right or front left, and the movement distance of its center of gravity (C) is the distance between a pair of legs (G3) ( G4) may be different.

When the multi-legged robot is in the first mode, the center of gravity (C) of the multi-legged robot can be moved by the first distance (L1) in the left-right direction (Y), and when the multi-legged robot is in the second mode, the center of gravity of the multi-legged robot (C) may be moved by the second distance L2 in the left and right direction (Y).

The height of the center of gravity (C) in the first mode is higher than the height of the center of gravity (C) in the second mode, and the moving distance (H1) of the center of gravity (C) in the first mode is the center of gravity (C) in the second mode may be longer than the moving distance (H2) of

In the first mode, the possibility of the multi-legged robot overturning is high, and in the second mode, the possibility of the multi-legged robot overturning is low.

The first mode is preferably performed when the multi-legged robot walks at a low speed below the set speed, and the second mode is preferably performed when the multi-legged robot walks at a high speed above the set speed.

Here, the set speed may be a speed set lower than the maximum speed of the multi-legged robot. For example, when the maximum speed of the multi-legged robot is 40 km/h, the set speed may be 20 km/h.

The multi-legged robot can run in a low speed mode, a high speed mode, and a maximum speed mode. In the low speed mode, the multi-legged robot controls the actuators 460 of the front assembly (F) and the rear assembly (R) in the first mode. can do. In the high-speed mode, the multi-legged robot may control the actuators 460 of each of the front assembly F and the rear assembly R in the second mode.

12 is a plan view when the multi-legged robot according to an embodiment of the present invention is in a third mode.

The processor 180 may control the actuator 460 of the front assembly F in the second mode and the actuator 460 of the rear assembly ® in the first mode, and this complex mode is the third mode of the multi-legged robot. mode can be defined.

In the multi-legged robot shown in FIG. 12 , a gap G4 between a pair of front legs may be narrower than a gap G3 between a pair of hind legs.

In the multi-legged robot, a pair of front legs 320 and 340 and a pair of hind legs 330 and 350 may not collide in the front-rear direction, and the multi-legged robot may walk at a maximum speed and may sprint.

The above description is merely illustrative of the technical spirit of the present invention, and various modifications and variations will be possible without departing from the essential characteristics of the present invention by those skilled in the art to which the present invention pertains.

Therefore, the embodiments disclosed in the present invention are not intended to limit the technical spirit of the present invention, but to explain, and the scope of the technical spirit of the present invention is not limited by these embodiments.

The protection scope of the present invention should be construed by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

320: left leg 340: right leg
350: left joint 370: right through
390: left body 410: right body
430: control mechanism

Claims (20)

the left joint to which the left leg is connected;
a left body to which the left joint is connected;
the right joint to which the right leg is connected;
a right body to which the right joint is connected;
and an adjusting mechanism connected to the left body and the right body, respectively, to adjust a distance between the left body and the right body. The method of claim 1,
The control mechanism is
a pair of link assemblies connected to each of the left body and the right body;
an actuator installed on any one of the pair of link assemblies;
a screw rotated by the actuator;
and an elevating body provided on the other one of the pair of link assemblies and being meshed with the screw to move up and down along the screw. 3. The method of claim 2,
The pair of link assemblies are connected to the left body and the right body to be vertically spaced apart from each other. 4. The method of claim 3,
The pair of link assemblies includes a lower link assembly and an upper link assembly,
The actuator is installed in the lower link assembly,
The elevating body is a multi-legged robot installed in the upper link assembly. The method of claim 1,
Each of the pair of link assemblies is
a first link;
a second link rotatably connected to the first link and the left body;
A multi-legged robot including the first link and a third link rotatably connected to the right body. 6. The method of claim 5,
The pair of link assemblies includes a lower link assembly and an upper link assembly,
The actuator is installed on the first link of the lower link assembly,
The elevating body is a multi-legged robot installed on the first link of the upper link assembly. 7. The method of claim 6,
The actuator is installed under the first link of the lower link assembly,
The screw is a multi-legged robot passing through the first link of each of the pair of link assemblies. 8. The method of claim 7,
An overall length of the lower link assembly is longer than an overall length of the upper link assembly. The method of claim 1,
a left frame coupled to the left body;
The multi-legged robot further comprising a right frame coupled to the right body. the left joint to which the left leg is connected;
a left body to which the left joint is connected;
the right joint to which the right leg is connected;
a right body to which the right joint is connected;
a pair of link assemblies respectively connected to the left body and the right body;
The pair of link assemblies is equipped with a link driving mechanism for folding or unfolding the pair of link assemblies. 11. The method of claim 10,
The link driving mechanism is
an actuator installed on any one of the pair of link assemblies;
a screw rotated by the actuator;
and an elevating body provided on the other one of the pair of link assemblies and being meshed with the screw to move up and down along the screw. 12. The method of claim 11,
Each of the pair of link assemblies is
a first link;
a second link rotatably connected to the first link and the left body;
A multi-legged robot including the first link and a third link rotatably connected to the right body. 13. The method of claim 12,
The pair of link assemblies includes a lower link assembly and an upper link assembly,
The actuator is installed on the first link of the lower link assembly,
The elevating body is a multi-legged robot installed on the first link of the upper link assembly. The front assembly is connected to the rear assembly by a pair of side frames,
Each of the front assembly and the rear assembly is
The left joint is connected to the left leg, and
a left body to which the left joint is connected;
The right joint is connected to the right leg, and
a right body to which the right joint is connected;
and an adjustment mechanism connected to the left body and the right body to adjust a distance between the left body and the right body. 15. The method of claim 14,
The control mechanism is
a pair of link assemblies connected to each of the left body and the right body;
an actuator installed on any one of the pair of link assemblies;
a screw rotated by the actuator;
and an elevating body provided on the other one of the pair of link assemblies and being meshed with the screw to move up and down along the screw. 16. The method of claim 15,
The pair of link assemblies are connected to the left body and the right body to be vertically spaced apart from each other. 17. The method of claim 16,
The pair of link assemblies includes a lower link assembly and an upper link assembly,
The actuator is installed in the lower link assembly,
The elevating body is a multi-legged robot installed in the upper link assembly. 15. The method of claim 14,
The multi-legged robot in which the front assembly further includes a processor for controlling the actuators of each of the rear assemblies. 19. The method of claim 18,
The actuator is controlled in a plurality of modes,
The plurality of modes
a first mode in which the left body and the right body are spaced apart by a first distance;
and a second mode in which the left body and the right body are spaced apart by a second distance shorter than the first distance. 20. The method of claim 19,
The first mode is implemented when the multi-legged robot walks at a low speed less than a set speed,
The second mode is a multi-legged robot that is implemented when the multi-legged robot walks at a high speed above the set speed.

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