CN115016510A - Robot navigation obstacle avoidance method and device and storage medium - Google Patents

Robot navigation obstacle avoidance method and device and storage medium Download PDF

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CN115016510A
CN115016510A CN202210944226.1A CN202210944226A CN115016510A CN 115016510 A CN115016510 A CN 115016510A CN 202210944226 A CN202210944226 A CN 202210944226A CN 115016510 A CN115016510 A CN 115016510A
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target navigation
original
robot
initial position
navigation position
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徐文霞
吕灿炯
于宝成
廖楚媛
尹淑媛
魏明
胡记伟
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Wuhan Institute of Technology
Wuhan Fiberhome Technical Services Co Ltd
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Wuhan Fiberhome Technical Services Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/027—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means comprising intertial navigation means, e.g. azimuth detector

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Abstract

The invention provides a robot navigation obstacle avoidance method, a device and a storage medium, belonging to the field of robot navigation, wherein the method comprises the following steps: constructing a grid map through a plurality of original distances; carrying out global path analysis on the initial position, the original moving angle, the target navigation position, the grid map and the target navigation position to obtain a global path and a horizontal coordinate difference value between the initial position and the target navigation position; and according to the original movement angle, performing path optimization analysis on the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and a plurality of current distances to obtain an optimized path until the target navigation position is reached. The invention optimizes the path planning of the robot, ensures the globally optimal route, and simultaneously realizes the capability of the mobile robot to avoid dynamic obstacles and reach a target point in a complex environment.

Description

Robot navigation obstacle avoidance method and device and storage medium
Technical Field
The invention mainly relates to the technical field of robot navigation, in particular to a robot navigation obstacle avoidance method, a device and a storage medium.
Background
With the rapid development of robotics, more and more robots are applied to various complicated environments. The robot autonomous navigation obstacle avoidance is one of basic problems of robot technology, and the robot usually establishes a two-dimensional grid map, and gray values in the grid indicate whether the point position is a free space or an obstacle, so as to perform a path planning algorithm. In most application scenarios, the environment of the robot is partially known and partially unknown, and for this case, a global planning path from the starting point to the target point should be planned according to the global environment information. In the process that a robot travels along a global planned path, how to select a proper local obstacle avoidance method to avoid an obstacle when the robot encounters an unknown obstacle is a problem to be solved at present.
Disclosure of Invention
The invention provides a method and a device for robot navigation and obstacle avoidance and a storage medium, aiming at the defects of the prior art.
The technical scheme for solving the technical problems is as follows: a robot navigation obstacle avoidance method comprises the following steps:
s1: acquiring distances from emitted laser to a plurality of objects in a preset area through a two-dimensional laser radar arranged on a robot to obtain a plurality of original distances, and constructing a grid map through the original distances;
s2: a target navigation position is led in, an initial position is obtained through the two-dimensional laser radar, an original moving angle is obtained through a gyroscope sensor arranged on the robot, global path analysis is carried out on the initial position, the original moving angle, the target navigation position, the grid map and the target navigation position, a global path and a horizontal coordinate difference value between the initial position and the target navigation position are obtained, and the robot is controlled to move in the grid map along the global path;
s3: acquiring distances from the laser emitted by the current position of the robot to a plurality of objects through the two-dimensional laser radar to obtain a plurality of current distances, and obtaining a current moving angle through the gyroscope sensor;
s4: and performing path optimization analysis on the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and the current distances according to the original movement angle to obtain an optimized path, controlling the robot to move in the grid map along the optimized path, and returning to the step S3 until the target navigation position is reached.
Another technical solution of the present invention for solving the above technical problems is as follows: a robot navigation obstacle avoidance device, comprising:
the map building module is used for acquiring distances from emitted laser to a plurality of objects in a preset area through a two-dimensional laser radar arranged on the robot to obtain a plurality of original distances and building a grid map through the original distances;
the global path analysis module is used for leading in a target navigation position, obtaining an initial position through the two-dimensional laser radar, obtaining an original movement angle through a gyroscope sensor arranged on the robot, performing global path analysis on the initial position, the original movement angle, the target navigation position, the grid map and the target navigation position to obtain a global path and a horizontal coordinate difference value between the initial position and the target navigation position, and controlling the robot to move in the grid map along the global path;
the data acquisition module is used for acquiring the distances from the laser emitted by the current position of the robot to a plurality of objects through the two-dimensional laser radar to obtain a plurality of current distances and obtaining a current moving angle through the gyroscope sensor;
and the path optimization analysis module is used for performing path optimization analysis on the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and the plurality of current distances according to the original movement angle to obtain an optimized path, controlling the robot to move in the grid map along the optimized path, and returning to the data acquisition module until the target navigation position is reached.
Another technical solution of the present invention for solving the above technical problems is as follows: a robot navigation obstacle avoidance device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and when the processor executes the computer program, the robot navigation obstacle avoidance method is realized.
Another technical solution of the present invention for solving the above technical problems is as follows: a computer-readable storage medium, storing a computer program which, when executed by a processor, implements a robot navigation obstacle avoidance method as described above.
The invention has the beneficial effects that: constructing a grid map by a plurality of the original distances, analyzing the initial position, the original movement angle, the target navigation position, the grid map and the global path of the target navigation position to obtain a global path and a horizontal coordinate difference value of the initial position and the target navigation position, controlling the robot to move in the grid map along the global path, obtaining an optimized path by optimizing and analyzing the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and a plurality of paths of current distances according to the original movement angle, and controls the robot to move in the grid map along the optimized path until reaching the target navigation position, optimizes the path planning of the robot, ensures the globally optimal path, meanwhile, the capability that the mobile robot can avoid dynamic obstacles and reach a target point in a complex environment is realized.
Drawings
Fig. 1 is a schematic flow chart of a robot navigation obstacle avoidance method according to an embodiment of the present invention;
fig. 2 is a block diagram of a robot navigation obstacle avoidance device according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a schematic flow chart of a robot navigation obstacle avoidance method according to an embodiment of the present invention.
As shown in fig. 1, a robot navigation obstacle avoidance method includes the following steps:
s1: acquiring distances from emitted laser to a plurality of objects in a preset area through a two-dimensional laser radar arranged on a robot to obtain a plurality of original distances, and constructing a grid map through the original distances;
s2: a target navigation position is led in, an initial position is obtained through the two-dimensional laser radar, an original moving angle is obtained through a gyroscope sensor arranged on the robot, global path analysis is carried out on the initial position, the original moving angle, the target navigation position, the grid map and the target navigation position, a global path and a horizontal coordinate difference value between the initial position and the target navigation position are obtained, and the robot is controlled to move in the grid map along the global path;
s3: acquiring distances from the laser emitted by the current position of the robot to a plurality of objects through the two-dimensional laser radar to obtain a plurality of current distances, and obtaining a current moving angle through the gyroscope sensor;
s4: and performing path optimization analysis on the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and the current distances according to the original movement angle to obtain an optimized path, controlling the robot to move in the grid map along the optimized path, and returning to the step S3 until the target navigation position is reached.
Preferably, the type of the gyro sensor may be an MPU6050 gyro sensor.
It should be understood that the environment in which the robot is located is simulated and mapped, represented as the grid map.
Specifically, a target navigation point (i.e., the target navigation position) is set on the grid map, and a global navigation path (i.e., the global path) is planned according to the position of an obstacle appearing on the path by using an a-x algorithm added with a new heuristic function.
Specifically, on a planned global navigation path (i.e., the global path), a DWA algorithm added with a new distance evaluation function is used to optimize a local obstacle avoidance navigation path so that the local obstacle avoidance navigation path fits the planned global navigation path (i.e., the global path).
It should be understood that the robot is controlled to move by using the upper computer software, and the raw data (i.e. the initial position and the plurality of the current distances) of the sensors are acquired by the two-dimensional lidar carried by the mobile robot.
In the above embodiment, a grid map is constructed by a plurality of the original distances, a global path and a difference between horizontal coordinates of the original position and the target navigation position are obtained by analyzing the initial position, the original movement angle, the target navigation position, the grid map and the global path of the target navigation position, and the robot is controlled to move in the grid map along the global path, according to the original moving angle, the difference value of the horizontal coordinates of the initial position and the target navigation position, the global path, the target navigation position, the grid map and a plurality of paths of the current distance, the optimized path is obtained by optimizing and analyzing, and controls the robot to move in the grid map along the optimized path until reaching the target navigation position, optimizes the path planning of the robot, ensures the globally optimal path, meanwhile, the capability that the mobile robot can avoid dynamic obstacles and reach a target point in a complex environment is realized.
Optionally, as an embodiment of the present invention, in step S2, performing a global path analysis on the initial position, the original movement angle, the target navigation position, the grid map, and the target navigation position, and obtaining a global path and a difference between horizontal coordinates of the initial position and the target navigation position includes:
calculating a heuristic function on the initial position, the original movement angle and the target navigation position to obtain the heuristic function and a horizontal coordinate difference value between the initial position and the target navigation position;
calculating an evaluation function through a first formula to the heuristic function to obtain the evaluation function, wherein the first formula is as follows:
Figure 397167DEST_PATH_IMAGE001
，
wherein the content of the first and second substances,
Figure 376625DEST_PATH_IMAGE002
is a node
Figure 953100DEST_PATH_IMAGE003
The evaluation function of (a) the evaluation function of (b),
Figure 912091DEST_PATH_IMAGE004
the true cost value consumed by the original node to any node,
Figure 54359DEST_PATH_IMAGE005
is a node
Figure 204718DEST_PATH_IMAGE003
The heuristic function of (2);
and generating a global path through the target navigation position, the grid map and the valuation function.
As will be appreciated, the amount of time required,
Figure 534068DEST_PATH_IMAGE006
is a variable fixed value, i.e. is the initial locationThe shortest distance value to the target location.
Specifically, the conventional a-algorithm is a heuristic path search method, and the general form of the evaluation function is:
Figure 296750DEST_PATH_IMAGE008
wherein
Figure 496787DEST_PATH_IMAGE009
Is a node
Figure 286888DEST_PATH_IMAGE003
Cost value consumed from the initial node to the target node; the actual cost value consumed from the initial node to any node;
Figure 837955DEST_PATH_IMAGE005
for robot slave node
Figure 371705DEST_PATH_IMAGE003
A heuristic of the cost value consumed moving to the target point.
In the embodiment, the heuristic function and the difference value between the initial position and the horizontal coordinate of the target navigation position are obtained by computing the heuristic function of the initial position, the initial movement angle and the target navigation position, the valuation function is obtained by computing the valuation function of the heuristic function in a first mode, and the global path is generated by the target navigation position, the grid map and the valuation function, so that the global path planned by the robot is closer to the real shortest path, and the requirements of global path optimization and real-time obstacle avoidance in the navigation process of the robot in the complex environment are effectively met.
Optionally, as an embodiment of the present invention, the original moving angle includes an original horizontal moving angle and an original vertical moving angle, and the calculating of the heuristic function on the initial position, the original moving angle, and the target navigation position to obtain the heuristic function and a difference between horizontal coordinates of the initial position and the target navigation position includes:
calculating a heuristic function of the initial position, the original horizontal movement included angle, the original vertical movement included angle and the target navigation position through a second formula to obtain the heuristic function and a difference value of horizontal coordinates of the initial position and the target navigation position, wherein the second formula is as follows:
Figure 939432DEST_PATH_IMAGE010
，
wherein the content of the first and second substances,
Figure 166014DEST_PATH_IMAGE011
，
wherein the content of the first and second substances,
Figure 204377DEST_PATH_IMAGE012
is a node
Figure 276238DEST_PATH_IMAGE003
The heuristic function of (a) is,
Figure 716447DEST_PATH_IMAGE013
the difference value of the horizontal coordinates of the initial position and the target navigation position,
Figure 582772DEST_PATH_IMAGE014
the difference value of the vertical coordinates of the initial position and the target navigation position,
Figure 609896DEST_PATH_IMAGE015
the included angle is moved in the original horizontal direction,
Figure 219869DEST_PATH_IMAGE016
the included angle of the original vertical movement is obtained,
Figure 514584DEST_PATH_IMAGE017
is the abscissa of the target navigation position,
Figure 82969DEST_PATH_IMAGE018
is the abscissa of the initial position and is,
Figure 564766DEST_PATH_IMAGE019
is the ordinate of the target navigation position,
Figure 244009DEST_PATH_IMAGE020
is the ordinate of the initial position.
Specifically, the euclidean distance in equation (2) is used as an evaluation, and the path obtained by using this method is shortest, but the calculation amount is increased, so that the search efficiency is reduced, and the equation is as follows:
Figure 894695DEST_PATH_IMAGE022
when the method is applied to scenes with high search efficiency requirements, the Manhattan distance in the formula (3) is selected as evaluation, and definition is performed
Figure 633981DEST_PATH_IMAGE023
And
Figure 603074DEST_PATH_IMAGE024
respectively representing the coordinates of the current point and the target point, and the evaluation formula is as follows:
Figure 820429DEST_PATH_IMAGE026
to make a heuristic function
Figure 89736DEST_PATH_IMAGE027
More approximate to the true value (i.e. the distance between two straight lines), the invention combines the characteristics of the Manhattan distance and the Euclidean distance to set a new heuristic function, and the formula is as follows:
Figure 734344DEST_PATH_IMAGE029
wherein
Figure 957777DEST_PATH_IMAGE015
The included angle between the moving direction of the robot for avoiding the obstacle and the horizontal direction of the coordinate axis is shown,
Figure 713243DEST_PATH_IMAGE016
and the included angle between the motion direction of the robot for avoiding the obstacle and the vertical direction of the coordinate axis is shown. In the formula (4)
Figure 571478DEST_PATH_IMAGE030
And
Figure 652567DEST_PATH_IMAGE031
respectively representing the difference value of the abscissa and the ordinate between the current node position (i.e. the initial position) and the position of the target point (i.e. the target navigation position) of the robot, and the formula is as follows:
Figure 596252DEST_PATH_IMAGE033
。
in the embodiment, the heuristic function and the difference value between the horizontal coordinates of the initial position and the target navigation position are obtained by calculating the heuristic function of the initial position, the original horizontal movement included angle, the original vertical movement included angle and the target navigation position through the second formula, so that the calculated amount is reduced, the searching efficiency is improved, and the capability of the mobile robot in avoiding dynamic obstacles and reaching a target point under a complex environment is realized.
Optionally, as an embodiment of the present invention, in step S4, a process of performing a path optimization analysis on the current movement angle, the horizontal coordinate difference between the initial position and the target navigation position, the global path, the target navigation position, the grid map, and the plurality of current distances according to the original movement angle to obtain an optimized path includes:
judging whether the current moving angle is equal to the original moving angle or not, and if so, taking the global path as an optimized path;
if not, obtaining a linear velocity value of the current sampling velocity of the robot through a speedometer arranged on the robot, and carrying out path optimization on the linear velocity value, the original horizontal movement included angle, the current movement angle, the target navigation position, the grid map and a difference value between the initial position and the horizontal coordinate of the target navigation position according to a plurality of current distances to obtain the optimized path.
It should be understood that, through the judgment of the current movement angle and the original movement angle, whether the path generates an offset or not is known, so that the globally optimal route can be more closely fitted.
In the embodiment, the optimized path is obtained by optimizing and analyzing the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and paths of a plurality of current distances through the original movement angle, so that the global path optimization and real-time obstacle avoidance functions of the mobile robot navigation are realized, and certain effectiveness and feasibility are achieved.
Optionally, as an embodiment of the present invention, the current movement angle includes a current horizontal movement included angle, and the process of performing path optimization on the linear velocity value, the original horizontal movement included angle, the current movement angle, the target navigation position, the grid map, and a difference between horizontal coordinates of the initial position and the target navigation position according to a plurality of current distances includes:
judging whether the current distances are equal, if so, taking a first preset value as a value of an initial evaluation function; if not, screening the minimum value of the current distances to obtain the minimum distance, and taking the minimum distance as the value of the initial evaluation function;
calculating a target evaluation function for the linear velocity value, the original horizontal movement included angle, the initial evaluation function, the current horizontal movement included angle and the horizontal coordinate difference value between the initial position and the target navigation position by a third formula to obtain the target evaluation function, wherein the third formula is as follows:
Figure 420988DEST_PATH_IMAGE034
，
wherein the content of the first and second substances,
Figure 900773DEST_PATH_IMAGE035
，
wherein the content of the first and second substances,
Figure 887184DEST_PATH_IMAGE036
in order to be the objective evaluation function,
Figure 318165DEST_PATH_IMAGE037
to smooth the weights of the merit function,
Figure 681014DEST_PATH_IMAGE038
、
Figure 779420DEST_PATH_IMAGE039
、
Figure 671152DEST_PATH_IMAGE040
and
Figure 356474DEST_PATH_IMAGE041
are the weight values of the evaluation function,
Figure 523013DEST_PATH_IMAGE042
in order to evaluate the function for the azimuth,
Figure 210346DEST_PATH_IMAGE043
is an initial evaluation function, the value corresponding to the initial evaluation function is the first preset value or the minimum distance obtained by screening,
Figure 804139DEST_PATH_IMAGE044
the linear velocity value is the value of the linear velocity,
Figure 944133DEST_PATH_IMAGE045
in order to be a function of the distance evaluation,
Figure 648784DEST_PATH_IMAGE015
moving the included angle for the original horizontal direction,
Figure 957668DEST_PATH_IMAGE046
The difference value of the horizontal coordinates of the initial position and the target navigation position,
Figure 722361DEST_PATH_IMAGE047
the included angle is the current horizontal movement angle;
and generating an optimized path through the target navigation position, the grid map and the target evaluation function.
Preferably, the first preset value may be 0.
It should be understood that whether the current distances are all equal is judged, if yes, it is indicated that no obstacle exists in the area scanned by the two-dimensional laser radar, and therefore a first preset value is used as a value of an initial evaluation function;
if not, the fact that the obstacle exists in the area scanned by the two-dimensional laser radar is indicated, so that the minimum value of the current distances is screened, the minimum distance is obtained through screening, and the minimum distance is used as the value of the initial evaluation function.
Specifically, all motion trajectories are simulated for all the desirable sampling speed groups, and an optimal trajectory is selected by using an evaluation function method, wherein the evaluation function is as follows:
Figure 349652DEST_PATH_IMAGE048
wherein
Figure 857993DEST_PATH_IMAGE049
The method mainly comprises the following steps of as follows, for an azimuth angle evaluation function of a robot moving to a target point at a current sampling speed, assisting the robot to move towards a terminal point direction all the time in the moving process without deviating from the direction, and expressing:
Figure 519919DEST_PATH_IMAGE050
，
Figure 455514DEST_PATH_IMAGE051
i.e. the original horizontal directionMoving included angle
Figure 71565DEST_PATH_IMAGE052
；
Figure 118019DEST_PATH_IMAGE053
An evaluation function (namely the initial evaluation function) of the distance between the robot and the obstacle on the motion trail;
Figure 900030DEST_PATH_IMAGE054
the linear velocity value of the current sampling velocity of the robot is obtained;
Figure 740947DEST_PATH_IMAGE055
to smooth the weights of the merit function,
Figure 608409DEST_PATH_IMAGE056
、
Figure 192974DEST_PATH_IMAGE057
、
Figure 319238DEST_PATH_IMAGE058
is the weight of the evaluation function.
In order to enable the robot to be close to the original global path to a great extent and effectively avoid obstacles in the movement process, the invention adds a new distance evaluation function on the basis of the evaluation function
Figure 596635DEST_PATH_IMAGE059
And an evaluation function representing the distance from the end point of the motion trail to the optimal path is as follows:
Figure 420235DEST_PATH_IMAGE060
。
as should be appreciated, the first and second members,
Figure 74070DEST_PATH_IMAGE061
the following formula can be obtained:
Figure 565094DEST_PATH_IMAGE062
Figure 13393DEST_PATH_IMAGE063
for the original planned path and the included angle of the coordinate axis in the horizontal direction (i.e. the original horizontal movement included angle)
Figure 91333DEST_PATH_IMAGE015
），
Figure 283280DEST_PATH_IMAGE015
The included angle between the motion direction of the robot avoiding the obstacle and the horizontal direction of the coordinate axis (namely the current horizontal moving included angle)
Figure 628810DEST_PATH_IMAGE064
）。
In the above embodiment, the optimized path is obtained by optimizing a plurality of current distances to a linear velocity value, an original horizontal movement included angle, a current movement angle, a target navigation position, a grid map, and a path of a horizontal coordinate difference value between an initial position and a target navigation position, so that the robot can be close to an original global path to a great extent, and an obstacle can be effectively avoided in the movement process.
Alternatively, as another embodiment of the present invention, the speed of the robot needs to be limited within a certain range, there are a maximum speed and a minimum speed, and the formula is as follows:
Figure 513590DEST_PATH_IMAGE065
wherein
Figure 311781DEST_PATH_IMAGE066
Is the minimum linear velocity of the robot,
Figure 307419DEST_PATH_IMAGE067
is the maximum linear velocity;
the maximum increasing (decreasing) speed of the inspection robot is influenced by the performance of the motor, the simulated speed in the dynamic window must accord with the real speed, and the formula is as follows:
Figure 8921DEST_PATH_IMAGE068
wherein
Figure 799023DEST_PATH_IMAGE069
Is the robot linear velocity;
Figure 350090DEST_PATH_IMAGE070
maximum linear acceleration;
Figure 618260DEST_PATH_IMAGE071
is the minimum linear acceleration;
Figure 938383DEST_PATH_IMAGE072
is the angular velocity of the robot;
Figure 164965DEST_PATH_IMAGE073
is the maximum angular acceleration;
Figure 439214DEST_PATH_IMAGE074
the minimum angular acceleration.
In order to ensure that the robot can stop before hitting the obstacle, the robot must keep a safe distance from the obstacle, and under the maximum speed constraint, the speed has a range, and the formula is as follows:
Figure 511075DEST_PATH_IMAGE075
wherein
Figure 951284DEST_PATH_IMAGE076
The shortest distance of the robot from the obstacle.
Optionally, as another embodiment of the present invention, the present invention uses upper computer software to control the robot to move, and obtains the raw data of the sensor through the two-dimensional laser radar carried by the mobile robot, and simulates the environment where the robot is located to build a map, which is represented as a grid map; setting a target navigation point on the grid map, and planning a global navigation path according to the position of an obstacle appearing on the path by using an A-star algorithm added with a new heuristic function; optimizing the local obstacle avoidance navigation path on the global navigation path planned by the improved A-x algorithm by utilizing a DWA algorithm added with a new distance evaluation function, so that the local obstacle avoidance navigation path is attached to the global navigation path planned by the improved A-x algorithm; and repeating the steps until the robot safely navigates to the target point according to the requirement of the robot for navigating a plurality of target points. The invention realizes the global path optimization and real-time obstacle avoidance functions of the mobile robot navigation, and has certain effectiveness and feasibility.
Optionally, as another embodiment of the invention, the invention optimizes the navigation algorithm of the mobile robot by improving the a-algorithm and the DWA algorithm to meet the requirements of global path optimization and real-time obstacle avoidance of the navigation process in the complex environment of the robot. Setting a new heuristic function for the A-algorithm to enable the global path planned by the robot to be closer to the real shortest path; on the basis of the traditional DWA algorithm, a new distance evaluation function is added, and in the safe speed range of the robot, some unnecessary moving directions are reduced to a certain extent, so that the moving track of the robot is smoother and can move to a target point more quickly, the improved A-x algorithm and the improved DWA algorithm realize the global path optimization and real-time obstacle avoidance functions of the mobile robot navigation, and have certain effectiveness and feasibility.
Optionally, as another embodiment of the present invention, the real value of the distance from the current position to the target point of the robot is set as
Figure 817608DEST_PATH_IMAGE077
When is coming into contact with
Figure 343268DEST_PATH_IMAGE078
In time, the path search space is large, the number of nodes is large, the search efficiency is low, and the optimal solution can be searched finally; when in use
Figure 218820DEST_PATH_IMAGE079
In the process, the path search space is small, the number of nodes is small, the search speed and efficiency are improved, but the planned path is not the optimal solution in most cases; when in use
Figure 280579DEST_PATH_IMAGE080
In the process, the path search is expanded along the shortest path node sequence, so that the search efficiency is high, but the actual situation is difficult to realize. To make a heuristic function
Figure 848964DEST_PATH_IMAGE081
More approximate to the true value
Figure 861919DEST_PATH_IMAGE082
The invention combines the characteristics of the Manhattan distance and the Euclidean distance to set a new heuristic function, and the formula is as follows,
Figure 806741DEST_PATH_IMAGE015
the included angle between the moving direction of the robot for avoiding the obstacle and the horizontal direction of the coordinate axis is shown,
Figure 457428DEST_PATH_IMAGE016
and the included angle between the motion direction of the robot for avoiding the obstacle and the vertical direction of the coordinate axis is shown. In the formula (4)
Figure 462293DEST_PATH_IMAGE083
And
Figure 696965DEST_PATH_IMAGE084
respectively representing the difference values of the horizontal and vertical coordinates between the current node position of the robot and the position of the target point, and the formula is as follows:
Figure 914320DEST_PATH_IMAGE086
Figure 183627DEST_PATH_IMAGE088
Figure 329700DEST_PATH_IMAGE090
in order to verify the effectiveness of the heuristic function, a special value is taken, and the included angle between the original motion direction of the robot to the target point and the horizontal direction of the coordinate axis is set under the condition that the robot has an obstacle
Figure 786089DEST_PATH_IMAGE091
The robot avoids the included angle between the moving direction of the barrier and the horizontal direction of the coordinate axis
Figure 807135DEST_PATH_IMAGE092
Let the distance from A to C
Figure 930948DEST_PATH_IMAGE093
In the right triangle ACB, the distance from C to B is obtained according to the cosine function, and the movement distance from a to B is 2.
According to heuristic functions
Figure 12037DEST_PATH_IMAGE094
Is calculated to obtain
Figure 221301DEST_PATH_IMAGE095
The true value of the original movement distance is 2, the heuristic function obtained through verification is closer to the true cost value, the global path optimization and real-time obstacle avoidance functions of the mobile robot navigation are realized, and certain effectiveness and feasibility are achieved.
Optionally, as another embodiment of the present invention, the executing steps of the present invention may further be:
s1, controlling the robot to move by using upper computer software, acquiring the original data of the sensor through a two-dimensional laser radar carried by the mobile robot, and simulating and drawing the environment where the robot is located to be represented as a grid map;
s2, setting target navigation points on the grid map, and planning a global navigation path according to the positions of the obstacles on the path by using an A-x algorithm added with a new heuristic function;
s3, optimizing the local obstacle avoidance navigation path on the planned global navigation path in S2 by using a DWA algorithm added with a new distance evaluation function, so that the local obstacle avoidance navigation path is attached to the planned global navigation path in S2;
and S4, repeating the S2 and S3 aiming at the requirement of the robot for navigating a plurality of target points until the robot safely navigates to the target points.
Fig. 2 is a block diagram of a robot navigation obstacle avoidance device according to an embodiment of the present invention.
Optionally, as another embodiment of the present invention, as shown in fig. 2, a robot navigation obstacle avoidance device includes:
the map building module is used for acquiring distances from emitted laser to a plurality of objects in a preset area through a two-dimensional laser radar arranged on the robot to obtain a plurality of original distances and building a grid map through the original distances;
the global path analysis module is used for leading in a target navigation position, obtaining an initial position through the two-dimensional laser radar, obtaining an original movement angle through a gyroscope sensor arranged on the robot, performing global path analysis on the initial position, the original movement angle, the target navigation position, the grid map and the target navigation position to obtain a global path and a horizontal coordinate difference value between the initial position and the target navigation position, and controlling the robot to move in the grid map along the global path;
the data acquisition module is used for acquiring the distances from the laser emitted by the current position of the robot to a plurality of objects through the two-dimensional laser radar to obtain a plurality of current distances and obtaining a current moving angle through the gyroscope sensor;
and the path optimization analysis module is used for performing path optimization analysis on the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and the plurality of current distances according to the original movement angle to obtain an optimized path, controlling the robot to move in the grid map along the optimized path, and returning to the data acquisition module until the target navigation position is reached.
Optionally, as an embodiment of the present invention, the global path analysis module is specifically configured to:
calculating a heuristic function on the initial position, the original movement angle and the target navigation position to obtain the heuristic function and a horizontal coordinate difference value between the initial position and the target navigation position;
calculating an evaluation function through a first formula to the heuristic function to obtain the evaluation function, wherein the first formula is as follows:
Figure 16344DEST_PATH_IMAGE096
，
wherein the content of the first and second substances,
Figure 994665DEST_PATH_IMAGE002
is a node
Figure 246654DEST_PATH_IMAGE003
The evaluation function of (a) the evaluation function of (b),
Figure 943215DEST_PATH_IMAGE097
the true cost value consumed by the original node to any node,
Figure 306063DEST_PATH_IMAGE098
is a node
Figure 873311DEST_PATH_IMAGE003
The heuristic function of (2);
and generating a global path through the target navigation position, the grid map and the valuation function.
Optionally, as an embodiment of the present invention, the original moving angle includes an original horizontal moving included angle and an original vertical moving included angle, and the process of calculating a heuristic function for the initial position, the original moving angle, and the target navigation position in the global path analysis module to obtain the heuristic function and a difference between horizontal coordinates of the initial position and the target navigation position includes:
calculating a heuristic function of the initial position, the original horizontal movement included angle, the original vertical movement included angle and the target navigation position through a second formula to obtain the heuristic function and a difference value of horizontal coordinates of the initial position and the target navigation position, wherein the second formula is as follows:
Figure 809385DEST_PATH_IMAGE099
，
wherein the content of the first and second substances,
Figure 993242DEST_PATH_IMAGE100
，
wherein the content of the first and second substances,
Figure 628623DEST_PATH_IMAGE101
is a node
Figure 581535DEST_PATH_IMAGE003
The heuristic function of (a) is,
Figure 175328DEST_PATH_IMAGE102
the difference value of the horizontal coordinates of the initial position and the target navigation position,
Figure DEST_PATH_IMAGE103
the difference value of the vertical coordinates of the initial position and the target navigation position,
Figure 879104DEST_PATH_IMAGE015
the included angle is moved in the original horizontal direction,
Figure 583755DEST_PATH_IMAGE016
the included angle of the original vertical movement is obtained,
Figure 391174DEST_PATH_IMAGE104
is the abscissa of the target navigation position,
Figure DEST_PATH_IMAGE105
is the abscissa of the initial position and is,
Figure 952605DEST_PATH_IMAGE106
is the ordinate of the target navigation position,
Figure 81360DEST_PATH_IMAGE107
is the ordinate of the initial position.
Optionally, another embodiment of the present invention provides a robot navigation obstacle avoidance apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the robot navigation obstacle avoidance method as described above is implemented. The device may be a computer or the like.
Optionally, another embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for robot navigation obstacle avoidance is implemented as described above.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A robot navigation obstacle avoidance method is characterized by comprising the following steps:
s1: acquiring distances from emitted laser to a plurality of objects in a preset area through a two-dimensional laser radar arranged on the robot to obtain a plurality of original distances, and constructing a grid map through the plurality of original distances;
s2: a target navigation position is led in, an initial position is obtained through the two-dimensional laser radar, an original moving angle is obtained through a gyroscope sensor arranged on the robot, global path analysis is carried out on the initial position, the original moving angle, the target navigation position, the grid map and the target navigation position, a global path and a horizontal coordinate difference value between the initial position and the target navigation position are obtained, and the robot is controlled to move in the grid map along the global path;
s3: acquiring distances from the laser emitted by the current position of the robot to a plurality of objects through the two-dimensional laser radar to obtain a plurality of current distances, and obtaining a current moving angle through the gyroscope sensor;
s4: and performing path optimization analysis on the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and the current distances according to the original movement angle to obtain an optimized path, controlling the robot to move in the grid map along the optimized path, and returning to the step S3 until the target navigation position is reached.
2. The robot navigation obstacle avoidance method according to claim 1, wherein in step S2, the process of performing global path analysis on the initial position, the initial movement angle, the target navigation position, the grid map, and the target navigation position to obtain a global path and a difference between horizontal coordinates of the initial position and the target navigation position includes:
calculating a heuristic function on the initial position, the original movement angle and the target navigation position to obtain the heuristic function and a horizontal coordinate difference value between the initial position and the target navigation position;
calculating an evaluation function through a first formula to the heuristic function to obtain the evaluation function, wherein the first formula is as follows:
Figure 534251DEST_PATH_IMAGE001
，
wherein the content of the first and second substances,
Figure 461756DEST_PATH_IMAGE002
is a node
Figure 898816DEST_PATH_IMAGE003
The evaluation function of (a) the evaluation function of (b),
Figure 278982DEST_PATH_IMAGE004
the true cost value consumed by the original node to any node,
Figure 325435DEST_PATH_IMAGE005
is a node
Figure 841867DEST_PATH_IMAGE003
The heuristic function of (2);
and generating a global path through the target navigation position, the grid map and the valuation function.
3. The robot navigation obstacle avoidance method according to claim 2, wherein the original movement angle includes an original horizontal movement angle and an original vertical movement angle, and the process of calculating the initial position, the original movement angle and the target navigation position by the heuristic function to obtain the heuristic function and the difference between the horizontal coordinates of the initial position and the target navigation position includes:
calculating a heuristic function of the initial position, the original horizontal movement included angle, the original vertical movement included angle and the target navigation position through a second formula to obtain the heuristic function and a difference value of horizontal coordinates of the initial position and the target navigation position, wherein the second formula is as follows:
Figure 948363DEST_PATH_IMAGE006
，
wherein the content of the first and second substances,
Figure 815825DEST_PATH_IMAGE007
，
wherein the content of the first and second substances,
Figure 167434DEST_PATH_IMAGE005
is a node
Figure 803952DEST_PATH_IMAGE003
The heuristic function of (a) is,
Figure 815771DEST_PATH_IMAGE008
the difference value of the horizontal coordinates of the initial position and the target navigation position,
Figure 904949DEST_PATH_IMAGE009
is the difference value of the initial position and the target navigation position vertical coordinate,
Figure 558784DEST_PATH_IMAGE010
the included angle is moved in the original horizontal direction,
Figure 49809DEST_PATH_IMAGE011
the included angle of the original vertical movement is obtained,
Figure 999572DEST_PATH_IMAGE012
is the abscissa of the target navigation position,
Figure 107206DEST_PATH_IMAGE013
is the abscissa of the initial position and is,
Figure 564732DEST_PATH_IMAGE014
is the ordinate of the target navigation position,
Figure 910263DEST_PATH_IMAGE015
is the ordinate of the initial position.
4. The robot navigation obstacle avoidance method according to claim 3, wherein in step S4, the process of performing path optimization analysis on the current movement angle, the difference between the horizontal coordinates of the initial position and the target navigation position, the global path, the target navigation position, the grid map, and the plurality of current distances according to the original movement angle, and obtaining the optimized path includes:
judging whether the current moving angle is equal to the original moving angle or not, and if so, taking the global path as an optimized path;
if not, obtaining a linear velocity value of the current sampling velocity of the robot through a speedometer arranged on the robot, and carrying out path optimization on the linear velocity value, the original horizontal movement included angle, the current movement angle, the target navigation position, the grid map and a difference value between the initial position and the horizontal coordinate of the target navigation position according to a plurality of current distances to obtain the optimized path.
5. The robot navigation obstacle avoidance method according to claim 4, wherein the current movement angle includes a current horizontal movement angle, and the process of performing path optimization on the linear velocity value, the original horizontal movement angle, the current movement angle, the target navigation position, the grid map, and the difference between the horizontal coordinates of the initial position and the target navigation position according to the plurality of current distances includes:
judging whether the current distances are equal, if so, taking a first preset value as a value of an initial evaluation function; if not, screening the minimum value of the current distances to obtain the minimum distance, and taking the minimum distance as the value of the initial evaluation function;
calculating a target evaluation function according to a third formula on the linear velocity value, the original horizontal movement included angle, the initial evaluation function, the current horizontal movement included angle and the horizontal coordinate difference value between the initial position and the target navigation position to obtain the target evaluation function, wherein the third formula is as follows:
Figure 573805DEST_PATH_IMAGE016
，
wherein the content of the first and second substances,
Figure 637576DEST_PATH_IMAGE017
，
wherein the content of the first and second substances,
Figure 633214DEST_PATH_IMAGE018
in order to be the objective evaluation function,
Figure 833251DEST_PATH_IMAGE019
to smooth the weights of the evaluation function,
Figure 888931DEST_PATH_IMAGE020
、
Figure 675884DEST_PATH_IMAGE021
、
Figure 209634DEST_PATH_IMAGE022
and
Figure 529756DEST_PATH_IMAGE023
are the weight values of the evaluation function,
Figure 490759DEST_PATH_IMAGE024
in order to evaluate the function for the azimuth,
Figure 263543DEST_PATH_IMAGE025
is an initial evaluation function, the value corresponding to the initial evaluation function is the first preset value or the minimum distance obtained by screening,
Figure 600984DEST_PATH_IMAGE026
the linear velocity value is the value of the linear velocity,
Figure 277078DEST_PATH_IMAGE027
in order to be a function of the distance evaluation,
Figure 674561DEST_PATH_IMAGE010
the included angle is moved in the original horizontal direction,
Figure 669062DEST_PATH_IMAGE028
the difference value of the horizontal coordinates of the initial position and the target navigation position,
Figure 810193DEST_PATH_IMAGE029
the included angle is the current horizontal movement angle;
and generating an optimized path through the target navigation position, the grid map and the target evaluation function.
6. The utility model provides a barrier device is kept away in navigation of robot which characterized in that includes:
the map building module is used for acquiring distances from emitted laser to a plurality of objects in a preset area through a two-dimensional laser radar arranged on the robot to obtain a plurality of original distances and building a grid map through the plurality of original distances;
the global path analysis module is used for leading in a target navigation position, obtaining an initial position through the two-dimensional laser radar, obtaining an original movement angle through a gyroscope sensor arranged on the robot, performing global path analysis on the initial position, the original movement angle, the target navigation position, the grid map and the target navigation position to obtain a global path and a horizontal coordinate difference value between the initial position and the target navigation position, and controlling the robot to move in the grid map along the global path;
the data acquisition module is used for acquiring the distances from the laser emitted by the current position of the robot to a plurality of objects through the two-dimensional laser radar to obtain a plurality of current distances and obtaining a current moving angle through the gyroscope sensor;
and the path optimization analysis module is used for performing path optimization analysis on the current movement angle, the horizontal coordinate difference value between the initial position and the target navigation position, the global path, the target navigation position, the grid map and the plurality of current distances according to the original movement angle to obtain an optimized path, controlling the robot to move in the grid map along the optimized path, and returning to the data acquisition module until the target navigation position is reached.
7. The robot navigation obstacle avoidance device of claim 6, wherein the global path analysis module is specifically configured to:
calculating a heuristic function on the initial position, the original movement angle and the target navigation position to obtain the heuristic function and a horizontal coordinate difference value between the initial position and the target navigation position;
calculating an evaluation function through a first formula to the heuristic function to obtain the evaluation function, wherein the first formula is as follows:
Figure 370488DEST_PATH_IMAGE030
，
wherein the content of the first and second substances,
Figure 673293DEST_PATH_IMAGE002
is a node
Figure 187713DEST_PATH_IMAGE003
The evaluation function of (a) the evaluation function of (b),
Figure 866956DEST_PATH_IMAGE031
the true cost value consumed by the original node to any node,
Figure 16178DEST_PATH_IMAGE032
a heuristic function for a node;
and generating a global path through the target navigation position, the grid map and the valuation function.
8. The robot navigation obstacle avoidance device of claim 7, wherein the original movement angle includes an original horizontal movement angle and an original vertical movement angle, and the process of calculating the heuristic function for the initial position, the original movement angle, and the target navigation position in the global path analysis module to obtain the heuristic function and the difference between the horizontal coordinates of the initial position and the target navigation position includes:
calculating a heuristic function of the initial position, the original horizontal movement included angle, the original vertical movement included angle and the target navigation position through a second formula to obtain the heuristic function and a difference value of horizontal coordinates of the initial position and the target navigation position, wherein the second formula is as follows:
Figure 224305DEST_PATH_IMAGE033
，
wherein the content of the first and second substances,
Figure 458978DEST_PATH_IMAGE034
，
wherein the content of the first and second substances,
Figure 443376DEST_PATH_IMAGE035
is a node
Figure 447104DEST_PATH_IMAGE003
The heuristic function of (a) is,
Figure 91712DEST_PATH_IMAGE036
the difference value of the horizontal coordinates of the initial position and the target navigation position,
Figure 813681DEST_PATH_IMAGE037
the difference value of the vertical coordinates of the initial position and the target navigation position,
Figure 834726DEST_PATH_IMAGE010
the included angle is moved in the original horizontal direction,
Figure 958540DEST_PATH_IMAGE011
the included angle of the original vertical movement is obtained,
Figure 275514DEST_PATH_IMAGE038
is the abscissa of the target navigation position,
Figure 219199DEST_PATH_IMAGE039
is the abscissa of the initial position and is,
Figure 43936DEST_PATH_IMAGE040
is the ordinate of the target navigation position,
Figure 756677DEST_PATH_IMAGE041
is the ordinate of the initial position.
9. A robot navigation obstacle avoidance system comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, the robot navigation obstacle avoidance method according to any one of claims 1 to 5 is implemented.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the robot navigation obstacle avoidance method according to any one of claims 1 to 5.
CN202210944226.1A 2022-08-08 2022-08-08 Robot navigation obstacle avoidance method and device and storage medium Pending CN115016510A (en)

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