CN112540609A - Path planning method and device, terminal equipment and storage medium - Google Patents
Path planning method and device, terminal equipment and storage medium Download PDFInfo
- Publication number
- CN112540609A CN112540609A CN202010752888.XA CN202010752888A CN112540609A CN 112540609 A CN112540609 A CN 112540609A CN 202010752888 A CN202010752888 A CN 202010752888A CN 112540609 A CN112540609 A CN 112540609A
- Authority
- CN
- China
- Prior art keywords
- path
- dynamic obstacle
- mobile robot
- dynamic
- local motion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 78
- 230000004888 barrier function Effects 0.000 claims description 24
- 230000008569 process Effects 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 20
- 230000003068 static effect Effects 0.000 claims description 7
- QBPFLULOKWLNNW-UHFFFAOYSA-N chrysazin Chemical compound O=C1C2=CC=CC(O)=C2C(=O)C2=C1C=CC=C2O QBPFLULOKWLNNW-UHFFFAOYSA-N 0.000 claims description 4
- 230000001133 acceleration Effects 0.000 description 14
- 230000006870 function Effects 0.000 description 11
- 238000010586 diagram Methods 0.000 description 6
- 238000005259 measurement Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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 or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Abstract
The application provides a path planning method, a path planning device, terminal equipment and a storage medium, relates to the technical field of path planning, and aims to obtain an effective navigation path through planning so as to meet the requirement of a real-time driving scene. The path planning method comprises the following steps: acquiring a first motion parameter of at least one dynamic obstacle; predicting a local motion path of the at least one dynamic obstacle within a first time period according to the first motion parameter; and determining a target path for navigating the mobile robot according to the local motion path.
Description
Technical Field
The present application relates to the field of path planning technologies, and in particular, to a path planning method and apparatus, a terminal device, and a storage medium.
Background
With the development of sensor technology, artificial intelligence and other technologies, the robot field becomes a new and briskly-developed field, and the mobile robot is paid more and more attention as an important application. Generally, existing mobile robots plan a feasible navigation path from a starting point to an end point in advance, and then perform autonomous navigation.
However, most path planning methods for existing mobile robots only consider the state of an obstacle at the current time to plan a path, and the obtained navigation path is difficult to meet the requirements of a real-time driving scene. Therefore, the existing path planning method is low in effectiveness and difficult to obtain an effective navigation path.
Disclosure of Invention
The embodiment of the application provides a path planning method, a path planning device, terminal equipment and a storage medium, and can solve the problem of low effectiveness of the existing path planning method.
In a first aspect, an embodiment of the present application provides a path planning method, where the method includes:
acquiring a first motion parameter of at least one dynamic obstacle;
predicting a local motion path of the at least one dynamic obstacle within a first time period according to the first motion parameter;
and determining a target path for navigating the mobile robot according to the local motion path.
By adopting the path planning method provided by the application, the first motion parameter of at least one dynamic obstacle is obtained in advance, namely the motion condition of the dynamic obstacle in the planning area of the mobile robot is obtained. The local motion path of the at least one dynamic obstacle over the first time period may thus be predicted from the first motion parameter. The motion trail of the dynamic barrier in the planning area of the mobile robot in a future period of time is embodied through the local motion path. And then determining a target path for navigating the mobile robot according to the local motion path. The target path is obtained by planning based on the predicted local motion path of the dynamic obstacle, so that the robot does not need to detect the dynamic obstacle in real time when navigating based on the target path, and can avoid collision with the dynamic obstacle. Therefore, the path planning method of the embodiment of the application can meet the requirement of a real-time driving scene, and is high in effectiveness.
Optionally, in the operation of predicting the local motion path of the at least one dynamic obstacle in the first time period, the process of generating the local motion path of the single dynamic obstacle includes:
calculating the budget duration consumed by the mobile robot to travel to the current position of the dynamic obstacle based on the current position of the mobile robot and the second motion parameter;
determining a first time period according to the budget time length;
calculating a plurality of position coordinates of the dynamic obstacle in the first time period according to the first motion parameters of the dynamic obstacle;
and generating a local motion path of the dynamic obstacle according to a plurality of position coordinates.
Optionally, the calculating, according to the first motion parameter of the dynamic obstacle, a plurality of position coordinates of the dynamic obstacle in the first time period includes:
equally dividing the first time period into a plurality of second time periods;
and calculating the position coordinates of the dynamic obstacle in each second time period according to the first motion parameters of the dynamic obstacle and the plurality of second time periods to obtain a plurality of position coordinates.
Optionally, the calculating the position coordinates of the dynamic obstacle in each of the second time periods according to the first motion parameter of the dynamic obstacle and the second time periods includes:
calculating the position coordinates by the following formula:
wherein Δ t represents a duration of the second time period, v represents a linear velocity required when the local movement path of the dynamic obstacle is generated, and ω represents an angular velocity required when the local movement path of the dynamic obstacle is generated; x is the number oftAn instantaneous abscissa representing a node on a local motion path of the dynamic obstacle; y istAn instantaneous ordinate representing a node on a local motion path of the dynamic obstacle; thetatRepresenting the instantaneous angle of the local motion path of the dynamic obstacle to the horizontal direction;xt-1an initial abscissa representing a node on a local motion path of the dynamic obstacle; y ist-1An initial ordinate representing a node on a local motion path of the dynamic obstacle; thetat-1Representing the initial angle of the local motion path of the dynamic barrier with the horizontal.
Optionally, the determining a target path for navigating the mobile robot according to the local motion path includes:
establishing a potential field map according to the local motion path, and extracting a plurality of key points in the potential field map;
and determining the target path according to the plurality of key points.
Optionally, the establishing a potential field map according to the local motion path and extracting a plurality of key points in the potential field map includes:
and establishing the potential field map according to the local motion path and the static obstacle.
Optionally, after determining a target path for navigating the mobile robot according to the local motion path, the method further includes:
acquiring a grid map, the position of the target path in the grid map and the position of a dynamic obstacle;
determining the score of each grid in the grid map according to the position of the target path and the position of the dynamic obstacle; the score is used to represent the probability that the grid is occupied by a dynamic obstacle;
obtaining the score of each grid occupied by the position of the target path;
and if the score meets a preset condition, returning to execute the first motion parameter of the at least one dynamic obstacle so as to update the target path.
In a second aspect, an embodiment of the present application provides a path planning apparatus, including:
the acquisition module is used for acquiring a first motion parameter of at least one dynamic obstacle;
the prediction module is used for predicting a local motion path of the at least one dynamic obstacle in a first time period according to the first motion parameter;
and the determining module is used for determining a target path for navigating the mobile robot according to the local motion path.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the path planning method when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the path planning method.
In a fifth aspect, an embodiment of the present application provides a computer program product, which when run on a terminal device, causes the terminal device to execute the path planning method according to any one of the above first aspects.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a path planning method according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of step S11 of the path planning method according to an embodiment of the present application.
Fig. 3 is a schematic flow chart of a path planning method according to another embodiment of the present application.
Fig. 4 is a schematic diagram of a predicted local movement path of a dynamic obstacle according to an embodiment of the present application.
Fig. 5 is a schematic diagram of generating a potential field map based on a predicted local motion path of a dynamic obstacle according to another embodiment of the present application.
Fig. 6 is a schematic structural diagram of a path planning apparatus according to an embodiment of the present application.
Fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
In order to explain the technical means of the present application, the following description will be given by way of specific examples.
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a path planning method according to an embodiment of the present disclosure. In this embodiment, the path planning method is used for planning a navigation path in a traveling process of a mobile robot, and an execution subject of the path planning method is a terminal device. The terminal device may be the mobile robot itself, or may be a device other than the mobile robot. When the terminal device is other than the mobile robot, data communication can be performed between the terminal device and the mobile robot, so that data interaction between the terminal device and the mobile robot and operations such as control over the mobile robot can be realized. The following embodiments are described by taking the mobile robot as an example of the terminal device:
the path planning method shown in fig. 1 includes the following steps:
s11: a first motion parameter of at least one dynamic obstacle is acquired.
In step S11, the dynamic obstacle is an obstacle having a movement speed and existing in the navigation area of the mobile robot. Such as a basketball rolling within the navigation area of the mobile robot.
The first motion parameter is used for describing the motion state of the dynamic obstacle and is a motion parameter measured by the mobile robot as a reference object. For example, when the mobile robot is used as a reference object, the dynamic obstacle moves leftwards, the current movement speed is 0.5m/s, and the movement acceleration is 0.01m/s2And meanwhile, the current position of the dynamic obstacle is 3m away from the mobile robot.
In some embodiments, the first motion state parameter comprises: at least one of a current moving speed, a moving direction, a moving acceleration, and a linear distance from the mobile robot of the dynamic obstacle.
It can be understood that the first motion parameter may be a first motion parameter corresponding to a current time of the dynamic obstacle, may be a historical motion parameter of the dynamic obstacle, and may also be a motion parameter of the dynamic obstacle corresponding to a next time after the current time.
In this embodiment, the first motion parameter of the dynamic obstacle in the navigation area is acquired by the sensing module of the mobile robot, and the first motion parameter of the dynamic obstacle can be used to describe the motion condition of the dynamic obstacle in the navigation area of the mobile robot, so the first motion parameter of the dynamic obstacle is acquired, that is, the environment information of the navigation area is acquired. The local motion path of the dynamic obstacle in the navigation area is predicted based on the first motion parameter of the dynamic obstacle, so that the target path of the mobile robot in the navigation area is planned better on the basis of considering the local motion path, the navigation of the mobile robot is smoother based on the target path, and the mobile robot does not need to continuously detect the dynamic obstacle and perform path planning again in the process. It should be understood that in all embodiments of the present application, the obtaining of the first motion parameter of the at least one dynamic obstacle is actually obtaining environmental information of the navigation area of the mobile robot, so that the navigation path of the mobile robot may be better planned.
In one embodiment, a perception module of a mobile robot includes: at least one of electronic devices such as a laser radar and a camera. The laser radar is used for transmitting a detection signal (laser beam) to the dynamic obstacle, comparing a received signal (target echo) reflected from the dynamic obstacle with the transmitted signal, and processing the signal to obtain a motion parameter of the dynamic obstacle, such as at least one of the distance and the direction between the dynamic obstacle and the mobile robot, the height, the speed, the posture and the shape of the dynamic obstacle. The camera is used for acquiring an environment image in a navigation area of the mobile robot, namely, the distribution position of the dynamic robot can be confirmed through the environment image. The first motion parameters of the dynamic obstacle include the motion parameters of the dynamic obstacle and its distributed position within the navigation area of the mobile robot.
As shown in fig. 4(a), there are 4 dynamic obstacles in the navigation area of the mobile robot, which are respectively dynamic obstacle 1, dynamic obstacle 2, dynamic obstacle 3, and dynamic obstacle 4, and since the 4 dynamic obstacles are in dynamic motion, the 4 dynamic obstacles may collide with the mobile robot during the traveling process of the mobile robot, so it is necessary to obtain the first motion parameters of the 4 dynamic obstacles, that is, to obtain the motion states of the 4 dynamic obstacles, so as to predict the local motion paths corresponding to the dynamic obstacles based on the first motion parameters of the 4 dynamic obstacles, and further to better plan the target path of the mobile robot in the navigation area based on considering the 4 local motion paths, as shown in fig. 4 (b), so that the mobile robot can better avoid the motion area of the 4 dynamic obstacles based on the target path Thereby avoiding collisions with dynamic obstacles.
As to when the first motion parameter of the at least one dynamic obstacle is acquired, the following two scenarios may be included, but not limited to.
Scene 1: and if the information for instructing the mobile robot to operate the operation is detected, starting to acquire a first motion parameter of at least one dynamic obstacle.
For example, when information instructing the mobile robot to run a job is detected at a certain time point, a sensing module of the mobile robot starts to detect and acquire a first motion parameter of a dynamic obstacle in a navigation area. For example, the mobile robot detects by using the laser radar to obtain at least one of the parameters of the distance, the direction, the height, the speed, the attitude, the shape and the like of a dynamic obstacle from the mobile robot in the navigation area.
Scene 2: and if the mobile robot is detected to move to the navigation area, starting to acquire a first motion parameter of at least one dynamic obstacle.
Illustratively, when the mobile robot is detected to travel to a waiting hall of a railway station, the acquisition of the first motion parameters of the at least one dynamic obstacle is started.
It should be understood that, in practical applications, in order to facilitate planning and obtaining the navigation path of the mobile robot according to the first motion parameter of the dynamic obstacle, the first motion parameter of at least one dynamic obstacle may be obtained and then stored in the storage area of the terminal device in advance, so that a subsequent terminal device can plan the navigation path of the mobile robot according to the stored first motion parameter. Or, after obtaining the first motion parameter of at least one dynamic obstacle, directly sending the first motion parameter to an independent terminal device, subsequently receiving the navigation path of the mobile robot planned by the processing device, and returning the navigation path to the mobile robot.
S12: and predicting a local motion path of the at least one dynamic obstacle in the first time period according to the first motion parameter.
In step S12, the local motion path is a set of a plurality of position nodes predicted to move the dynamic obstacle within a time period based on the first motion parameter. For example, based on the first motion parameter of the dynamic obstacle 1, A, B, C is predicted at the position of the dynamic obstacle 1 within 10s, and the path connecting the three positions is the local motion path of the dynamic obstacle 1 in the time period.
The first time period is the longest prediction time period set according to the prediction demand. For example, if it is desired to confirm whether or not the dynamic obstacle will move to collide with the target position point within the time period consumed for the mobile robot to move from the current position to the target position point, the movement path of the dynamic obstacle within the time period needs to be predicted, and the first time period may be set to 100s according to the setting. Alternatively, if it is desired to confirm whether the mobile robot moves to the current position of the dynamic obstacle within the time period consumed for moving from the current position to the current position point of the dynamic obstacle, it is necessary to predict the movement path of the dynamic obstacle within the time period, and the first time period may be set to 80s according to the setting.
It should be noted that, in all embodiments of the present application, the local motion path of the at least one dynamic obstacle in the first time period is predicted according to the first motion parameter, that is, the environment change condition of the navigation area of the mobile robot in the future first time period is predicted, so as to most effectively plan the navigation path of the mobile robot according to the predicted future environment.
For example, the motion state of the dynamic state is described based on a first motion parameter of the dynamic obstacle and a Constant Velocity (CV) model, a Constant Acceleration (CA) model, and a Current Statistical model (CS) model, and three state equations describing the dynamic obstacle are obtained respectively. Then, the motion states of the dynamic obstacle described by the three state equations are estimated based on an Interactive Multiple Model (IMM) algorithm, and three estimation results are fused, so that the local motion path of the dynamic obstacle in the first time period is predicted. The IMM algorithm firstly utilizes a Kalman filter to respectively estimate the motion states of the dynamic obstacles described by the three models, the Kalman filter takes the current position of the obstacle acquired by the mobile robot as the sample input of the filter, and the estimation value of the motion state is obtained according to a state updating equation; and then calculating model mixing probability, and fusing the motion states of the three models estimated by the Kalman filter according to the mixing probability to obtain a final motion state estimation value of the dynamic obstacle, namely predicting to obtain a local motion path of the dynamic obstacle in a first time period.
As shown in fig. 4(a), according to the existence of the dynamic obstacle 1, the dynamic obstacle 2, the dynamic obstacle 3, and the dynamic obstacle 4 in the navigation area of the mobile robot, the local motion path of each dynamic obstacle in the first time period is predicted based on the first motion parameters of each of the 4 dynamic obstacles, such as the initial speed, the acceleration, the motion direction, and the like of the dynamic obstacle. As shown in fig. 4(a), the local movement path of the dynamic obstacle 1 corresponds to a path B in the drawing, the local movement path of the dynamic obstacle 2 corresponds to a path a in the drawing, the local movement path of the dynamic obstacle 3 corresponds to a path C in the drawing, and the local movement path of the dynamic obstacle 4 corresponds to a path D in the drawing.
As a possible implementation manner of this embodiment, in the operation of predicting the local motion path of at least one dynamic obstacle in the first time period, the local motion path generation process for a single dynamic obstacle includes:
s21: and calculating the budget time length consumed by the mobile robot to travel to the current position of the dynamic obstacle based on the current position of the mobile robot and the second motion parameters.
S22: and determining a first time period according to the budget duration.
S23: and calculating a plurality of position coordinates of the dynamic obstacle in a first time period according to the first motion parameters of the dynamic obstacle.
S24: and generating a local motion path of the dynamic obstacle according to the position coordinates.
In this embodiment, the current position is a spatial position corresponding to the mobile robot when the first motion parameter of the dynamic obstacle is obtained in the acquisition of the mobile robot in the navigation area of the mobile robot.
The second motion parameter is used for describing the motion state of the mobile robot, and is the motion parameter of the mobile robot which is correspondingly measured when the first motion parameter of the dynamic obstacle is obtained through the measurement of the mobile robot. For example, when the first motion parameter of the dynamic obstacle 1 is measured, the second motion parameter of the mobile robot is measured, and the second motion parameter comprises the motion speed and the motion acceleration of the mobile robot, for example, the motion speed of the mobile robot is 0.6m/s, and the motion acceleration is 0.01m/s2。
In one embodiment, the perception module of the mobile robot further comprises: at least one of an Inertial Measurement Unit (IMU), a code table, and other electronic devices. The inertial measurement unit comprises at least one of an accelerometer and a gyroscope, wherein the accelerometer is used for detecting and obtaining the acceleration of the mobile robot, and the gyroscope is used for detecting and obtaining the angular velocity of the mobile robot. The code table is used for detecting and obtaining the running speed of the mobile robot. And detecting through an inertia measurement unit and a code table to obtain a second motion parameter of the mobile robot.
In some embodiments, the second motion parameter comprises: at least one of a current motion velocity and a motion acceleration of the mobile robot. Specifically, the current movement speed of the mobile robot is obtained through measurement of a code table in a sensing module of the mobile robot, and the acceleration of the mobile robot is obtained through detection of an inertia measurement unit.
The estimated time is the estimated time consumed by the mobile robot to linearly travel to the current position of the dynamic obstacle. For example, in FIG. 4(a), the mobile robot is located 4m away from the dynamic obstacle 4, and the mobile robot moves at an initial movement speed of 0m/s and a movement acceleration of 2m/s2Namely, the estimated time length of straight line travel from the current position to the dynamic obstacle 4 is calculated to be 2 s.
It is understood that if there are multiple dynamic obstacles in the navigation area of the mobile robot, the budget time consumed for the mobile robot to travel to the current position of each dynamic obstacle may be different.
As an example and not by way of limitation, in order to reduce the calculation amount of calculating the position coordinates of the dynamic obstacle, it is preferable that the budget duration is equal to the first time period, that is, only a plurality of position coordinates of the dynamic obstacle within the budget duration during which the mobile robot travels to the current position of the dynamic obstacle are calculated, and the local movement path of the dynamic obstacle within the budget duration is obtained.
In some embodiments, the estimated duration is the longest estimated duration consumed by the mobile robot to travel to the current position of the dynamic obstacle after avoiding the obstacle existing between the mobile robot and the dynamic obstacle. For example, the length of time it takes for the mobile robot to travel to the current position of the dynamic obstacle 2 after avoiding the dynamic obstacles 3 and 4.
In this embodiment, after determining the estimated time period consumed by the mobile robot to travel to the current position of the dynamic obstacle, in order to obtain the motion condition of the dynamic obstacle in the estimated time period in the future and to conveniently plan the target path of the mobile robot according to the motion condition, a plurality of position coordinates which are possibly reached by the motion of the dynamic obstacle within the estimated time period are calculated according to the first motion parameter of the dynamic obstacle and the estimated time period. And generating a local motion path of the dynamic barrier within the future estimated time length based on the plurality of position coordinates, namely, performing path planning of the mobile robot based on the predicted motion trajectory of the dynamic barrier within the future estimated time length, so as to avoid collision with the dynamic barrier with a high probability.
Illustratively, in FIG. 4(a), the mobile robot is 4m away from the dynamic obstacle 4, and the mobile robot moves at an initial movement speed of 0m/s and a movement acceleration of 2m/s2That is, the estimated time length of the straight line from the current position to the dynamic barrier 4 is 2s, the right straight line motion of the dynamic barrier 4 is obtained by calculation, the motion speed is 1.5m/s, and the acceleration is 0m/s2And calculating a plurality of position coordinates of the dynamic obstacle within the estimated time length 2s, such as position coordinate 1(1.5, 0), position coordinate 2(3, 0), position coordinate 3(4.5, 0) and position coordinate 4(6, 0), and connecting the 4 position coordinates to form a local motion path of the dynamic obstacle 4 within 2s, such as a D path in the figure.
In an embodiment of the present application, calculating a plurality of position coordinates of the dynamic obstacle in a first time period according to the first motion parameter and the estimated duration of the dynamic obstacle includes:
the first time period is divided into a plurality of second time periods.
And calculating the position coordinates of the dynamic obstacle in each second time period according to the first motion parameters of the dynamic obstacle and the plurality of second time periods to obtain a plurality of position coordinates.
In this embodiment, the first time period is equal to the budget time period, and the budget time period is taken as the first time period. In order to better calculate and calculate a plurality of position coordinates of the dynamic obstacle in a first time period and predict a local motion path of the dynamic obstacle with the best effect, the first time period is divided into a plurality of second time periods, and the position coordinates of the dynamic obstacle in each second time period are calculated and obtained to obtain a plurality of position coordinates.
It will be appreciated that from the first motion parameters of the dynamic barrier and the plurality of second time periods, there may be one or more, or even zero, position coordinates of the dynamic barrier calculated during each of the second time periods.
Illustratively, in FIG. 4(a), the mobile robot is 4m away from the dynamic obstacle 4, and the mobile robot moves at an initial movement speed of 0m/s and a movement acceleration of 2m/s2Namely, the estimated time length of the straight line from the current position to the dynamic barrier 4 is calculated to be 2s, and the 2s is divided into 4 second time periods which are respectively 0-0.5 s, 0.6 s-1 s, 1.1 s-1.5 s and 1.6 s-2 s.
Then, according to the first motion parameter of the dynamic barrier and the 4 second time periods, for example, according to the right linear motion of the dynamic barrier 4, the motion speed is 1.5m/s, and the acceleration is 0m/s2And calculating a plurality of position coordinates of the dynamic obstacle within the budget time length 2s according to the parameter information and the 4 second time periods, wherein the position coordinates 1 corresponding to the 1 st time period 0-0.5 s are (1.5, 0), the position coordinates 2 corresponding to the 2 nd time period 0.6 s-1 s are (3, 0), the position coordinates 3 corresponding to the 3 rd time period 1.1 s-1.5 s are (4.5, 0), and the position coordinates 4 corresponding to the 4 th time period 1.6 s-2 s are (6, 0), and connecting the 4 position coordinates to be used as a local motion path of the dynamic obstacle 4 within 2s, such as a D path in the figure.
In some embodiments, the position coordinates of the dynamic obstacle corresponding to the last time point in each second time period are calculated according to the first motion parameters of the dynamic obstacle and the second time periods, so as to obtain a plurality of position coordinates.
As a possible implementation manner of this embodiment, calculating the position coordinates of the dynamic obstacle in each second time period according to the first motion parameter of the dynamic obstacle and the plurality of second time periods includes:
the position coordinates are calculated by the following formula:
where Δ t represents a duration of the second time period, v represents a linear velocity required when the local movement path of the dynamic obstacle is generated, and ω represents an angular velocity required when the local movement path of the dynamic obstacle is generated; x is the number oftAn instantaneous abscissa representing a node on a local motion path of the dynamic obstacle; y istAn instantaneous ordinate representing a node on a local motion path of the dynamic obstacle; thetatRepresenting the instantaneous angle of the local motion path of the dynamic obstacle to the horizontal direction; x is the number oft-1An initial abscissa representing a node on a local motion path of the dynamic obstacle; y ist-1An initial ordinate representing a node on a local motion path of the dynamic obstacle; thetat-1Representing the initial angle of the local motion path of the dynamic barrier with the horizontal.
S13: and determining a target path for navigating the mobile robot according to the local motion path.
In step S13, the target path is a path in the environment of the predicted navigation area including the local motion path of the dynamic obstacle.
For example, as shown in (B) of fig. 4, local motion paths corresponding to the dynamic obstacles 1, 2, 3, and 4 are predicted, for example, the local motion path of the dynamic obstacle 1 corresponds to a path B of the figure, the local motion path of the dynamic obstacle 2 corresponds to a path a of the figure, the local motion path of the dynamic obstacle 3 corresponds to a path C of the figure, and the local motion path of the dynamic obstacle 4 corresponds to a path D of the figure, a target path for navigating the mobile robot and avoiding the 4 local motion paths is determined according to the 4 local motion paths, for example, the target path of the figure (B), the mobile robot can plan the obtained target path based on the predicted local motion path of the dynamic obstacle, navigate more smoothly, and the mobile robot does not need to detect the dynamic obstacle continuously during the period, and path planning is carried out again.
It is understood that, according to the local motion path of the dynamic obstacle, the determined target path is a path for navigating the mobile robot in the first time period in the future, and is a determined path. The process that the mobile robot navigates based on the path does not need to plan the target path again under the condition that the dynamic barrier does not have sudden state change, and the mobile robot continuously navigates based on the target path, so that the movement is smoother, the movement is more stable, and the CPU consumption of the mobile robot is greatly reduced.
Referring to fig. 5, in an embodiment of the present application, a path planning method is provided, which mainly relates to a process of determining a target path according to a local motion path. The method comprises the following steps:
determining a target path for navigating the mobile robot according to the local motion path, comprising:
establishing a potential field map according to the local motion path, and extracting a plurality of key points in the potential field map;
and determining a target path for navigating the mobile robot according to the plurality of key points.
In this embodiment, in a map including a local motion path, a potential field map is constructed by using a gravitational potential function and a repulsive potential function in an artificial potential field method and combining the local motion path with a reference area occupied by the local motion path of the dynamic obstacle.
Specifically, the numerical value of the acting force of the local motion path of the dynamic barrier on the mobile robot represents the numerical value result of adopting a repulsive force function, and the resultant force direction is opposite to the motion direction of the robot and is expressed as an obstruction; the numerical value of the acting force of the key point to the mobile robot represents the numerical result of adopting the gravitational potential function, the resultant force direction is consistent with the motion direction of the robot, the attraction effect is shown, and the action can be used as a reference for path planning, such as a plurality of key points in (d) of fig. 5.
Specifically, a specific implementation process of constructing and obtaining the potential field map by using the artificial market method can refer to a related implementation scheme in the prior art, and is not described herein again.
As shown in fig. 5, a potential field map is constructed by using a gravitational potential function and a repulsive potential function in the artificial potential field method and combining local motion paths, with reference to regions occupied by the local motion paths corresponding to each of the dynamic obstacles 1, 2, 3, and 4. In fig. 5, the darker the color, the higher the possibility that the dynamic obstacle will appear in the first time period, and therefore, when planning the path of the mobile robot, the area of the local movement path of the dynamic obstacle should be avoided.
With reference to fig. 5, in an embodiment of the present application, there is a possible scenario that not only dynamic obstacles but also obstacles without moving speed exist in a navigation area of a mobile robot, and when planning a target path for the robot to navigate, consideration is also given to avoiding the part of static obstacles.
Therefore, establishing a potential field map according to the local motion path, and extracting a plurality of key points in the potential field map, includes:
and establishing a potential field map according to the local motion path and the static barrier, and extracting a plurality of key points in the potential field map.
In this embodiment, in order to plan the navigation path of the mobile robot better, it is also necessary to take into consideration the route planning of an obstacle in a stationary state in the navigation area of the mobile robot.
The static obstacle is an obstacle that is present in a stationary state in a navigation area with the mobile robot. For example a stationary large stone in the navigation area of the mobile robot.
In an embodiment of the present application, there is a possible scenario that not only dynamic obstacles but also obstacles without moving speed exist in a navigation area of a mobile robot, and when planning a target path for the robot to navigate, consideration needs to be given to avoiding the partially stationary obstacles.
Therefore, determining a target path for navigating the mobile robot according to the local motion path includes:
and determining a target path for navigating the mobile robot according to the local motion path and the static obstacle.
With reference to fig. 3, an embodiment of the present application provides a path planning method, which mainly relates to a process in which a mobile robot performs navigation based on a target path, detects whether collision with an obstacle in a navigation area occurs, and determines whether the target path needs to be updated. The method comprises the following steps:
s31, acquiring a grid map, the position of a target path in the grid map and the position of a dynamic obstacle;
s32, determining the score of each grid in the grid map according to the position of the target path and the position of the dynamic obstacle; the score is used to represent the probability that the grid is occupied by a dynamic obstacle;
s33: obtaining the score of each grid occupied by the position of the target path;
s34: and if the score meets the preset condition, returning to execute the first motion parameter of the at least one dynamic obstacle so as to update the target path.
In the embodiment, the map representation method in robotics includes a feature map, a topological map, a grid map, and a direct representation method. The grid map is used for dividing the environment into a series of grids, wherein each grid is preset with a score, if the grid is occupied by the obstacle, the score of the grid is updated, and the probability that the grid is occupied by the obstacle is represented through the score.
For example, in the initial grid map, the scores of all the grids are 0, and since no obstacle exists on the target route, the score of each grid occupied by the position of the target route is also 0 at the beginning, that is, the probability that the target route is occupied by the obstacle is 0 at this time. If the dynamic obstacle moves to the area where the target path is located in the process of moving the mobile robot, the grid where the corresponding target path is located is occupied by the obstacle, that is, the score 1 of the grid is updated, that is, the probability that the grid is occupied by the obstacle is 100%.
In the embodiment of the application, in the process of navigating the mobile robot based on the target path, in order to detect whether the moving robot in the process of traveling collides with the dynamic obstacle, the grid map, the position of the target path in the grid map and the position of the dynamic obstacle are acquired.
It will be appreciated that after the target path is obtained, the target path is fixedly mapped in the grid map, and the position of the target path is mapped in the grid map with a certain score for each grid. In the process of moving the mobile robot, the mobile robot dynamically refreshes the grid map, namely, the grid map and the positions of the dynamic obstacles in the grid map are updated and acquired in real time or according to a specified period so as to determine whether the dynamic obstacles occupy the grid mapped by the target path position.
In this embodiment, the preset condition is a logic process for determining whether the score of the grid occupied by the target path is normal. The method comprises the steps of periodically obtaining the score of each grid of a grid map mapped by the position of a target path, determining whether the score of each grid changes, if the score changes, for example, changes from 0 to 1, namely, the fact that a dynamic obstacle in reality moves to the target path, the corresponding dynamic obstacle occupies the grid of the target path mapped in the grid map, and when the mobile robot continues to travel, the mobile robot collides with the obstacle, so that the first motion parameter of at least one dynamic obstacle needs to be obtained again to update the target path.
As can be seen from the above, in the path planning method provided in the embodiment of the present application, the first motion parameter of at least one dynamic obstacle is obtained in advance, that is, the motion condition of the dynamic obstacle in the planning area of the mobile robot is obtained. Therefore, the local motion path of the at least one dynamic obstacle in the first time period can be predicted according to the first motion parameter, and the motion trail of the dynamic obstacle in the planning area of the mobile robot in the future time period is embodied through the local motion path. A target path for navigating the mobile robot is then determined from the local motion path, such that the mobile robot may plan a resulting target path based on the predicted local motion path of the dynamic obstacle. Because the navigation path is planned based on the motion situation of the dynamic barrier in a future time, when the mobile robot navigates based on the target path, the mobile robot can effectively avoid the motion area of the dynamic barrier and avoid colliding with the barrier, and during the period, the mobile robot does not need to continuously detect the dynamic barrier in the navigation area to plan the path again and frequently update the target path, so that the mobile robot can travel more smoothly when navigating the path, and the traveling efficiency is higher.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 6 shows a block diagram of a path planning apparatus provided in the embodiment of the present application, which corresponds to the method in the foregoing embodiment, and only shows portions related to the embodiment of the present application for convenience of description.
Referring to fig. 6, the apparatus includes:
an obtaining module 101, configured to obtain a first motion parameter of at least one dynamic obstacle;
the prediction module 102 is configured to predict a local motion path of the at least one dynamic obstacle in a first time period according to the first motion parameter;
a determining module 103, configured to determine a target path for navigating the mobile robot according to the local motion path.
Optionally, the prediction module 102 includes a first calculation unit, a second calculation unit and a generation unit.
The first calculating unit is used for calculating the budget duration consumed by the mobile robot to travel to the current position of the dynamic obstacle based on the current position of the mobile robot and the second motion parameter;
the second calculation unit is used for calculating a plurality of position coordinates of the dynamic barrier in a first time period according to the first motion parameter and the budget duration of the dynamic barrier;
and the generating unit is used for generating a local motion path of the dynamic obstacle according to the position coordinates.
Optionally, the second calculating unit is further configured to divide the first time period into a plurality of second time periods, and calculate the position coordinates of the dynamic obstacle in each of the second time periods according to the first motion parameter of the dynamic obstacle and the plurality of second time periods, so as to obtain a plurality of position coordinates.
Optionally, the second calculating unit is further configured to calculate, according to the first motion parameter of the dynamic obstacle and the plurality of second time periods, position coordinates of the dynamic obstacle in each of the second time periods, and includes:
the position coordinates are calculated by the following formula:
where Δ t represents a duration of the second time period, v represents a linear velocity required when the local movement path of the dynamic obstacle is generated, and ω represents an angular velocity required when the local movement path of the dynamic obstacle is generated; x is the number oftAn instantaneous abscissa representing a node on a local motion path of the dynamic obstacle; y istAn instantaneous ordinate representing a node on a local motion path of the dynamic obstacle; thetatRepresenting the instantaneous angle of the local motion path of the dynamic obstacle to the horizontal direction; x is the number oft-1An initial abscissa representing a node on a local motion path of the dynamic obstacle; y ist-1An initial ordinate representing a node on a local motion path of the dynamic obstacle; thetat-1Representing the initial angle of the local motion path of the dynamic barrier with the horizontal.
Optionally, the determining module 103 includes an establishing unit, an extracting unit, and a first determining unit.
The establishing unit is used for establishing a potential field map according to the local motion path;
the extraction unit is used for extracting a plurality of key points in the potential field map;
a first determining unit for determining a target path for navigating the mobile robot according to the plurality of key points.
Optionally, the establishing unit is further configured to establish a potential field map according to the local movement path and the static obstacle.
Optionally, the obtaining module 101 further includes a first obtaining unit, a second obtaining unit, a third obtaining unit, and a second determining unit.
The system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring a grid map, the position of a target path in the grid map and the position of a dynamic obstacle;
the second determining unit is used for determining the score of each grid in the grid map according to the position of the target path and the position of the dynamic obstacle; the score is used to represent the probability that the grid is occupied by a dynamic obstacle;
a second acquisition unit configured to acquire a score of each grid occupied by a position of the target path;
and the third acquisition unit is used for returning to execute the acquisition of the first motion parameter of the at least one dynamic obstacle to update the target path if the score meets the preset condition.
Fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 7, the terminal device 7 of this embodiment includes: at least one processor 70 (only one processor is shown in fig. 7), a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, the steps of any of the various path planning method embodiments described above being implemented by the processor 70 when the computer program 72 is executed by the processor 70.
The terminal device 7 may be a desktop computer, a notebook, a palm computer, a cloud server, or a mobile robot. The terminal device may include, but is not limited to, a processor 70, a memory 71. Those skilled in the art will appreciate that fig. 7 is only an example of the terminal device 7, and does not constitute a limitation to the terminal device 7, and may include more or less components than those shown, or combine some components, or different components, for example, and may further include input/output devices, network access devices, and the like.
The Processor 70 may be a Central Processing Unit (CPU), and the Processor 70 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), off-the-shelf Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may in some embodiments be an internal storage unit of the terminal device 7, such as a hard disk or a memory of the terminal device 7. The memory 71 may also be an external storage device of the terminal device 7 in other embodiments, such as a plug-in hard disk provided on the terminal device 7, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 71 may also include both an internal storage unit of the terminal device 7 and an external storage device. The memory 71 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of a computer program. The memory 71 may also be used to temporarily store data that has been output or is to be output.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps that can be implemented in the above method embodiments.
The embodiments of the present application provide a computer program product, which when running on a terminal device, enables the terminal device to implement the steps in the above method embodiments when executed.
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, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer readable storage medium and used by a processor to implement the steps of the embodiments of the methods described above. 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 at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, 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. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
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 embodiment.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A method of path planning, the method comprising:
acquiring a first motion parameter of at least one dynamic obstacle;
predicting a local motion path of the at least one dynamic obstacle within a first time period according to the first motion parameter;
and determining a target path for navigating the mobile robot according to the local motion path.
2. The method of claim 1, wherein in the operation of predicting the local motion path of the at least one dynamic obstacle over the first time period, the local motion path generation process for a single dynamic obstacle comprises:
calculating the budget duration consumed by the mobile robot to travel to the current position of the dynamic obstacle based on the current position of the mobile robot and the second motion parameter;
determining the first time period according to the budget duration;
calculating a plurality of position coordinates of the dynamic obstacle in the first time period according to the first motion parameters of the dynamic obstacle;
and generating a local motion path of the dynamic obstacle according to a plurality of position coordinates.
3. The method of claim 2, wherein said calculating a plurality of position coordinates of the dynamic obstacle during the first time period based on the first motion parameter of the dynamic obstacle comprises:
equally dividing the first time period into a plurality of second time periods;
and calculating the position coordinates of the dynamic obstacle in each second time period according to the first motion parameters of the dynamic obstacle and the plurality of second time periods to obtain a plurality of position coordinates.
4. The method of claim 3, wherein said calculating the position coordinates of the dynamic obstacle during each of the second time periods based on the first motion parameters of the dynamic obstacle and the second time periods comprises:
calculating the position coordinates by the following formula:
wherein Δ t represents a duration of the second time period, v represents a linear velocity required when the local movement path of the dynamic obstacle is generated, and ω represents an angular velocity required when the local movement path of the dynamic obstacle is generated; x is the number oftAn instantaneous abscissa representing a node on a local motion path of the dynamic obstacle; y istAn instantaneous ordinate representing a node on a local motion path of the dynamic obstacle; thetatRepresenting the instantaneous angle of the local motion path of the dynamic obstacle to the horizontal direction; x is the number oft-1An initial abscissa representing a node on a local motion path of the dynamic obstacle; y ist-1An initial ordinate representing a node on a local motion path of the dynamic obstacle; thetat-1Representing the initial angle of the local motion path of the dynamic barrier with the horizontal.
5. The method of claim 1, wherein determining a target path for navigating the mobile robot based on the local motion path comprises:
establishing a potential field map according to the local motion path, and extracting a plurality of key points in the potential field map;
and determining the target path according to the plurality of key points.
6. The method of claim 5, wherein said building a potential field map from said local motion path comprises:
and establishing the potential field map according to the local motion path and the static obstacle.
7. The method of claim 1, wherein after determining a target path for navigating the mobile robot based on the local motion path, further comprising:
acquiring a grid map, the position of the target path in the grid map and the position of a dynamic obstacle;
determining the score of each grid in the grid map according to the position of the target path and the position of the dynamic obstacle; the score is used to represent the probability that the grid is occupied by a dynamic obstacle;
obtaining the score of each grid occupied by the position of the target path;
and if the score meets a preset condition, returning to execute the first motion parameter of the at least one dynamic obstacle so as to update the target path.
8. A path planning apparatus, comprising:
the acquisition module is used for acquiring a first motion parameter of at least one dynamic obstacle;
the prediction module is used for predicting a local motion path of the at least one dynamic obstacle in a first time period according to the first motion parameter;
and the determining module is used for determining a target path for navigating the mobile robot according to the local motion path.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010752888.XA CN112540609A (en) | 2020-07-30 | 2020-07-30 | Path planning method and device, terminal equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010752888.XA CN112540609A (en) | 2020-07-30 | 2020-07-30 | Path planning method and device, terminal equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112540609A true CN112540609A (en) | 2021-03-23 |
Family
ID=75013455
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010752888.XA Pending CN112540609A (en) | 2020-07-30 | 2020-07-30 | Path planning method and device, terminal equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112540609A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109782763A (en) * | 2019-01-18 | 2019-05-21 | 中国电子科技集团公司信息科学研究院 | A kind of method for planning path for mobile robot under dynamic environment |
| CN113467462A (en) * | 2021-07-14 | 2021-10-01 | 中国人民解放军国防科技大学 | Pedestrian accompanying control method and device for robot, mobile robot and medium |
| CN114137955A (en) * | 2021-10-26 | 2022-03-04 | 中国人民解放军军事科学院国防科技创新研究院 | Multi-robot rapid collaborative map building method based on improved market method |
| CN116300979A (en) * | 2023-05-26 | 2023-06-23 | 君华高科集团有限公司 | Robot cruise path generation system and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008050206A1 (en) * | 2008-10-01 | 2010-05-27 | Micro-Star International Co., Ltd., Jung-Ho City | Route planning method for mobile robot device, involves consecutively spreading map grid from point of origin to target in direction to adjacent map grids until map grids contact with each other, and defining map grids as movement route |
| CN106774347A (en) * | 2017-02-24 | 2017-05-31 | 安科智慧城市技术（中国）有限公司 | Robot path planning method, device and robot under indoor dynamic environment |
| CN107357293A (en) * | 2017-07-31 | 2017-11-17 | 上海应用技术大学 | Method for planning path for mobile robot and system |
| CN108762264A (en) * | 2018-05-22 | 2018-11-06 | 重庆邮电大学 | The dynamic obstacle avoidance method of robot based on Artificial Potential Field and rolling window |
| CN109782763A (en) * | 2019-01-18 | 2019-05-21 | 中国电子科技集团公司信息科学研究院 | A kind of method for planning path for mobile robot under dynamic environment |
| CN111290385A (en) * | 2020-02-19 | 2020-06-16 | 深圳前海达闼云端智能科技有限公司 | Robot path planning method, robot, electronic device and storage medium |
-
2020
- 2020-07-30 CN CN202010752888.XA patent/CN112540609A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008050206A1 (en) * | 2008-10-01 | 2010-05-27 | Micro-Star International Co., Ltd., Jung-Ho City | Route planning method for mobile robot device, involves consecutively spreading map grid from point of origin to target in direction to adjacent map grids until map grids contact with each other, and defining map grids as movement route |
| CN106774347A (en) * | 2017-02-24 | 2017-05-31 | 安科智慧城市技术（中国）有限公司 | Robot path planning method, device and robot under indoor dynamic environment |
| CN107357293A (en) * | 2017-07-31 | 2017-11-17 | 上海应用技术大学 | Method for planning path for mobile robot and system |
| CN108762264A (en) * | 2018-05-22 | 2018-11-06 | 重庆邮电大学 | The dynamic obstacle avoidance method of robot based on Artificial Potential Field and rolling window |
| CN109782763A (en) * | 2019-01-18 | 2019-05-21 | 中国电子科技集团公司信息科学研究院 | A kind of method for planning path for mobile robot under dynamic environment |
| CN111290385A (en) * | 2020-02-19 | 2020-06-16 | 深圳前海达闼云端智能科技有限公司 | Robot path planning method, robot, electronic device and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 白巧克力: "机器⼈局部避障的动态窗口法(dynamic window approach）", pages 1 - 3, Retrieved from the Internet <URL:https://heyijia.blog.csdn.net/article/details/44983551?spm=1001.2014.3001.5506> * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109782763A (en) * | 2019-01-18 | 2019-05-21 | 中国电子科技集团公司信息科学研究院 | A kind of method for planning path for mobile robot under dynamic environment |
| CN109782763B (en) * | 2019-01-18 | 2021-11-23 | 中国电子科技集团公司信息科学研究院 | Mobile robot path planning method in dynamic environment |
| CN113467462A (en) * | 2021-07-14 | 2021-10-01 | 中国人民解放军国防科技大学 | Pedestrian accompanying control method and device for robot, mobile robot and medium |
| CN113467462B (en) * | 2021-07-14 | 2023-04-07 | 中国人民解放军国防科技大学 | Pedestrian accompanying control method and device for robot, mobile robot and medium |
| CN114137955A (en) * | 2021-10-26 | 2022-03-04 | 中国人民解放军军事科学院国防科技创新研究院 | Multi-robot rapid collaborative map building method based on improved market method |
| CN116300979A (en) * | 2023-05-26 | 2023-06-23 | 君华高科集团有限公司 | Robot cruise path generation system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112540609A (en) | Path planning method and device, terminal equipment and storage medium | |
| JP7271702B2 (en) | Learning Method and System for Autonomous Driving Agent Based on Deep Reinforcement Learning | |
| CN110955242B (en) | Robot navigation method, system, robot and storage medium | |
| CN110488818B (en) | Laser radar-based robot positioning method and device and robot | |
| CN110320910B (en) | Vehicle avoidance control method and device, electronic equipment and storage medium | |
| CN111805535A (en) | Positioning navigation method, device and computer storage medium | |
| CN110942474A (en) | Robot target tracking method, device and storage medium | |
| JP5679121B2 (en) | Robot motion prediction control method and apparatus | |
| CN113848893A (en) | Robot navigation method, device, equipment and storage medium | |
| US20200409388A1 (en) | Controller, control method, and program | |
| US20220319054A1 (en) | Generating scene flow labels for point clouds using object labels | |
| Huang et al. | An autonomous UAV navigation system for unknown flight environment | |
| CN115903773A (en) | Mobile object control device, mobile object, learning device and method, and storage medium | |
| JP2020042516A (en) | Movement path generation device, mobile device, movement path generation method, and program | |
| Fabiani et al. | Tracking an unpredictable target among occluding obstacles under localization uncertainties | |
| CN114571460A (en) | Robot control method, device and storage medium | |
| CN114593735A (en) | Pose prediction method and device | |
| CN113934219A (en) | Robot automatic obstacle avoidance method, system, equipment and medium | |
| CN113359705A (en) | Path planning method, formation cooperative operation method and equipment | |
| Neuman et al. | Anytime policy planning in large dynamic environments with interactive uncertainty | |
| CN117232531B (en) | Robot navigation planning method, storage medium and terminal equipment | |
| Khan et al. | Dynamic Kalman filter-based velocity tracker for Intelligent vehicle | |
| CN114440882B (en) | Multi-intelligent home mobile device and collaborative road-finding anti-collision method thereof | |
| WO2022246802A1 (en) | Driving strategy determination method and apparatus, device, and vehicle | |
| JP7459238B2 (en) | Autonomous driving optimization method and system based on reinforcement learning based on user preferences |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |