CN115145261A - Mobile robot global path planning method following pedestrian specifications under human-computer coexistence - Google Patents
Mobile robot global path planning method following pedestrian specifications under human-computer coexistence Download PDFInfo
- Publication number
- CN115145261A CN115145261A CN202210360087.8A CN202210360087A CN115145261A CN 115145261 A CN115145261 A CN 115145261A CN 202210360087 A CN202210360087 A CN 202210360087A CN 115145261 A CN115145261 A CN 115145261A
- Authority
- CN
- China
- Prior art keywords
- pedestrian
- grid
- von
- pedestrians
- mixed
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000009826 distribution Methods 0.000 claims abstract description 51
- 230000003068 static effect Effects 0.000 claims abstract description 12
- 238000012545 processing Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000010845 search algorithm Methods 0.000 claims description 9
- 238000013179 statistical model Methods 0.000 claims description 8
- 238000007476 Maximum Likelihood Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 3
- 239000004744 fabric Substances 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 241000282414 Homo sapiens Species 0.000 description 2
- 229910003460 diamond Inorganic materials 0.000 description 2
- 239000010432 diamond Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000003064 k means clustering Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 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, 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
- G05D1/0212—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, 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
- G05D1/0212—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
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to a global path planning method and a device for a mobile robot, comprising the following steps: responding to the path planning request, and acquiring pedestrian information in the target area; classifying the pedestrians through a static map according to the positions of the pedestrians to obtain a classification result; acquiring pedestrian information of pedestrians in each grid, and performing clustering processing on the moving direction of the pedestrians to obtain a plurality of clustering results; determining the mixed Von-Mises distribution of each grid according to the clustering result, and determining the pedestrian moving direction preference of the grids according to the mixed Von-Mises distribution; and determining the optimal global moving path of the mobile robot according to the mixed Von-Mises distribution of each grid and the moving direction preference of the pedestrians. The invention has the beneficial effects that: the mobile robot can autonomously generate a global path which accords with walking specifications of pedestrians in a scene according to the preference of walking directions of the pedestrians in the scene, and reduces the influence on the orders of surrounding pedestrians and public transport while guiding the mobile robot to move.
Description
Technical Field
The invention relates to a global path planning method and a global path planning device for a mobile robot, in particular to a global path planning method and a global path planning device for a mobile robot, which follow pedestrian walking specifications in a human-computer coexistence environment.
Background
In the service robot autonomous intelligent technology, the robot autonomous navigation technology is a key point to be concerned. In recent years, with the development of urbanization in China, the number of cities and population are remarkably increased, and service robots have many important application scenes, such as express mail and takeout collection, building cleaning, file distribution, welcome reception and the like, which all require that the robots can be autonomously navigated in a man-machine coexistence environment.
In order to realize autonomous navigation of a mobile robot in a human-computer coexistence environment, global path planning is firstly needed, namely a collision-free global path from a starting point to a target point of the robot is planned. In a traditional global path planning algorithm of a mobile robot, only the obstacle avoidance in a static environment is performed, the influence of the behavior of the robot on surrounding pedestrians is not considered, and the traffic jam is caused, so that the method is not suitable for navigation of the mobile robot in a human-computer coexistence scene. When the mobile robot moves in a human-computer coexistence environment, the robot is required to be capable of following the walking specification of the pedestrian in the navigation process so as to avoid causing retrograde behavior, causing traffic jam and influencing normal walking of the pedestrian. The most common pedestrian walking disciplines, such as in many countries, pedestrians default to walking to the right.
Disclosure of Invention
The invention provides a global path planning method and a global path planning device for a mobile robot, and aims to at least solve one of the technical problems in the prior art.
The technical scheme of the invention comprises a global path planning method for a mobile robot, which comprises the following steps:
s100, responding to a path planning request, and acquiring pedestrian information in a target area, wherein the pedestrian information comprises a pedestrian position, a pedestrian speed and a pedestrian area radius;
s200, classifying the pedestrians through a static map according to the positions of the pedestrians to obtain a classification result, wherein the static map comprises a plurality of grids;
s300, acquiring the pedestrian information of the pedestrians in each grid, and performing clustering processing on the moving direction of the pedestrians to obtain a plurality of clustering results;
s400, determining mixed Von-Mises distribution of each grid according to the clustering result, and determining pedestrian movement direction preference of the grids according to the mixed Von-Mises distribution;
and S500, determining the optimal global moving path of the mobile robot according to the mixed Von-Mises distribution of each grid and the moving direction preference of the pedestrian.
Further, the step S100 includes: and acquiring the pedestrian information of the target area within a preset time in an image acquisition or near-field acquisition mode to obtain all the pedestrian information of the target area within the preset time.
Further, the step S200 includes: and matching the pedestrian position of each pedestrian with the two-dimensional grid coordinates of the static map, and distributing the pedestrian information to the corresponding grid.
Further, the step S300 includes: clustering directions of movement of the multiple pedestrians for each grid through a clustering metric, wherein the clustering metric includes at least one of an elbow, an interval statistic, a contour coefficient and a Canopy.
Further, the step S400 includes:
taking a plurality of the clustering results of each grid as
Determining the mixed Von-Mises distribution of the pedestrian moving direction in the grid, and obtaining the mixed Von-Mises distribution through a formula:
wherein p is θ Alpha, mu and kappa are respectively calculation parameters of mixed Von-Mises distribution, M is the number of the clustering results, and alpha m Is a weight parameter for each of the clustered results and
μ m and kappa m Is the statistical model parameter for each distribution;
obtaining each independent Von-Mises distribution in the mixed Von-Mises distribution, and obtaining the Von-Mises distribution through a formula:
wherein J 0 (kappa) is a Bessel correction function of order 0 of
Calculating maximum likelihood estimation to obtain statistical model parameters mu and kappa, and determining a weight parameter alpha when combining each independent Von-Mises distribution into the mixed Von-Mises distribution m The calculation method is as follows:
wherein, P m Is the data quantity in the clustering result, and P is the sum of the data quantity of the clustering result in all grids;
and generating a pedestrian walking preference direction map.
Further, the step S400 includes:
the number likelihood function of each individual Von-Mises distribution is
Then the statistical model parameters mu and k can be obtained by calculating the maximum likelihood estimation in the way of
Further, the step S500 includes:
taking the grid where the mobile robot is located as a starting point, and taking the cost of each grid movement as:
g′(s)＝g(s)+l(s,θ)
where g(s) is a moving cost of the mobile robot moving one grid, l (s, θ) is a moving cost variation caused by an influence of a moving direction preference of the pedestrian, and l (s, θ) is obtained by the following formula:
an optimal path is selected based on the pedestrian movement direction preference by a graph search algorithm.
Further, the step S500 includes: and according to the pedestrian movement direction preference in each grid, executing iterative calculation of all grids in the target area to obtain a minimum consumption global path according with the pedestrian movement preference direction.
The invention also relates to a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the above-mentioned method.
The technical scheme of the invention also relates to a computer device, which comprises: an image acquisition device and a computer readable storage medium.
The beneficial effects of the invention are as follows: the mobile robot can autonomously generate a global path according with walking specifications of pedestrians in a scene according to walking direction preference of the pedestrians in the scene, and reduces influence on surrounding pedestrians and public traffic order while guiding the mobile robot to move.
Drawings
Fig. 1 is a schematic diagram of a pedestrian walking preference map and a finally planned global path according to an embodiment of the present invention.
FIG. 2 is a pedestrian movement direction statistics and clustering chart for one of the grids according to an embodiment of the present invention.
Fig. 3 is a diagram for calculating the optimal cluster number of one grid according to the embodiment of the present invention.
Fig. 4a and 4b are schematic diagrams illustrating the hybrid Von-Mises distribution and the pedestrian movement direction preference of one grid according to the embodiment of the invention.
Fig. 5 is a schematic diagram of cost calculation of a conventional graph search algorithm.
FIG. 6 is a schematic diagram of cost calculation of a graph search algorithm according to an embodiment of the present invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention.
Referring to fig. 1-6, in some embodiments, the present invention discloses a mobile robot global path planning method following pedestrian walking norms in a human-machine coexistence environment, the method comprising the steps of:
s100, responding to the path planning request, and acquiring pedestrian information in the target area, wherein the pedestrian information comprises a pedestrian position, a pedestrian speed and a pedestrian occupation area radius.
S200, classifying the pedestrians through a static map according to the positions of the pedestrians to obtain a classification result, wherein the static map comprises a plurality of grids.
S300, acquiring pedestrian information of pedestrians in each grid, and performing clustering processing on the moving direction of the pedestrians to obtain a plurality of clustering results.
S400, determining the mixed Von-Mises distribution of each grid according to the clustering result, and determining the pedestrian moving direction preference of the grids according to the mixed Von-Mises distribution.
And S500, determining the optimal global moving path of the mobile robot according to the mixed Von-Mises distribution of each grid and the moving direction preference of the pedestrians.
For a further embodiment of step S100
Acquiring pedestrian information of a target area within preset time in an image acquisition or near-field acquisition mode, acquiring the pedestrian information through equipment such as a camera device or a sensor arranged on a robot, or acquiring the pedestrian information through the camera device or the sensor arranged on the target area, and determining the position, the speed and the radius of the occupied area of a pedestrian through space positioning after acquiring new pedestrian information so as to further acquire all the pedestrian information of the target area within the preset time;
referring to fig. 1, in order to count the pedestrian flow direction preference in a region, it is first necessary to collect the movement information of the pedestrian in the region. And collecting pedestrian information in the region for a period of time by a monitoring camera or a mobile robot, and obtaining each piece of pedestrian information in the whole region for a period of time by a pedestrian detection module. The pedestrian information includes the location, speed and radius of the footprint, i.e., the pedestrian information is represented as (p) x ,p y ,p v ,p θ ,p b ) T . And distributing the data into corresponding grids according to the pedestrian position information. In the figure, the grid size is 1 square meter. For example, one pedestrian message is (2.2,3.3,1.0,0.3,0.4) T It is assigned in the grid with coordinates (asterisk pattern) in the lower left corner (2.0,3.0). In fig. 1, "EA" and "EAH" indicate possible moving directions of the pedestrian, respectively.
For a further embodiment of step S200
Wherein, step S200 includes: and matching the pedestrian position of each pedestrian with the two-dimensional grid coordinates of the static map, and distributing the pedestrian information to the corresponding grid.
Referring to the embodiment of fig. 1, the spatial position of the pedestrian is mapped to a two-dimensional occupation grid map of a static environment, the position of the pedestrian on the grid map is determined, and the position distribution of the pedestrian is completed.
For a further embodiment of step S300
Wherein, step S300 includes: clustering the moving directions of the pedestrians for each grid through a clustering measure, wherein the clustering measure comprises at least one of an elbow, an interval statistic, a contour coefficient and a Canopy.
In some embodiments, based on the pedestrian information in each grid, a preference for the direction of movement of the pedestrian in that grid needs to be determined. Because pedestrians can have multiple walking directions at a certain position, such as at an intersection, the pedestrians can walk around the past four intersections, and therefore the pedestrian information in each grid needs to be clustered by using a K-means clustering method. However, the number of clusters in the grid is not a fixed value, and therefore the number of clusters needs to be determined.
In particular, the moving directions of K pedestrians in the grid
The most suitable cluster is selected using a cluster metric. Common metrics include elbow, interval statistics, contour coefficients or Canopy, etc. Taking the circled grid of FIG. 1 as an example, each line in FIG. 2 represents a direction of pedestrian movement
The grid contains K moving directions of the pedestrians. As shown in fig. 3, the optimal cluster number is determined by taking the elbow metric as an example. The metric determines the number of clusters by minimizing the squared error of the sample from the center point, and finding a distortion threshold based on the error. In fig. 3, based on the elbow metric values for different numbers of clusters, it can be seen that the distortion critical point is at 2 for the number of clusters. Therefore, the pedestrian movement direction in fig. 2 is divided into two clusters, and two clusters C1 and C2 can be obtained by clustering the data based on the K-means clustering method.
For a further embodiment of step S400
For a plurality of clusters in each grid, obtaining mixed Von-Mises distribution in each grid, and generating a pedestrian walking preference direction map;
from the multiple clusters in the grid, a mixed Von-Mises distribution of pedestrian movement directions in the grid can be obtained. A mixed Von-Mises distribution can be expressed as
In the formula, p θ Alpha, mu and kappa are respectively calculation parameters of mixed Von-Mises distribution, M is the number of clusters, and alpha m Is a weight parameter for each cluster and
μ m and kappa m Are the statistical model parameters for each distribution. In the mixed Von-Mises distribution, each Von-Mises distribution is independent of the others and can be expressed as
In the formula J 0 (κ) is a 0 th order Bessel correction function, which may be expressed as
Therefore, based on the clustering of data in step S300, each cluster can be established as an independent Von-Mises distribution, and a plurality of clusters form a mixed Von-Mises distribution.
Specifically, for a Von-Mises distribution, the statistical model parameters μ and κ can be obtained by maximum likelihood estimation. First, the log-likelihood function of equation 1.2 is
Then the statistical model parameters μ and κ may be obtained by computing the maximum likelihood estimates
Then, when a plurality of independent Von-Mises distributions are combined into a mixed Von-Mises distribution, the weight parameter α of each independent distribution needs to be calculated m . Because ofEach distribution is independent of the other, so the weighting parameter can be obtained by the ratio of the number of data in the cluster to the number of all data in the grid
In the formula P m Is the number of data in the cluster and P is the sum of the number of data for all clusters. Fig. 4 is a schematic diagram of the mixed Von-Mises distribution obtained by processing the data in fig. 2, wherein fig. 4a is the mixed Von-Mises distribution, and 4b is the direction preference of pedestrian movement in each grid. The directional preference of the pedestrian movement within each grid can be derived.
For a further embodiment of step S500
The step S500 specifically includes: improving a mobile cost function in a graph search algorithm according to the mixed Von-Mises distribution in each grid;
the traditional graph search algorithm such as Astar, mixed Astar and the like plans a shortest collision-free path from a starting point to an end point, but the path does not consider the influence of robot behaviors on pedestrian traffic. The specific method for searching the shortest path by the traditional mixed Astar algorithm is
F(s)＝G(s)+H(s) (1.7)
Wherein F(s) is the overall estimated path cost for selecting a certain grid, G(s) is the cost from the starting point to the selected grid, and H(s) is the cost from the selected grid to the end point.
As shown in fig. 5, in the conventional graph search algorithm, a path from a circle to a diamond is planned, and only four directions of movement from top to bottom, left to right can be selected, so that G(s) is the cost from the circle to the triangle, H(s) is the cost from the triangle to the diamond, the cost is consumed as G(s) =1 when each lattice of movement is performed, and finally, a path with the minimum F(s) needs to be found, that is, the optimal global path.
As shown in FIG. 6, taking the preferred direction of the grid at the starting point as an example, the cost of moving each grid is rewritten from 1 to
g′(s)＝g(s)+l(s,θ) (1.8)
Where l (s, θ) is the change in movement cost due to the influence of the preferred direction of the pedestrian, and can be expressed as
Based on equation 1.9, the cost will be less when moving along the direction of the preference of the pedestrian in the grid. As shown in fig. 6, it is less costly to move along the pedestrian preference direction in the grid, so the graph search algorithm can be made to select the optimal path based on the pedestrian preference direction.
S6: and calculating a global path with the lowest consumption according to the moving cost function in the improved graph search algorithm.
And sequentially and iteratively calculating the pedestrian preference direction in each grid to finally obtain a global path according with the pedestrian movement preference direction. As shown in fig. 1, taking the hybrid Astar algorithm as an example, although the global path searched by the conventional hybrid Astar algorithm (HA) is shorter, it moves against the human stream at multiple places, which easily affects the traffic order and is not in line with the general moving habit of human beings. The improved hybrid Astar algorithm based on the stream preference map can plan a global path that moves along the stream. The robot can be guided to merge into the stream of people in the mode, and the normal walking of the pedestrians cannot be influenced.
The global path planning method of the mobile robot enables the mobile robot to autonomously generate the global path which accords with the walking specification of the pedestrians in the scene according to the preference of the walking direction of the pedestrians in the scene, and reduces the influence on the order of the surrounding pedestrians and public transport while guiding the mobile robot to move.
It should be recognized that the method steps in embodiments of the present invention may be embodied or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The method may use standard programming techniques. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable connection, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, or the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention may also include the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The technical solution and/or the embodiments thereof may be variously modified and varied within the scope of the present invention.
Claims (10)
1. A global path planning method for a mobile robot is characterized by comprising the following steps:
s100, responding to a path planning request, and acquiring pedestrian information in a target area, wherein the pedestrian information comprises a pedestrian position, a pedestrian speed and a pedestrian occupation area radius;
s200, classifying the pedestrians through a static map according to the positions of the pedestrians to obtain a classification result, wherein the static map comprises a plurality of grids;
s300, acquiring the pedestrian information of the pedestrians in each grid, and performing clustering processing on the moving direction of the pedestrians to obtain a plurality of clustering results;
s400, determining mixed Von-Mises distribution of each grid according to the clustering result, and determining pedestrian movement direction preference of the grids according to the mixed Von-Mises distribution;
and S500, determining the optimal global moving path of the mobile robot according to the mixed Von-Mises distribution of each grid and the moving direction preference of the pedestrian.
2. The method of claim 1, wherein the step S100 comprises:
and acquiring the pedestrian information of the target area within a preset time in an image acquisition or near-field acquisition mode to obtain all the pedestrian information of the target area within the preset time.
3. The method of claim 1, wherein the step S200 comprises:
and matching the pedestrian position of each pedestrian with the two-dimensional grid coordinates of the static map, and distributing the pedestrian information to the corresponding grid.
4. The method of claim 1, wherein the step S300 comprises:
clustering directions of movement of the multiple pedestrians for each grid through a clustering metric, wherein the clustering metric includes at least one of an elbow, an interval statistic, a contour coefficient and a Canopy.
5. A copy protection method as claimed in claim 1, wherein the step S400 comprises:
taking a plurality of the clustering results of each grid as
Determining the mixed Von-Mises distribution of the pedestrian moving direction in the grid, and obtaining the mixed Von-Mises distribution through a formula:
wherein p is θ Alpha, mu and kappa are respectively calculation parameters of the distribution of the mixed Von-Mises, M is the number of the clustering results, and alpha m Is a weight parameter for each of the clustered results and
μ m and kappa m Is each divided intoStatistical model parameters of the cloth;
obtaining each independent Von-Mises distribution in the mixed Von-Mises distribution, and obtaining the Von-Mises distribution through a formula:
wherein J 0 (kappa) is a Bessel correction function of order 0 of
Obtaining statistical model parameters mu and kappa by calculating maximum likelihood estimation, and determining a weight parameter alpha when combining each independent Von-Mises distribution into the mixed Von-Mises distribution m The calculation method is as follows:
wherein, P m Is the data quantity in the clustering result, and P is the sum of the data quantity of the clustering result in all grids;
and generating a pedestrian walking preference direction map.
6. The method of claim 1, wherein the step S400 comprises:
the number likelihood function of each individual Von-Mises distribution is
The statistical model parameters mu and kappa can be obtained by calculating the maximum likelihood estimation in the way of
7. The method of claim 5, wherein the step S500 comprises:
taking the grid where the mobile robot is located as a starting point, and taking the cost of moving each grid as:
g′(s)＝g(s)+l(s,θ)
where g(s) is a moving cost of the mobile robot moving one grid, l (s, θ) is a moving cost variation caused by an influence of a moving direction preference of the pedestrian, and l (s, θ) is obtained by the following formula:
an optimal path is selected based on the pedestrian movement direction preference by a graph search algorithm.
8. The method of claim 7, wherein the step S500 comprises:
and according to the pedestrian movement direction preference in each grid, executing iterative calculation of all grids in the target area to obtain a minimum consumption global path according with the pedestrian movement preference direction.
9. A computer readable storage medium having stored thereon program instructions which, when executed by a processor, implement the method of any one of claims 1 to 8.
10. A computer device, comprising:
an image acquisition device;
the computer-readable storage medium of claim 9.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210360087.8A CN115145261B (en) | 2022-04-07 | 2022-04-07 | Global path planning method of mobile robot conforming to pedestrian specification under coexistence of human and machine |
| PCT/CN2022/128602 WO2023193424A1 (en) | 2022-04-07 | 2022-10-31 | Global navigation method for mobile robot in man-machine coexistence environment following pedestrian norm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210360087.8A CN115145261B (en) | 2022-04-07 | 2022-04-07 | Global path planning method of mobile robot conforming to pedestrian specification under coexistence of human and machine |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115145261A true CN115145261A (en) | 2022-10-04 |
| CN115145261B CN115145261B (en) | 2024-04-26 |
Family
ID=83406378
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210360087.8A Active CN115145261B (en) | 2022-04-07 | 2022-04-07 | Global path planning method of mobile robot conforming to pedestrian specification under coexistence of human and machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115145261B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023193424A1 (en) * | 2022-04-07 | 2023-10-12 | 哈尔滨工业大学（深圳） | Global navigation method for mobile robot in man-machine coexistence environment following pedestrian norm |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101071506A (en) * | 2006-01-25 | 2007-11-14 | 美国西门子医疗解决公司 | System and method for local pulmonary structure classification for computer-aided nodule detection |
| KR20080029239A (en) * | 2006-09-28 | 2008-04-03 | 한국전자통신연구원 | Apparatus and method for building environment map according to optimum path setting in autonomous mobile robot |
| EP2261856A2 (en) * | 2009-06-08 | 2010-12-15 | Siemens Aktiengesellschaft | Method for modelling and calculation of the uncertainty of a pose of an object in space |
| US20130107071A1 (en) * | 2011-10-28 | 2013-05-02 | Hao Tang | Local image feature descriptors according to circular distribution information |
| CN103558856A (en) * | 2013-11-21 | 2014-02-05 | 东南大学 | Service mobile robot navigation method in dynamic environment |
| CN105527964A (en) * | 2015-12-28 | 2016-04-27 | 桂林电子科技大学 | Robot path planning method |
| EP3264212A1 (en) * | 2016-06-30 | 2018-01-03 | Advanced Digital Broadcast S.A. | System and method for determining an energy-efficient path of an autonomous device |
| CN108353248A (en) * | 2015-08-27 | 2018-07-31 | Fzc哥络普斯 | Method and apparatus for positioning mobile device |
| US20180260646A1 (en) * | 2017-03-13 | 2018-09-13 | Takayuki Hara | Image processing device, image processing method, and computer program product |
| WO2019043171A1 (en) * | 2017-09-01 | 2019-03-07 | RobArt GmbH | Movement planning for autonomous mobile robots |
| CN109542117A (en) * | 2018-10-19 | 2019-03-29 | 哈尔滨工业大学（威海） | Based on the submarine navigation device Rolling Planning algorithm for improving RRT |
| CN110039540A (en) * | 2019-05-27 | 2019-07-23 | 聊城大学 | A kind of service robot paths planning method that multiple target optimizes simultaneously |
| CN110097171A (en) * | 2019-06-19 | 2019-08-06 | 吉林大学 | A kind of activity trajectory localization method and system based on scorpion micro-vibration Location Mechanism |
| CN110285813A (en) * | 2019-07-01 | 2019-09-27 | 东南大学 | A kind of man-machine co-melting navigation device of indoor mobile robot and method |
| CN111461447A (en) * | 2020-04-09 | 2020-07-28 | 南方科技大学 | Path planning method and device, electronic equipment and storage medium |
| EP3706034A1 (en) * | 2019-03-06 | 2020-09-09 | Robert Bosch GmbH | Movement prediction of pedestrians useful for autonomous driving |
| WO2021228040A1 (en) * | 2020-05-09 | 2021-11-18 | 苏州宝时得电动工具有限公司 | Path planning method and self-moving device |
-
2022
- 2022-04-07 CN CN202210360087.8A patent/CN115145261B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101071506A (en) * | 2006-01-25 | 2007-11-14 | 美国西门子医疗解决公司 | System and method for local pulmonary structure classification for computer-aided nodule detection |
| KR20080029239A (en) * | 2006-09-28 | 2008-04-03 | 한국전자통신연구원 | Apparatus and method for building environment map according to optimum path setting in autonomous mobile robot |
| EP2261856A2 (en) * | 2009-06-08 | 2010-12-15 | Siemens Aktiengesellschaft | Method for modelling and calculation of the uncertainty of a pose of an object in space |
| US20130107071A1 (en) * | 2011-10-28 | 2013-05-02 | Hao Tang | Local image feature descriptors according to circular distribution information |
| CN103558856A (en) * | 2013-11-21 | 2014-02-05 | 东南大学 | Service mobile robot navigation method in dynamic environment |
| CN108353248A (en) * | 2015-08-27 | 2018-07-31 | Fzc哥络普斯 | Method and apparatus for positioning mobile device |
| CN105527964A (en) * | 2015-12-28 | 2016-04-27 | 桂林电子科技大学 | Robot path planning method |
| EP3264212A1 (en) * | 2016-06-30 | 2018-01-03 | Advanced Digital Broadcast S.A. | System and method for determining an energy-efficient path of an autonomous device |
| US20180260646A1 (en) * | 2017-03-13 | 2018-09-13 | Takayuki Hara | Image processing device, image processing method, and computer program product |
| WO2019043171A1 (en) * | 2017-09-01 | 2019-03-07 | RobArt GmbH | Movement planning for autonomous mobile robots |
| CN109542117A (en) * | 2018-10-19 | 2019-03-29 | 哈尔滨工业大学（威海） | Based on the submarine navigation device Rolling Planning algorithm for improving RRT |
| EP3706034A1 (en) * | 2019-03-06 | 2020-09-09 | Robert Bosch GmbH | Movement prediction of pedestrians useful for autonomous driving |
| CN110039540A (en) * | 2019-05-27 | 2019-07-23 | 聊城大学 | A kind of service robot paths planning method that multiple target optimizes simultaneously |
| CN110097171A (en) * | 2019-06-19 | 2019-08-06 | 吉林大学 | A kind of activity trajectory localization method and system based on scorpion micro-vibration Location Mechanism |
| CN110285813A (en) * | 2019-07-01 | 2019-09-27 | 东南大学 | A kind of man-machine co-melting navigation device of indoor mobile robot and method |
| CN111461447A (en) * | 2020-04-09 | 2020-07-28 | 南方科技大学 | Path planning method and device, electronic equipment and storage medium |
| WO2021228040A1 (en) * | 2020-05-09 | 2021-11-18 | 苏州宝时得电动工具有限公司 | Path planning method and self-moving device |
Non-Patent Citations (7)
| Title |
|---|
| 王帅发;郑金华;胡建杰;邹娟;喻果;: "自适应偏好半径划分区域的多目标进化方法", 软件学报, no. 10, 15 October 2017 (2017-10-15), pages 2704 - 2721 * |
| 王炜;陈卫东;王勇;: "基于概率栅格地图的移动机器人可定位性估计", 机器人, no. 04, 15 July 2012 (2012-07-15), pages 485 - 491 * |
| 王耀兵等: "空间机器人", 北京理工大学出版社, pages: 50 - 53 * |
| 钱堃, 马旭东, 戴先中等: "预测行人运动的服务机器人POMDP导航", 机器人, vol. 32, no. 1, 31 December 2010 (2010-12-31), pages 18 - 24 * |
| 陈建勇;熊雄;张丹;: "一种有限区域运动目标分布模型及计算", 海军航空工程学院学报, no. 03, 20 May 2010 (2010-05-20), pages 295 - 298 * |
| 陈雨景: "移动机器人运动规划稠密行人行人轨迹预测", 万方, 2 November 2022 (2022-11-02) * |
| 陶庭叶: ""测量中方向数据的研究"", 中国优秀博硕士学位论文全文数据库 (硕士)基础科学辑, no. 4, pages 107 - 16 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023193424A1 (en) * | 2022-04-07 | 2023-10-12 | 哈尔滨工业大学（深圳） | Global navigation method for mobile robot in man-machine coexistence environment following pedestrian norm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115145261B (en) | 2024-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107831765B (en) | Positioning method, device, equipment and storage medium | |
| US10939791B2 (en) | Mobile robot and mobile robot control method | |
| CN113168717B (en) | Point cloud matching method and device, navigation method and equipment, positioning method and laser radar | |
| WO2020223974A1 (en) | Method for updating map and mobile robot | |
| JP2019512668A (en) | Root deviation recognition method, terminal, and storage medium | |
| WO2023193424A1 (en) | Global navigation method for mobile robot in man-machine coexistence environment following pedestrian norm | |
| KR101965296B1 (en) | Method and Apparatus for Estimating Location of a Moving Object and Generating Maps Sharing a Map between Particles | |
| CN110986945B (en) | Local navigation method and system based on semantic altitude map | |
| JP2016149090A (en) | Autonomous mobile device, autonomous mobile system, autonomous mobile method and program | |
| CN114186112B (en) | Robot navigation method based on Bayesian optimization multiple information gain exploration strategy | |
| CN114494329B (en) | Guide point selection method for autonomous exploration of mobile robot in non-planar environment | |
| CN115145261B (en) | Global path planning method of mobile robot conforming to pedestrian specification under coexistence of human and machine | |
| WO2023082985A1 (en) | Method and product for generating navigation path for electronic device | |
| CN112084853A (en) | Footprint prediction method, footprint prediction device and humanoid robot | |
| CN114343490B (en) | Robot cleaning method, robot, and storage medium | |
| CN115249266A (en) | Method, system, device and storage medium for predicting position of waypoint | |
| Lian et al. | Improved coding landmark-based visual sensor position measurement and planning strategy for multiwarehouse automated guided vehicle | |
| KR101896405B1 (en) | Poi mapping method and system for analyzing moving line of user | |
| CN116203973B (en) | Intelligent control system of track AI inspection robot | |
| Belavadi et al. | Frontier exploration technique for 3d autonomous slam using k-means based divisive clustering | |
| CN113313765A (en) | Positioning method, positioning device, electronic equipment and storage medium | |
| CN112580438A (en) | Point cloud identification method in three-dimensional scene | |
| CN114740849B (en) | Mobile robot autonomous navigation method and device based on pedestrian walking decision rule | |
| CN112925321A (en) | Ship path planning method and device based on artificial potential field method and storage medium | |
| CN112904365A (en) | Map updating method and device |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |