CN114564027A

CN114564027A - Path planning method of foot type robot, electronic equipment and readable storage medium

Info

Publication number: CN114564027A
Application number: CN202210267679.5A
Authority: CN
Inventors: 郑大可; 陈盛军; 肖志光
Original assignee: Shenzhen Pengxing Intelligent Research Co Ltd
Current assignee: Shenzhen Pengxing Intelligent Research Co Ltd
Priority date: 2022-03-17
Filing date: 2022-03-17
Publication date: 2022-05-31

Abstract

The application provides a path planning method of a legged robot, an electronic device and a computer readable storage medium, wherein the method comprises the following steps: acquiring a global path of the legged robot, wherein the global path comprises at least one node; constructing a local map according to the sensing information of the surrounding environment, wherein the local map covers at least one node; constructing a local accessible map based on the motion performance parameters of the legged robot and the local map; and determining a local target node of the foot type robot according to the global positioning precision, the global path, the current position and the local accessible map of the foot type robot, and planning the local path of the foot type robot based on the local accessible map, the local target node and the current position of the foot type robot. According to the method and the device, the foot type robot can conduct autonomous navigation even under the condition that the global positioning precision is abnormal.

Description

Path planning method of foot type robot, electronic equipment and readable storage medium

Technical Field

The present disclosure relates to the field of robotics, and in particular, to a path planning method for a foot robot, an electronic device, and a computer-readable storage medium.

Background

With the development of Artificial Intelligence (AI) technology, robots are increasingly widely used in daily production and life. Common robots include robotic arms, Automated Guided Vehicles (AGVs), unmanned forklifts, and the like. As the actual demand increases, the application demand of the legged robots, particularly the quadruped robots, is increasing in order to apply the robots in a wider range. Compared with a wheeled robot, the quadruped robot can cross more complex road conditions and realize more complex tasks.

Compared with an application scene of a wheeled robot, the application scene is usually in a small-range indoor environment, an important application scene of the foot type robot is a large-range outdoor environment, for example, the radius is larger than 10 kilometers, and for an outdoor environment with a large range, autonomous motion of the foot type robot is mainly navigated based on a topological map.

Disclosure of Invention

In view of the above, it is desirable to provide a path planning method for a legged robot, an electronic device and a computer readable storage medium, so as to solve the technical problem that the legged robot cannot perform autonomous navigation under the condition of reduced global positioning accuracy.

The application provides a path planning method of a legged robot, which comprises the following steps: acquiring a global path of a legged robot, wherein the global path comprises at least one node; constructing a local map according to the sensing information of the surrounding environment, wherein the local map covers at least one node; constructing a local passable map based on the motion performance parameters of the legged robot and the local map; determining a local target node of the legged robot according to the global positioning precision of the legged robot, the global path, the current position of the legged robot and the local passable map, and planning the local path of the legged robot based on the local passable map, the local target node and the current position of the legged robot.

In some embodiments, the determining a local target node of the legged robot from the global positioning accuracy of the legged robot, the global path, the current location of the legged robot, and the locally passable map, and planning the local path of the legged robot based on the locally passable map, the local target node, and the current location of the legged robot, comprises: when the global positioning precision of the foot type robot meets the preset precision requirement, determining a local target node of the foot type robot based on the global path, the current position of the foot type robot and the local accessible map; planning a local path of the legged robot based on the local map, the local passable map, the local target node, and a current position of the legged robot; when the global positioning precision of the foot robot does not meet the preset precision requirement, acquiring an object corresponding to the local target node, and calculating the pose of the object under a coordinate system of a speedometer; the object is taken as a new local target node, and a local path of the legged robot is planned based on the local passable map, the new local target node and the current position of the legged robot under the odometer coordinate system.

In some embodiments, before said obtaining the global path of the legged robot, comprising: obtaining the confidence coefficient of the initial global positioning precision of the legged robot; and when the confidence coefficient of the initial global positioning precision is greater than a preset value, acquiring initial position information, target position information and a global map of the surrounding environment, and planning a global path of the legged robot.

In some embodiments, after determining the local target node of the legged robot based on the global path, the current location of the legged robot, and the local passable map, further comprising: acquiring the ambient environment information of the local target node, extracting the semantic information of the local target node based on the ambient environment information, and recording an object corresponding to the local target node.

In some embodiments, the acquiring the object corresponding to the local target node when the global positioning accuracy of the legged robot does not meet the preset accuracy requirement includes: when the global positioning precision of the foot type robot does not meet the preset precision requirement, acquiring the current surrounding environment information of the foot type robot, and identifying a local area corresponding to the semantic information of the local target node from the current surrounding environment information; identifying an object corresponding to the local target node from the local region.

In some embodiments, the method further comprises: and when the foot type robot reaches the new local target node and the global positioning precision of the foot type robot still does not meet the preset precision requirement, outputting preset positioning abnormal information and controlling the foot type robot to exit the autonomous navigation mode.

In some embodiments, the method further comprises: when the foot robot reaches the new local target node and the global positioning accuracy of the foot robot still does not meet the preset accuracy requirement, acquiring an object corresponding to a next local target node from the global path, and calculating the pose of the object corresponding to the next local target node under the odometer coordinate system; and updating the object corresponding to the next local target node as a new local target node.

In some embodiments, the global path is obtained based on a global map plan with semantic information, and each node in the global path has corresponding semantic information.

The present application further provides an electronic device, the electronic device including: a processor; and the memorizer is stored with a plurality of program modules, and the program modules are loaded by the processor and execute the path planning method of the legged robot.

The present application also provides a computer-readable storage medium having stored thereon at least one computer instruction, which is loaded by a processor and executes the above-mentioned path planning method for a legged robot.

The path planning method, the electronic device and the storage medium of the foot robot can plan a global topological path, can identify complex terrain on a running path through sensing information of the foot robot, can intelligently analyze whether the foot robot can pass the complex terrain, plan and update a local path based on passability and real-time robot positions and target nodes of the global topological path until the foot robot reaches the target position, and correspond to different local path planning modes for the situation that global positioning accuracy is normal and the situation that the global positioning accuracy is abnormal, so that the foot robot can autonomously pass or avoid the complex terrain, and autonomously navigate under the situation that the global positioning accuracy is abnormal, and the adaptability of the foot robot to an outdoor environment is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a schematic diagram of an application environment architecture of a path planning method for a legged robot according to an embodiment of the present application.

Fig. 2 is a block diagram of a legged robot according to an embodiment of the present application.

Fig. 3 is a schematic perspective view of a legged robot according to an embodiment of the present invention.

Fig. 4 is a flowchart of a path planning method for a legged robot according to an embodiment of the present application.

Fig. 5 is a schematic diagram of a topological map provided in an embodiment of the present application.

Fig. 6 is a schematic diagram of a topological map provided in another embodiment of the present application.

Fig. 7 is a flowchart of a path planning method for a legged robot according to another embodiment of the present application.

Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Description of the main elements

Electronic equipment 1

Processor 10

Memory 20

Computer program 30

Slope filter 40

Terrain roughness filter 50

Step height filter 60

Flying floor height filter 70

Foot robot 100

Mechanical unit 101

Communication unit 102

Sensing unit 103

Interface unit 104

Memory cell 105

Display unit 106

Input unit 107

Control module 110

Power supply 111

Driving plate 1011

Motor 1012

Mechanical structure 1013

Fuselage main body 1014

Leg 1015

Foot 1016

Head structure 1017

Tail structure 1018

Carrying structure 1019

Saddle structure 1020

Camera structure 1021

Display panel 1061

Touch panel 1071

Input device 1072

Touch detection device 1073

Touch controller 1074

Server 2

DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION

The following specific examples will further illustrate the application in conjunction with the above figures.

In order that the above objects, features and advantages of the present application may be more clearly understood, a detailed description of the present application is given below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

In the following description, numerous specific details are set forth to provide a thorough understanding of the present application, and the described embodiments are merely a subset of the embodiments of the present application and are not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

In the following description, suffixes such as "module", "component", or "unit" used to represent components are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.

Fig. 1 is a schematic diagram of an application environment architecture of a path planning method for a legged robot according to a preferred embodiment of the present application.

The path planning method for the legged robot in the present application is applied to the electronic device 1, and the electronic device 1 can establish a communication connection with at least one legged robot 100 and at least one server 2 through a network. The network may be a wired network or a Wireless network, such as radio, Wireless Fidelity (WIFI), cellular, satellite, broadcast, etc. The cellular network may be a 4G network or a 5G network.

The electronic device 1 may be an electronic device installed with a path planning program, such as a smart phone, a personal computer, a server, and the like, where the server may be a single server, a cloud server, a server cluster, or the like. The server 2 may be a single server, a cloud server, a server cluster, or the like.

In some embodiments, the path planning method of the legged robot in the present application can also be applied to the legged robot 100.

Referring to fig. 2, fig. 2 is a schematic diagram of a hardware structure of a legged robot 100 according to an embodiment of the present invention. In the embodiment shown in fig. 2, the legged robot 100 includes a mechanical unit 101, a communication unit 102, a sensing unit 103, an interface unit 104, a storage unit 105, a control module 110, and a power supply 111. The various components of the legged robot 100 may be connected in any manner, including wired or wireless connections, and the like. Those skilled in the art will appreciate that the specific structure of the foot robot 100 shown in fig. 2 does not limit the foot robot 100, the foot robot 100 may include more or less components than those shown, some components do not necessarily belong to the essential structure of the foot robot 100, and some components may be omitted or combined as necessary within the scope of not changing the essence of the invention.

The following describes the components of the foot robot 100 in detail with reference to fig. 2:

the mechanical unit 101 is hardware of the foot robot 100. As shown in fig. 2, the machine unit 101 may include a drive plate 1011, a motor 1012, a machine structure 1013, as shown in fig. 3, the machine structure 1013 may include a body 1014, extendable legs 1015, feet 1016, and in other embodiments, the machine structure 1013 may further include extendable robotic arms (not shown), a rotatable head structure 1017, a swingable tail structure 1018, a load structure 1019, a saddle structure 1020, a camera structure 1021, and the like. It should be noted that each component module of the mechanical unit 101 may be one or multiple, and may be configured according to specific situations, for example, the number of the legs 1015 may be 4, each leg 1015 may be configured with 3 motors 1012, and the number of the corresponding motors 1012 is 12.

The communication unit 102 may be used for receiving and transmitting signals, and may also communicate with other devices through a network, for example, receive command information sent by a remote controller or other foot-type robot 100 to move in a specific direction at a specific speed according to a specific gait, and transmit the command information to the control module 110 for processing. The communication unit 102 includes, for example, a WiFi module, a 4G module, a 5G module, a bluetooth module, an infrared module, etc.

The sensing unit 103 is configured to acquire information data of an environment around the foot robot 100 and monitor parameter data of each component inside the foot robot 100, and send the information data to the control module 110. The sensing unit 103 includes various sensors such as a sensor for acquiring surrounding environment information: laser radar (for long-range object detection, distance determination, and/or velocity value determination), millimeter wave radar (for short-range object detection, distance determination, and/or velocity value determination), a camera, an infrared camera, a Global Navigation Satellite System (GNSS), and the like. Such as sensors monitoring various components inside the legged robot 100: an Inertial Measurement Unit (IMU) (for measuring values of velocity, acceleration and angular velocity values), a sole sensor (for monitoring sole impact point position, sole attitude, ground contact force magnitude and direction), a temperature sensor (for detecting component temperature). As for the other sensors such as the load sensor, the touch sensor, the motor angle sensor, and the torque sensor, which can be configured in the legged robot 100, the detailed description is omitted here.

The interface unit 104 may be used to receive inputs from external devices (e.g., data information, power, etc.) and transmit the received inputs to one or more components within the legged robot 100, or may be used to output to external devices (e.g., data information, power, etc.). The interface unit 104 may include a power port, a data port (e.g., a USB port), a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, and the like.

The storage unit 105 is used to store software programs and various data. The storage unit 105 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system program, a motion control program, an application program (such as a text editor), and the like; the data storage area may store data generated by the legged robot 100 in use (such as various sensing data acquired by the sensing unit 103, log file data), and the like. In addition, the storage unit 105 may include high-speed random access memory, and may also include non-volatile memory, such as disk memory, flash memory, or other volatile solid-state memory.

The display unit 106 is used to display information input by a user or information provided to the user. The Display unit 106 may include a Display panel 1061, and the Display panel 1061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.

The input unit 107 may be used to receive input numeric or character information. Specifically, the input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect a user's touch operations (e.g., operations of the user on the touch panel 1071 or near the touch panel 1071 using a palm, a finger, or a suitable accessory) and drive a corresponding connection device according to a preset program. The touch panel 1071 may include two parts of a touch detection device 1073 and a touch controller 1074. The touch detection device 1073 detects the touch orientation of the user, detects a signal caused by a touch operation, and transmits the signal to the touch controller 1074; the touch controller 1074 receives the touch information from the touch sensing device 1073, converts the touch information into touch point coordinates, and sends the touch point coordinates to the control module 110, and receives and executes commands from the control module 110. The input unit 107 may include other input devices 1072 in addition to the touch panel 1071. In particular, other input devices 1072 may include, but are not limited to, one or more of a remote control joystick or the like, and are not limited to such.

Further, the touch panel 1071 may cover the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or nearby, the touch panel 1071 transmits the touch operation to the control module 110 to determine the type of the touch event, and then the control module 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although in fig. 2, the touch panel 1071 and the display panel 1061 are two independent components to respectively implement the input and output functions, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to implement the input and output functions, which is not limited herein.

The control module 110 is a control center of the foot robot 100, connects the respective components of the entire foot robot 100 by using various interfaces and lines, and performs overall control of the foot robot 100 by running or executing a software program stored in the storage unit 105 and calling data stored in the storage unit 105.

The power supply 111 is used to supply power to various components, and the power supply 111 may include a battery and a power supply control board for controlling functions such as battery charging, discharging, and power consumption management. In the embodiment shown in fig. 2, the power source 111 is electrically connected to the control module 110, and in other embodiments, the power source 111 may be electrically connected to the sensing unit 103 (e.g., a camera, a radar, a sound box, etc.) and the motor 1012 respectively. It should be noted that each component may be connected to a different power source 111 or powered by the same power source 111.

On the basis of the above embodiments, in particular, in some embodiments, the communication connection with the foot robot 100 may be performed through a terminal device, when the terminal device communicates with the foot robot 100, the terminal device may transmit instruction information to the foot robot 100, and the foot robot 100 may receive the instruction information through the communication unit 102 and may transmit the instruction information to the control module 110 when receiving the instruction information, so that the control module 110 may process the instruction information to obtain the target velocity value. Terminal devices include, but are not limited to: the mobile phone, the tablet computer, the server, the personal computer, the wearable intelligent device and other electrical equipment with the image shooting function.

The instruction information may be determined according to a preset condition. In one embodiment, the legged robot 100 may include a sensing unit 103, and the sensing unit 103 may generate instruction information according to a current environment in which the legged robot 100 is located. The control module 110 may determine whether the current speed value of the legged robot 100 satisfies a corresponding preset condition according to the instruction information. If yes, keeping the current speed value and the current gait movement of the legged robot 100; if not, the target velocity value and the corresponding target gait are determined according to the corresponding preset conditions, so that the legged robot 100 can be controlled to move at the target velocity value and the corresponding target gait. The environmental sensors may include temperature sensors, air pressure sensors, visual sensors, sound sensors. The instruction information may include temperature information, air pressure information, image information, and sound information. The communication mode between the environmental sensor and the control module 110 may be wired communication or wireless communication. The manner of wireless communication includes, but is not limited to: wireless network, mobile communication network (3G, 4G, 5G, etc.), bluetooth, infrared.

Fig. 4 is a flowchart of a path planning method for a legged robot according to a preferred embodiment of the present application. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.

In an embodiment of the present application, the path planning method for a foot robot is applied to the electronic device 1 wirelessly connected to the foot robot 100, or is directly applied to the foot robot 100, and the path planning method for a foot robot is described below as an example of applying the method to the electronic device 1 wirelessly connected to the foot robot 100.

S401, a global path of the legged robot 100 is acquired.

In one embodiment, the global path of the legged robot 100 may be planned based on the starting position information of the legged robot 100, the target position information, and the global map of the surrounding environment. The surrounding environment of the legged robot 100 is an indoor environment, an outdoor environment, or a combination of the indoor environment and the outdoor environment, and the global map of the surrounding environment is a global topological map.

In an embodiment, the legged robot 100 may include a global positioning module, and the legged robot 100 may determine the position of itself in the global path through the global positioning module, for example, the global positioning module may obtain the position of the legged robot 100 in the global path based on information of reference objects around the legged robot, and the number of the reference objects may affect the global positioning accuracy of the global positioning module. Before performing path planning, the electronic device 1 may obtain a confidence of an initial global positioning accuracy of the global positioning module, and if the confidence of the initial global positioning accuracy is greater than a preset value, it indicates that the global positioning accuracy of the global positioning module meets an autonomous navigation requirement of the legged robot 100 (that is, the global positioning accuracy meets the preset accuracy requirement), and the electronic device 1 may perform global path planning, and plan a global path of the legged robot 100 by obtaining initial position information, target position information, and a global map of a surrounding environment of the legged robot 100. If the confidence of the initial global positioning accuracy is not greater than the preset value, it is indicated that the global positioning accuracy of the global positioning module cannot meet the autonomous navigation requirement of the foot robot 100, preset prompt information can be output, and the foot robot 100 can exit the autonomous navigation mode.

In one embodiment, obtaining the start position information, the target position information and the global map of the surrounding environment may include: before planning a path, the electronic device 1 acquires the start position information and the target position information input by the user, and downloads at least one global topological map of the indoor motion environment and/or the outdoor motion environment of the legged robot 100 from the server. In other embodiments, the electronic device 1 may also store in advance at least one global topological map of the indoor motion environment and/or the outdoor motion environment of the legged robot 100. The topological map is composed of nodes (nodes), routes (ways), relationships (relations), tags (tags), and other elements. Wherein the node represents a coordinate point of a location (e.g., coordinates of an intersection, any point on a road, a building, etc.). The route is composed of a number of nodes to represent streets, parks, sidewalks, etc. The relationships represent relationships between several nodes and routes for creating complex shapes, etc. The labels are used for information of nodes or routes, such as that a certain road is a one-way road, the highest speed per hour of a certain road, etc. The starting position information and the target position information can be longitude and latitude information or node information.

In an embodiment, the global path includes at least one node, and the node is a node in the global topological map. Planning the global path of the legged robot 100 includes: and planning a shortest path from the starting position to the target position as the global path based on a plurality of nodes in the at least one global topological map through a path planning algorithm. Referring to fig. 5, it is assumed that the start location is node a, the destination location is node B, and the route a is the shortest route from the start location node a to the destination location node B, i.e. the global route.

In one embodiment, the path planning algorithm is a-x algorithm. The A-algorithm is a heuristic search algorithm, and a heuristic search rule is established in the search process to measure the distance relationship between the real-time position and the target position, so that the search direction preferentially faces the direction of the position of the target point. In other embodiments, the path planning algorithm may also be dijkstra, D, LPA, or D lite.

S402, constructing a local map according to the sensing information of the surrounding environment.

In an embodiment, the local map covers at least one node on the global path.

In one embodiment, constructing the local map based on the sensed information of the surrounding environment comprises: when the foot robot starts to move in the surrounding environment from the initial position node A, sensing information of the surrounding environment is acquired by the sensing unit every preset time period. If the local map is constructed by the electronic device 1, the legged robot 100 may also transmit the sensing information to the electronic device 1 through the wireless communication network to construct the local map according to the sensing information of the surrounding environment. Optionally, the preset time period is thirty seconds.

In an embodiment, the local map is an elevation map. The local map building method comprises the following steps: and constructing the local map based on the elevation information and the preset resolution ratio in the preset range sensed by the sensing unit of the legged robot. Wherein the elevation information is height information reflecting terrain height information within the preset range and/or height information of any building or structure presented on the terrain. Optionally, the preset range is a range of 20m by 20m, and the preset resolution is 5 cm.

Specifically, the sensing unit may include a laser radar and a camera, and the legged robot 100 scans and photographs the surrounding environment through the laser radar and the camera during the movement process, obtains the topographic features of the surrounding environment, extracts topographic height information in the surrounding environment, and constructs a local map based on the extracted topographic height information.

Further, in an embodiment, constructing the local map further comprises: when the local map is constructed, if raised obstacles and/or holes are detected, screening the raised obstacles and/or holes on the local map, and marking the screened raised obstacles and/or holes as impassable obstacles.

Specifically, when the local map is constructed, the legged robot 100 may detect a protruding obstacle based on a ground segmentation and settlement collision detection method and sensing information, search for surface discontinuity in a lidar point cloud generated based on the sensing information to detect a hole, screen the protruding obstacle and/or hole on the local map if the protruding obstacle and/or hole is detected, and mark the protruding obstacle and/or hole on the local map. For example, semantic information is used for marking, i.e., the text "obstacles" is marked on the local map in the area of the raised obstacles and/or holes. In this way, raised obstacles such as walls, large rocks, etc., holes such as large pits, uncovered drains, etc. can be directly considered as obstacles and displayed on the local map, and subsequent analysis of the obstacle area is not required, thereby greatly reducing the calculation time.

And S403, constructing a local passable map based on the motion performance parameters of the legged robot 100 and the local map.

In one embodiment, the motion performance parameters of the legged robot 100 may include at least one of a height threshold that can cross a step, a roughness threshold that can pass through the ground, a slope threshold that can pass through a slope, and a height threshold that can pass through a suspended object.

In an embodiment, constructing a local passable map based on the motion performance parameters of the legged robot 100 and the local map may be accomplished by: a. dividing a projection plane of the local map into a plurality of cells; b. setting a slope filter to calculate the slope based on the motion performance parameters, setting a terrain roughness filter to calculate the ground roughness, setting a step height filter to calculate the step height, and setting a suspended floor height filter to calculate the suspended height; c. calculating a passability parameter of each cell according to the motion performance parameter and the local map; d. if the passability parameter of the cell is greater than or equal to 0 and less than or equal to 1, determining that the cell is a passable cell, that is, the legged robot 100 can pass through the cell; e. if the passability parameter of the cell is less than 0, determining that the cell is a cell which cannot pass through, namely, the legged robot 100 cannot pass through the cell; f. calculating an average value of passability parameters of all cells in the projection range of the legged robot 100; g. determining a passability tf of the projection range based on the average value; h. constructing the local passable map based on passability of all projection ranges within the local map.

In one embodiment, if the danger value is infinity, the passability parameter of the cell is less than 0 and infinitesimal, and it is determined that the legged robot 100 cannot pass through the cell. Specifically, if any condition that the slope gradient is greater than the gradient critical value, the ground roughness is greater than the ground roughness critical value, the step height is greater than the step height critical value, and the difference between the upper limit value of the cell suspended height from the ground and the suspended height is greater than the upper limit value of the cell suspended height from the ground and the lower limit value of the cell suspended height from the ground is satisfied, it is determined that the danger value is infinite.

In an embodiment, the passability tf of the footprint is represented by 0 or 1. If the average value is greater than or equal to 0 and less than or equal to 1, determining that the projection range is passable, and setting the passability tf of the projection range to be 1. And if the average value is less than 0, determining the projection range as not-passable, and setting the passability tf of the projection range as 0.

In one embodiment, the passability of all footprints is marked on the local map, and footprints with the passability tf of 1 are combined to construct the local passable map.

The foot robot 100 has a preset step size, which is the maximum distance that the foot structure of the foot robot 100 can span in each step during the motion process.

In other embodiments, the constructing the local passable map may further include: determining whether the legged robot 100 can pass through based on a plurality of preset constraint conditions, wherein the plurality of preset constraint conditions comprise: if the width of the hole is smaller than the preset step length, determining that the foot robot 100 can pass through the hole; if the inclination of the terrain where the foot robot 100 is located is greater than a preset inclination limit value, determining that the foot robot 100 cannot pass through the terrain; if the width of the slope area with the gradient larger than the preset gradient limit value is smaller than the preset step length, determining that the foot type robot can pass through the slope area; if the width of the ground area with the roughness greater than the roughness critical value is smaller than the preset step length, it is determined that the foot robot 100 can pass through the ground area.

S404, determining a local target node of the legged robot 100 according to the global positioning accuracy of the legged robot 100, the global path, the current position of the legged robot 100 and the local passable map, and planning a local path of the legged robot 100 based on the local passable map, the local target node and the current position of the legged robot 100.

In an embodiment, when a local passable map is constructed, a local target node of the legged robot 100 may be determined according to the global positioning accuracy of the legged robot 100, the global path, the current position of the legged robot 100, and the local passable map, and then the local path of the legged robot 100 may be planned based on the local passable map, the local target node, and the current position of the legged robot 100, so that the legged robot 100 moves based on the planned local path.

As shown in fig. 7, the refining process of determining the local target node of the legged robot 100 according to the global positioning accuracy of the legged robot 100, the global path, the current position of the legged robot 100 and the local passable map, and planning the local path of the legged robot 100 based on the local passable map, the local target node and the current position of the legged robot 100 may include:

s4041, when the global positioning accuracy of the legged robot 100 meets a preset accuracy requirement, determining a local target node of the legged robot 100 based on the global path and the current position of the legged robot 100.

In an embodiment, when the local path planning is performed, the confidence of the global positioning accuracy of the global positioning module may be obtained in real time, and then whether the global positioning accuracy meets the preset accuracy requirement may be determined according to the confidence of the global positioning accuracy. When it is determined that the global positioning accuracy of the legged robot 100 meets the preset accuracy requirement, it is indicated that the legged robot 100 has the capability of performing autonomous navigation based on the global path, and a local path from the legged robot 100 to a local target node can be planned. The current position of the foot robot is longitude and latitude information. Determining a local target node of the legged robot 100 includes: and determining a preset radius range including at least one node on the global topological path by taking the current position of the legged robot 100 as a circle center, and determining a node which is farthest from the legged robot 100 within the preset radius range and is located in the local passable map as the local target node. The preset radius range may be set according to an actual sensing requirement, for example, the preset radius range may be a sensing range of the legged robot 100.

As shown in fig. 5, for example, the current position of the legged robot 100 is a point O, a preset radius range including a node D on the global topological path is determined with the point O as a center, and assuming that the node D is located in the local passable map, the node D is determined as a node farthest from the legged robot within the preset radius range, that is, the node D is the local target node.

It should be noted that, with the continuous movement of the legged robot 100, the local target node may be continuously updated until the target position is reached.

S4042, planning a local path of the legged robot 100 based on the local map, the local passable map, the local target node, and the current position of the legged robot 100.

In an embodiment, since the position of the legged robot 100 needs to maintain contact with the ground, the local path is a 2.5D path, each point on the local path has an X-axis coordinate and a Y-axis coordinate of a global coordinate system, and a Z-axis coordinate, i.e., a height, corresponding to each point can be obtained according to the X-axis coordinate, the Y-axis coordinate, and the local elevation map of each point.

In an embodiment, planning a local path of the legged robot 100 based on the local map, the local passable map, the local target node, and the current location of the legged robot 100 may include: selecting a passable projection range on the local passable map, and planning a shortest path from the current position of the foot robot 100 to the local target node by using the passable projection range through a path planning algorithm. Optionally, the path planning algorithm is an a-algorithm.

In one embodiment, for the legged robot 100, a global coordinate system (also referred to as a world coordinate system) and an odometer coordinate system may be defined for autonomous navigation. The global coordinate system can be used to establish a description of the working environment space of the legged robot 100, and is generally fixed, and the global coordinate system is usually constructed by using a geodetic coordinate system. The odometer coordinate system is a coordinate system established with the odometer as a center, and is not a fixed coordinate system as the odometer runs with the legged robot 100. The odometer is an effective sensor for relative positioning of the legged robot 100, and can provide real-time pose information for the legged robot 100. The odometer can calculate the change of the relative pose of the foot robot 100 according to the data of the encoder, the gyroscope and other sensors.

In an embodiment, the current position of the legged robot 100 may refer to position information determined under a global coordinate system.

In an embodiment, the electronic device 1 may send the planned local path of the legged robot 100 to the legged robot 100, so that the legged robot 100 performs autonomous motion under the navigation of the local path to move to the local target node.

In one embodiment, the local path may be updated continuously until the legged robot 100 reaches a target position. With the continuous movement of the legged robot 100, the local map, the local accessible map and the local target nodes are updated every other preset time period, so as to plan an updated local path, and the updated local path is tracked until the legged robot 100 reaches the target position, and finally autonomous path planning and autonomous movement in a large-scale complex environment are completed.

In an embodiment, after determining the local target node of the legged robot 100, the legged robot 100 may obtain surrounding environment information of the local target node through a sensing unit, extract semantic information of the local target node based on the surrounding environment information, and record an object corresponding to the local target node, so as to facilitate a subsequent autonomous navigation based on the object corresponding to the local target node when the global positioning accuracy does not meet the preset accuracy requirement. The semantic information of the local target node may refer to a location feature for characterizing the location of the local target node, for example, the semantic information may be a doorway of an underground garage, a road center, a square entrance, an elevator entrance, an overpass exit, and the like. Assuming that the node D is the local target node, and the semantic information of the node D is an overpass exit, that is, the node D is located at or near the overpass exit.

In an embodiment, the object corresponding to the local target node may be an object used to mark an approximate position of the local target node, for example, node D is the local target node, semantic information of node D is an overpass exit, and the object corresponding to the local target node may be a light pole, a signboard pole, or the like near the overpass exit. The object corresponding to the local target node may be set according to actually acquired ambient environment information of the local target node, and preferably, the object which is closest to and fixed to the local target node is set as the object corresponding to the local target node.

As shown in fig. 6, the current position of the legged robot 100 is point O, node D is the local target node, fixed objects D1 and D2 exist near node D, and the object D1 closest to node D and fixed is set as the object corresponding to node D.

In an embodiment, in the process that the legged robot 100 moves to the local target node based on the planned local path, the sensing unit may further obtain the ambient environment information of the local target node, extract the semantic information of the local target node based on the ambient environment information, and record the object corresponding to the local target node, so as to facilitate the autonomous navigation based on the object corresponding to the local target node when the global positioning accuracy does not meet the preset accuracy requirement.

In an embodiment, if the global path of the legged robot 100 in step 401 is obtained based on a global map plan with semantic information, that is, each node in the global path records semantic information around the node in advance, and an object corresponding to each node. In this case, the legged robot 100 does not need to acquire the information of the environment around the node through the sensing unit during the autonomous navigation, that is, does not need to extract semantic information of the node based on the information of the environment around the node and determine an object corresponding to the node.

S4043, when the global positioning accuracy of the legged robot 100 does not meet the preset accuracy requirement, obtaining an object corresponding to the local target node, and calculating a pose of the object in a coordinate system of the odometer. In one embodiment, when the legged robot 100 navigates to the local target node based on the planned local path, the global positioning accuracy may be reduced due to the variation of the number of surrounding reference objects. When the global positioning accuracy of the foot robot 100 does not meet the preset accuracy requirement, the foot robot 100 cannot accurately obtain the position of the foot robot 100 in the global path and the position of the local target node to which the foot robot is going, that is, the foot robot 100 does not have the capability of performing autonomous navigation based on the global path.

Assuming that the node D is the local target node, since the semantic information of the node D and the object corresponding to the node D are determined before the global positioning accuracy is reduced. The legged robot 100 can acquire surrounding environment information in real time through the sensing unit, identify an object corresponding to the semantic information of the node D in the surrounding environment of the legged robot 100, and further calculate the pose of the object in the odometer coordinate system, where the pose of the object is a local target point in the odometer coordinate system. That is, when the global positioning accuracy of the legged robot 100 does not meet the preset accuracy requirement, the position of the object D1 is equal to the position of the node D, so as to obtain the pose of the object D1 in the odometer coordinate system, and further, the autonomous navigation to the position of the object D1 can be realized based on the odometer coordinate system.

Specifically, when the positioning accuracy of the legged robot 100 does not meet the preset accuracy requirement, the current surrounding environment information of the legged robot may be acquired through the sensing unit, a local region corresponding to the semantic information of the local target node (node D) is identified from the current surrounding environment information, and an object corresponding to the local target node is identified from the local region, so as to achieve positioning of the object D1.

S4044, the object is used as a new local target node, and a local path of the legged robot 100 is planned based on the local passing map, the new local target node, and the current position of the legged robot 100 in the odometer coordinate system.

In an embodiment, when the pose of the object d1 in the odometer coordinate system is calculated, the object d1 may be used as a new local target node, and the foot robot 100 may autonomously navigate to the position where the object d1 is located by planning a local path of the foot robot 100.

Since the position of the legged robot 100 needs to be kept in contact with the ground, the local path is also a 2.5D path, each point on the local path has an X-axis coordinate and a Y-axis coordinate of a global coordinate system, and a Z-axis coordinate, i.e., a height, corresponding to each point can be obtained according to the X-axis coordinate, the Y-axis coordinate, and the local elevation map of each point. Because the odometer has the performance of keeping stable, high-precision and smooth navigation in a short distance, the planned local 2.5D path can ensure that the legged robot 100 performs autonomous navigation near a desired global path.

In an embodiment, planning a local path of the legged robot 100 based on the locally passable map, the new local target node, and the current position of the legged robot 100 in the odometer coordinate system may include: and selecting a passable projection range on the local passable map, and planning a shortest path from the current position of the legged robot 100 to the new local target node by using the passable projection range through a path planning algorithm. Optionally, the path planning algorithm is an a-algorithm.

In an embodiment, the electronic device 1 may send the planned local path of the legged robot 100 to the legged robot 100, so that the legged robot 100 performs autonomous motion under the navigation of the local path to move to the new local target node, i.e., to the position where the object d1 is located.

In an embodiment, when the legged robot 100 reaches the new local target node and the global positioning accuracy of the legged robot 100 still does not meet the preset accuracy requirement, preset positioning abnormality information may be output, and the legged robot 100 may be controlled to exit the autonomous navigation mode. The preset positioning abnormal information can be set according to actual requirements, and the method is not limited in the application. For example, as shown in fig. 6, when the legged robot 100 performs autonomous motion under the navigation of the local path to reach the position of the object d1, and the global positioning accuracy of the legged robot 100 still does not meet the preset accuracy requirement, since the semantic information of the node E and the object corresponding to the node E cannot be known, the steps S4043 and S4044 cannot be repeatedly performed, and at this time, the legged robot 100 may be controlled to exit the autonomous navigation mode.

In one embodiment, if the global path of the legged robot 100 is obtained based on a global map plan with semantic information, each node in the global path records the semantic information around the node in advance, and an object corresponding to each node. At this time, steps S4043 and S4044 may be repeatedly performed to update the local path, so that the legged robot 100 performs autonomous motion to the position where the object corresponding to the node E is located under the navigation of the local path. When the robot moves to the position where the object corresponding to the node E is located, the steps S4043 and S4044 may be further repeatedly executed to update the local path, so that the legged robot 100 autonomously moves to the position where the object corresponding to the node B is located under the navigation of the local path, and the target position is reached.

Specifically, when the legged robot 100 reaches the new local target node (object d1) and the positioning accuracy of the legged robot 100 still does not meet the preset accuracy requirement, acquiring an object corresponding to the next local target node (node E) from the global path, and calculating the pose of the object corresponding to the next local target node in the odometer coordinate system; updating an object corresponding to the next local target node (node E) to be a new local target node, and planning a local path of the legged robot based on the local accessible map, the updated new local target node and the current position of the legged robot in the odometer coordinate system, so that the legged robot 100 can perform autonomous motion to the position of the object corresponding to the node E under the navigation of the local path.

In an embodiment, when the legged robot 100 performs the autonomous movement under the navigation of the local path, the global positioning accuracy is restored to meet the preset accuracy requirement, and the steps S4041 and S4042 may be switched to plan the local path of the legged robot 100 until the legged robot 100 reaches the target position.

In an embodiment, a path tracking plan, a dynamic obstacle avoidance plan and a corresponding motion control of the legged robot 100 may also be performed based on the local path and the dynamic obstacles sensed by the legged robot 100.

It can be understood that, during the autonomous movement of the legged robot 100 based on the local path, a dynamic obstacle, such as a pedestrian, an animal, a vehicle, etc., may be encountered, the legged robot 100 may transmit the sensed obstacle information to the electronic device 1, and the electronic device 1 determines, based on the obstacle information, motion control on the legged robot 100, such as transmitting an instruction to control the legged robot 100 to turn, pause, accelerate, decelerate, etc., so as to avoid the dynamic obstacle, thereby implementing path tracking planning, dynamic obstacle avoidance planning, and corresponding motion control of the legged robot 100.

In other embodiments of the present application, the legged robot 100 may also be the electronic device 1 itself, autonomously perform path planning, including constructing a global path based on a global map, constructing a local map based on sensing information, calculating passability parameters to construct a passable map, planning and updating local target nodes and local paths, autonomously move to a target position along the continuously updated local path, and autonomously avoid obstacles when encountering dynamic obstacles during movement. Therefore, sensing information does not need to be transmitted to other electronic equipment 1, and local paths planned by other electronic equipment and obstacle avoidance motion control do not need to be received, so that time delay in the path planning and autonomous motion processes is reduced, and the motion efficiency is improved.

Fig. 8 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present application.

The electronic device 1 includes, but is not limited to, a processor 10, a memory 20, a computer program 30 stored in the memory 20 and executable on the processor 10, a slope filter 40, a terrain roughness filter 50, a step height filter 60, and a suspended layer height filter 70. The computer program 30 is, for example, a route planning program. The processor 10 implements steps of the path planning method for the legged robot, such as steps S401 to S404 shown in fig. 4, and steps S401 to S403 and S4041 to S4044 shown in fig. 7, when executing the computer program 30.

If the legged robot 100 is the electronic device 1, the processor 10 is the control module 110, and the memory 20 is the storage unit 105.

Illustratively, the computer program 30 may be partitioned into one or more modules/units, which are stored in the memory 20 and executed by the processor 10 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 30 in the electronic device 1.

It will be appreciated by a person skilled in the art that the schematic diagram is only an example of the electronic device 1 and does not constitute a limitation of the electronic device 1, and that it may comprise more or less components than shown, or some components may be combined, or different components, e.g. the electronic device 1 may further comprise an input output device, a network access device, a bus, etc.

The Processor 10 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor 10 may be any conventional processor or the like, the processor 10 being the control center of the electronic device 1, various interfaces and lines connecting the various parts of the whole electronic device 1.

The memory 20 may be used to store the computer program 30 and/or the modules/units, and the processor 10 implements various functions of the electronic device 1 by running or executing the computer program and/or the modules/units stored in the memory 20 and calling data stored in the memory 20. The memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the electronic apparatus 1, and the like. In addition, the memory 20 may include volatile and non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other storage device.

The gradient filter 40 is a computer program for calculating a gradient of a slope, the terrain roughness filter 50 is a computer program for calculating a roughness of a ground, the step height filter 60 is a computer program for calculating a step height, and the flying height filter 70 is a computer program for calculating a flying height.

The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the methods described above can be realized. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM).

The specific embodiment of the computer-readable storage medium according to the present application has substantially the same expansion content as the above embodiments of the path planning method for a legged robot, and will not be described herein again.

The path planning method, the electronic device and the storage medium of the foot robot can plan a global topological path, can identify complex terrains on a running path through sensing information of the foot robot, can intelligently analyze whether the foot robot can pass the complex terrains, plans and updates a local path based on passability and real-time robot positions and target nodes of the global topological path until the foot robot reaches the target position, enables the foot robot to independently pass or avoid the complex terrains, and improves the adaptability of the foot robot to an outdoor environment.

It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Several units or means recited in the apparatus claims may also be embodied by one and the same item or means in software or hardware. The terms first, second, etc. are used to denote names, but not to denote any particular order.

Although the present application has been described in detail with reference to preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present application.

Claims

1. A method for path planning for a legged robot, the method comprising:

acquiring a global path of a legged robot, wherein the global path comprises at least one node;

constructing a local map according to the sensing information of the surrounding environment, wherein the local map covers at least one node;

constructing a local passable map based on the motion performance parameters of the legged robot and the local map;

determining a local target node of the legged robot according to the global positioning precision of the legged robot, the global path, the current position of the legged robot and the local passable map, and planning the local path of the legged robot based on the local passable map, the local target node and the current position of the legged robot.

2. The method for planning the path of a legged robot according to claim 1, wherein the determining a local target node of the legged robot according to the global positioning accuracy of the legged robot, the global path, the current position of the legged robot and the locally available map, and the planning the local path of the legged robot based on the locally available map, the local target node and the current position of the legged robot, comprises:

when the global positioning precision of the foot type robot meets the preset precision requirement, determining a local target node of the foot type robot based on the global path, the current position of the foot type robot and the local accessible map;

planning a local path of the legged robot based on the local map, the local passable map, the local target node, and a current position of the legged robot;

when the global positioning precision of the foot robot does not meet the preset precision requirement, acquiring an object corresponding to the local target node, and calculating the pose of the object under a coordinate system of a speedometer;

the object is taken as a new local target node, and a local path of the legged robot is planned based on the local passable map, the new local target node and the current position of the legged robot under the odometer coordinate system.

3. The path planning method for a legged robot according to claim 1, characterized in that before said obtaining the global path of the legged robot, it comprises:

obtaining the confidence coefficient of the initial global positioning precision of the legged robot;

and when the confidence coefficient of the initial global positioning precision is greater than a preset value, acquiring initial position information, target position information and a global map of the surrounding environment, and planning a global path of the legged robot.

4. The method for planning a path of a legged robot according to claim 2, wherein after determining a local target node of the legged robot based on the global path, the current position of the legged robot and the local passable map, further comprising:

acquiring the ambient environment information of the local target node, extracting the semantic information of the local target node based on the ambient environment information, and recording an object corresponding to the local target node.

5. The path planning method for the legged robot according to claim 4, wherein the acquiring the object corresponding to the local target node when the global positioning accuracy of the legged robot does not meet the preset accuracy requirement comprises:

when the global positioning precision of the foot type robot does not meet the preset precision requirement, acquiring the current surrounding environment information of the foot type robot, and identifying a local area corresponding to the semantic information of the local target node from the current surrounding environment information;

identifying an object corresponding to the local target node from the local region.

6. The path planning method for a legged robot according to claim 2, characterized in that it further comprises:

and when the foot type robot reaches the new local target node and the global positioning precision of the foot type robot still does not meet the preset precision requirement, outputting preset positioning abnormal information and controlling the foot type robot to exit the autonomous navigation mode.

7. The path planning method for a legged robot according to claim 2, characterized in that it further comprises:

when the foot robot reaches the new local target node and the global positioning accuracy of the foot robot still does not meet the preset accuracy requirement, acquiring an object corresponding to a next local target node from the global path, and calculating the pose of the object corresponding to the next local target node under the odometer coordinate system; and updating the object corresponding to the next local target node as a new local target node.

8. The path planning method for a legged robot according to claim 7, wherein the global path is obtained based on a global map plan with semantic information, and each node in the global path has corresponding semantic information.

9. An electronic device, characterized in that the electronic device comprises:

a processor; and

a memory having stored therein a plurality of program modules that are loaded by the processor and execute the path planning method for a legged robot according to any of claims 1-8.

10. A computer readable storage medium having stored thereon at least one computer instruction, wherein the instruction is loaded by a processor and performs a method of path planning for a legged robot as claimed in any one of claims 1 to 8.