CN112284393A - A global path planning method and system for an intelligent mobile robot - Google Patents
A global path planning method and system for an intelligent mobile robot Download PDFInfo
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Abstract
本发明公开了一种智能移动机器人全局路径规划方法和系统，方法包括：S1，确定作业全局信息，对所述作业全局的场景进行格栅化后获得全局栅格地图；S2，对所述全局栅格地图设置行驶规则以及设置特殊区域；S3，将所述全局栅格地图的栅格值离散化，定义移动机器人通过的区域与障碍物的间距大于所述移动机器人的外接圆半径；S4，采用A*算法对所述全局栅格地图定义通行值；S5，根据所述通行值以及所述移动机器人的起始点和目标点，输出所述栅格地图中一系列连续的路径点坐标值。通过预先构建的场景全局栅格地图，设置基于类似于机动车交通规则，对全局栅格地图定义通行值来改进A*全局路径搜索算法，生成输出连续的路径点坐标值实现自主导航。
The invention discloses a global path planning method and system for an intelligent mobile robot. The method includes: S1, determining global job information, and performing gridization on the global job scene to obtain a global grid map; S2, analyzing the global The grid map sets the driving rules and sets the special area; S3, discretizes the grid values of the global grid map, and defines that the distance between the area passed by the mobile robot and the obstacle is greater than the radius of the circumcircle of the mobile robot; S4, A* algorithm is used to define a pass value for the global grid map; S5, according to the pass value and the starting point and target point of the mobile robot, output a series of continuous path point coordinate values in the grid map. Through the pre-built global grid map of the scene, the setting is based on the traffic rules similar to the motor vehicle, and the global grid map is defined to define the passing value to improve the A* global path search algorithm, and generate and output continuous coordinate values of waypoints to realize autonomous navigation.
Description
Technical Field
The invention relates to the field of intelligent control of mobile robots, in particular to a global path planning method and system for an intelligent mobile robot.
Background
At present, the mobile robot serves all industries of social development, can replace manual task execution, is high in execution efficiency and low in error rate, can effectively reduce labor cost and operation cost, such as factories, hospitals, families, hotels, exhibition halls, restaurants and the like, and mainly executes operation tasks of logistics, transportation, distribution and the like.
The mobile robot needs to navigate autonomously in a working scene, and can be positioned accurately and travel safely. When the operation scene is complex, such as more dynamic objects, narrower driving area and the like, the motion of the mobile robot needs to follow the traffic rules similar to those of motor vehicles, such as a forbidden zone, a right-going zone, a resistant zone, a forbidden line, a single-line and the like, so that the mobile robot can be ensured to pass in order in the complex operation scene, and the problem of traffic jam and even congestion possibly occurring between the mobile robot and the dynamic objects and between the multiple mobile robots is solved.
The mobile robot works in a working scene, generally, a global map of the surrounding working scene is constructed in advance, and safe traveling from a starting point to a target point is realized according to the requirement of a work task. However, the general global path planning algorithm of the mobile robot only searches the nearest or shortest short-cut path, but does not comply with the traffic rule of the travelable channel, which results in poor smoothness of traveling in scenes such as many dynamic objects, narrow travelable area, and the like.
In order to ensure the passing efficiency and safety of the mobile robot, certain traffic rule setting needs to be performed on a scene map pre-constructed by the mobile robot, and traffic rules similar to those of human motor vehicles identify which areas can walk, which areas cannot walk, and which areas can only walk in one direction. The mobile robot is based on these traffic rule constraints, and therefore, how to realize safe and efficient operation of the robot is the work focus of those skilled in the art.
Disclosure of Invention
The invention provides a global path planning method and a global path planning system for an intelligent mobile robot, which are used for realizing autonomous navigation of the mobile robot during operation in a complex dynamic scene, have good safety and order and higher real-time performance and solve the problem of traffic jam in a narrow operation area of multiple mobile robots in different complex dynamic scenes.
In order to solve the technical problem, the invention provides a global path planning method for an intelligent mobile robot, which comprises the following steps:
s1, determining the overall information of the operation, and obtaining an overall grid map after the overall scene of the operation is subjected to grid formation;
s2, setting a driving rule and a special area for the global grid map;
s3, discretizing the grid values of the global grid map, and defining that the distance between the area where the mobile robot passes and the obstacle is larger than the radius of the circumscribed circle of the mobile robot;
s4, defining a passing value for the global grid map by adopting an A-star algorithm;
and S5, outputting a series of continuous path point coordinate values in the grid map according to the passing value and the starting point and the target point of the mobile robot.
Wherein the special region includes at least one of a forbidden region, a right row region, a rejection region, a forbidden line, and a single row line.
Wherein the S2 includes:
and acquiring a forbidden region, a right-row region, a rejection region and forbidden row lines in a first preset region by setting the grid values of the global grid map, and setting single-row lines in a second preset region by limiting the grid diffusion of the global grid map.
The grid values of the global grid map range from 0 to 255 or from 0 to 1023.
Wherein, after the S5, the method further comprises:
and sensing surrounding environment information in real time through a laser sensor and a Doppler radar, and carrying out global positioning on the mobile robot in the grid map.
Wherein, after the S5, the method further comprises:
receiving a grid value adjusting instruction, and adjusting the grid value of the global grid map.
Wherein, after the S5, the method further comprises:
and S6, detecting the traffic value of the grid where the mobile robot is currently located, and setting the traveling speed of the mobile robot.
In addition, the embodiment of the invention also discloses an intelligent mobile robot global path planning system, which comprises:
the grid map conversion module is used for outputting a grid map after grid processing is carried out according to the input operation global scene;
the driving rule and special area setting module is connected with the grid map conversion module and is used for setting driving rules and setting special areas for the grid map;
the map discretization module is connected with the grid map conversion module and the driving rule and special area setting module and is used for discretizing the grid values of the global grid map and defining that the distance between an area where the mobile robot passes and an obstacle is larger than the radius of a circumscribed circle of the mobile robot;
and the A-algorithm path planning module is connected with the map discretization module, receives the starting point and the target point of the mobile robot, defines a pass value for the global grid map by adopting an A-algorithm, and outputs a series of continuous path point coordinate values from the starting point to the target point in the grid map to the mobile robot.
The map discretization module is connected with the map discretization module and used for receiving a grid value adjusting instruction and adjusting the grid values of the global grid map.
The system further comprises a traveling speed limiting module connected with the A-algorithm path planning module and used for setting the traveling speed of the mobile robot according to the passing value of the current grid passed by the mobile robot.
Compared with the prior art, the intelligent mobile robot global path planning method and the intelligent mobile robot global path planning system provided by the embodiment of the invention have the following beneficial effects:
according to the method and the system for planning the global path of the intelligent mobile robot, a traffic rule similar to a motor vehicle traffic rule is set through a pre-constructed scene global grid map, a global path search algorithm is improved by defining a traffic value on the global grid map, and continuous path point coordinate values are generated and output to realize navigation, so that the mobile robot can realize autonomous navigation in complex dynamic scene operation, the safety and the orderliness are good, the real-time performance is high, and the problem of traffic jam in a narrow operation area of multiple mobile robots in different complex dynamic scenes is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart illustrating steps of a global path planning method for an intelligent mobile robot according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart illustrating steps of a global path planning method for an intelligent mobile robot according to another embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an embodiment of an intelligent mobile robot global path planning system provided in the present application;
fig. 4 is a schematic structural diagram of another embodiment of the global path planning system for an intelligent mobile robot provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-4, fig. 1 is a schematic flowchart illustrating steps of an embodiment of a global path planning method for an intelligent mobile robot provided in the present application; fig. 2 is a schematic flowchart illustrating steps of a global path planning method for an intelligent mobile robot according to another embodiment of the present disclosure; fig. 3 is a schematic structural diagram of an embodiment of an intelligent mobile robot global path planning system provided in the present application; fig. 4 is a schematic structural diagram of another embodiment of the global path planning system for an intelligent mobile robot provided in the present application.
In a specific embodiment, the present invention provides a global path planning method for an intelligent mobile robot, including:
s1, determining the overall information of the operation, and obtaining an overall grid map after the overall scene of the operation is subjected to grid formation; namely, the whole working environment of the mobile robot is determined, the allowable activity range of the mobile robot is set as the operation global, and then a global grid map is obtained after the grid processing is carried out;
s2, setting a driving rule and a special area for the global grid map; the purpose of setting the form rule and the special area is to enable the mobile robot to follow a certain rule in the moving process, which may be the current traffic rule or other self-defined traffic rules, and the invention is not limited to this.
S3, discretizing the grid values of the global grid map, and defining that the distance between the area where the mobile robot passes and the obstacle is larger than the radius of the circumscribed circle of the mobile robot; the grid value discretization is adopted, the distance between the area where the mobile robot passes and the obstacle is defined to be larger than the radius of the circumscribed circle of the mobile robot, so that the grid value discretization method is greatly different from a conventional global grid map, and the size of the mobile robot is considered.
For example, a conventional global grid map method is to discretize the real environment into a square grid with a resolution, e.g., 20cm width, where 1 indicates obstacles, 0 indicates no obstacles, and-1 indicates unknown. However, such a simplified representation method often does not take the safety size information of the mobile robot into account, and a path close to an obstacle is planned, and a certain set traffic regulation constraint condition is taken into account, so that a situation that the mobile robot collides with an obstacle area to cause a logic error easily occurs in an actual operation.
S4, defining a passing value for the global grid map by adopting an A-star algorithm; the A-algorithm is a heuristic algorithm and consists of a cost function and a heuristic function. The cost function represents the cost from the starting grid to the target grid, and is usually calculated by the cost value of the walking grid, and the heuristic function represents the estimated required cost value from the starting point to the target point, and is usually calculated by the Manhattan distance from the current grid to the target grid.
And S5, outputting a series of continuous path point coordinate values in the grid map according to the passing value and the starting point and the target point of the mobile robot.
The method has the advantages that the A-global path search algorithm is improved by setting a scene global grid map which is constructed in advance and defining a traffic value on the basis of motor vehicle traffic rules, and the automatic navigation is realized by generating and outputting continuous path point coordinate values, so that the automatic navigation of the mobile robot is realized during the operation of a complex dynamic scene, the safety and orderliness are good, the real-time performance is high, and the problem of traffic jam in a narrow operation area of the mobile robot in different complex dynamic scenes is solved.
The invention does not limit the setting mode, type and number of the special area, and the special area can be only the special area caused by actual terrain, or can be the special area formed by communication signals such as electromagnetic interference or other reasons, and the special area comprises at least one of a forbidden area, a right-row area, a rejection area, a forbidden line and a single-row line.
It should be noted that even in a special area where actual terrain is formed, the path planning of the mobile robot according to the present invention may be appropriately adjusted, that is, may be completely the same, or may be adjusted to a certain degree.
The present invention is not limited to the setting manner for realizing the special area, and in an embodiment, the S2 includes:
and acquiring a forbidden region, a right-row region, a rejection region and forbidden row lines in a first preset region by setting the grid values of the global grid map, and setting single-row lines in a second preset region by limiting the grid diffusion of the global grid map.
It should be noted that the present invention is not limited to the above-mentioned special area setting manner, and may also be set in a model manner, that is, the special areas are classified, the areas are selected by using a keyboard or a mouse, and then types are selected to implement the setting of the special areas, and the specific areas may also be assigned, so that in the identification process, the specific assigned areas may be defined as the required types.
The grid values of the global grid map are not limited in scope, and can be set according to the actual path planning precision and the data processing capacity, and the grid values of the global grid map generally range from 0 to 255 or 0 to 1023.
In the present invention, after the path planning of the mobile robot is completed, the position of the mobile robot is continuously changed in the moving process of the mobile robot, so that the mobile robot needs to be continuously positioned to move according to a predetermined path, and the positioning manner of the mobile robot is not limited in the present invention, in an embodiment, after the step S5, the method further includes:
and sensing surrounding environment information in real time through a laser sensor and a Doppler radar, and carrying out global positioning on the mobile robot in the grid map.
The method for sensing the surrounding environment information is not limited to the above method, and other methods may be used, such as triggering the mobile robot after the mobile robot reaches a specified position by using a trigger to sense the position, or even combining with a satellite positioning system to realize positioning.
Since the accuracy of the grid map to be adopted is different for different scenes, in order to achieve flexible use and reduce use cost, in an embodiment, after the S5, the method further includes:
receiving a grid value adjusting instruction, and adjusting the grid value of the global grid map.
The method for adjusting the grid values of the global grid map is not limited, and the grid values can be directly input by a keyboard and the like, can be realized by mode selection, or can be in other modes, such as increasing a first gear or reducing the first gear.
Since only one mobile robot can freely travel during the traveling process of the mobile robot, but if there are a plurality of mobile robots, traffic congestion may occur, and in most cases, the number of mobile robots is large, and therefore, it is necessary to set the speed of the mobile robot to improve traffic safety, in one embodiment, after the step S5, the method further includes:
and S6, detecting the traffic value of the grid where the mobile robot is currently located, and setting the traveling speed of the mobile robot.
The method for controlling the traveling speed of the mobile robot is not limited, and the method can be used for real-time correspondence with the traffic value and can also be used for speed limitation on each road section similar to a train.
In one embodiment, the conventional raster map raster cost value is subjected to traversal modification, the global path planning of the mobile robot is to find a safe collision-free continuous path point from a starting point to a target point on a constructed global raster map by combining with laser real-time sensing surrounding environment information, and the path is usually the shortest or the execution time is the least.
The present invention expands the range of each grid value in the conventional global grid map from 0, 1, -1 to 0 to 255, where 0 represents completely free, 254 represents completely occupied, and 255 represents unknown. Compared with the original grid occupation method, the value of the method is increased by 1 to 253, and the value of the method is higher as the method is closer to the obstacle. Since the mobile robot has a certain size, the distance from its center to the obstacle cannot be smaller than the inscribed circle radius of the mobile robot, and therefore 253 is defined as the value of the grid within the inscribed circle radius of the mobile robot from the obstacle, the mobile robot can pass through the grids having values of 1 to 252, cannot pass through the grids having values of 253 or 254, and can see the configuration parameters through the grid having value of 255. The grid value magnitude outside the radius of the circumscribing circle from the obstacle can be calculated as follows:
fcost＝e-w(d-r)·252(d>r)
wherein d is the distance to the nearest barrier, r is the radius of the circumscribed circle of the mobile robot, and w is the adjustment coefficient.
For the setting of forbidden zones, forbidden lines and rejection zones: setting a cost value to 253 or 254 in a designated area of the grid map, wherein a forbidden zone is set to 253, a forbidden line is set to 254, the value of the rejection zone can be designated to be any one of 1 to 252, the rejection zone can be crossed when the setting is small, and a path with lower cost can be selected when the setting is large; for the right row area setting: the right-going area is used in some specific situations, and is especially important when dealing with multiple robots. The method assumes that the mobile robot can see the center line of the area close to the right row, regard the center line as a virtual 'barrier', and then always expand the barrier leftwards, so that the grid on the left side of the 'barrier' always has a large value (but can also pass), and the mobile robot plans a path always on the right side of the 'barrier'; for the one-line zone setting: unlike the above-described area, this is achieved by restricting mobile robot planning.
Improving A global path planning algorithm: each iteration of the algorithm is expanded from the grid with the minimum current objective function value (4 communication or 8 communication), the grid with the cost value of 253 or 254 cannot be expanded until the grid is expanded to the target point, and then the algorithm is traced back to the starting point from the target point according to the direction of the fastest potential energy reduction, so that one path planning is finished. Each point of the grid map is set with a cost value, the invention defines a new traffic value P, which has 5 values in a 4-connected system, namely NONE, LEFT, RIGHT, UP and DOWN respectively representing no limit, limit LEFT expansion, limit RIGHT expansion, limit UP expansion and limit DOWN expansion.
After the improved A global path planning algorithm, a series of continuous path point coordinate values from the starting point S to the target point E in the grid map are output, wherein the middle coordinate value only has coordinate [ x y ] information, and the final target point E has a coordinate [ x y ] and an angle theta value. The mobile robot can realize autonomous navigation from the starting point S to the target point E by continuously and accurately passing through the intermediate path points. Of course, for the dynamic obstacles which do not exist in the map during the driving process, the mobile robot can automatically stop or automatically avoid the obstacles for autonomous navigation, which may involve a local path planning algorithm of the mobile robot.
In addition, the embodiment of the invention also discloses an intelligent mobile robot global path planning system, which comprises:
the grid map conversion module 10 is configured to output a grid map after grid processing is performed according to an input operation global scene;
a driving rule and special area setting module 20 connected to the grid map conversion module 10, for setting driving rules and special areas for the grid map;
the map discretization module 30 is connected with the grid map conversion module 10 and the driving rule and special area setting module 20, and is used for discretizing the grid values of the global grid map and defining that the distance between the area where the mobile robot passes and the obstacle is larger than the radius of the circumscribed circle of the mobile robot;
and the A-algorithm path planning module 40 is connected with the map discretization module 30, receives the starting point and the target point of the mobile robot, defines a pass value for the global grid map by adopting an A-algorithm, and outputs a series of continuous path point coordinate values from the starting point to the target point in the grid map to the mobile robot.
Since the above-mentioned global path planning system of the intelligent mobile robot is a system corresponding to the above-mentioned global path planning method of the intelligent mobile robot, and has the same beneficial effects, the present invention will not be described in detail.
In the present invention, because grid values need to be adjusted in different scenarios, in an embodiment, the system for planning a global path of an intelligent mobile robot further includes a grid value setting module 50 connected to the map discretization module 30, and configured to receive a grid value adjusting instruction and adjust the grid values of the global grid map.
The method for adjusting the grid values of the global grid map is not limited, and the grid values can be directly input by a keyboard and the like, can be realized by mode selection, or can be in other modes, such as increasing a first gear or reducing the first gear.
In one embodiment, the method and system for global path planning of an intelligent mobile robot further comprise a traveling speed limiting module 30 connected to the a-algorithm path planning module 40, and configured to set a traveling speed of the mobile robot according to a traffic value of a current grid through which the mobile robot passes.
In summary, according to the method and system for planning the global path of the intelligent mobile robot provided by the embodiments of the present invention, a scene global grid map is pre-constructed, traffic rules similar to motor vehicle traffic rules are set, a global path search algorithm is improved by defining a traffic value for the global grid map, and continuous path point coordinate values are generated and output to implement autonomous navigation, so that the mobile robot can implement autonomous navigation in complex dynamic scene operation, the safety and the order are good, the real-time performance is high, and the problem of traffic congestion in a narrow operation area of multiple mobile robots applied to different complex dynamic scenes is solved.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109059924A (en) * | 2018-07-25 | 2018-12-21 | 齐鲁工业大学 | Adjoint robot Incremental Route method and system for planning based on A* algorithm |
| CN109443364A (en) * | 2018-11-13 | 2019-03-08 | 国网浙江宁波市鄞州区供电有限公司 | Paths planning method based on A* algorithm |
| WO2020041817A1 (en) * | 2018-08-31 | 2020-03-05 | RobArt GmbH | Exploration of a robot deployment area by an autonomous mobile robot |
| CN111272186A (en) * | 2020-03-04 | 2020-06-12 | 广东博智林机器人有限公司 | Robot path configuration method and device and readable storage medium |
| CN111538343A (en) * | 2020-06-22 | 2020-08-14 | 天津联汇智造科技有限公司 | A system, method and storage medium for robot to set traffic rules |
| CN111543908A (en) * | 2020-05-15 | 2020-08-18 | 弗徕威智能机器人科技（上海）有限公司 | Method and device for planning travelling path and intelligent equipment travelling path |
| WO2020197126A1 (en) * | 2019-03-25 | 2020-10-01 | 주식회사 트위니 | Method for estimating positioning of moving object by using big cell grid map, recording medium in which program for implementing same is stored, and computer program stored in medium in order to implement same |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112284393B (en) * | 2020-10-23 | 2022-12-23 | 苏州大学 | Global path planning method and system for intelligent mobile robot |
-
2020
- 2020-10-23 CN CN202011147139.0A patent/CN112284393B/en active Active
-
2021
- 2021-08-31 WO PCT/CN2021/115501 patent/WO2022083292A1/en active Application Filing
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109059924A (en) * | 2018-07-25 | 2018-12-21 | 齐鲁工业大学 | Adjoint robot Incremental Route method and system for planning based on A* algorithm |
| WO2020041817A1 (en) * | 2018-08-31 | 2020-03-05 | RobArt GmbH | Exploration of a robot deployment area by an autonomous mobile robot |
| CN109443364A (en) * | 2018-11-13 | 2019-03-08 | 国网浙江宁波市鄞州区供电有限公司 | Paths planning method based on A* algorithm |
| WO2020197126A1 (en) * | 2019-03-25 | 2020-10-01 | 주식회사 트위니 | Method for estimating positioning of moving object by using big cell grid map, recording medium in which program for implementing same is stored, and computer program stored in medium in order to implement same |
| CN111272186A (en) * | 2020-03-04 | 2020-06-12 | 广东博智林机器人有限公司 | Robot path configuration method and device and readable storage medium |
| CN111543908A (en) * | 2020-05-15 | 2020-08-18 | 弗徕威智能机器人科技（上海）有限公司 | Method and device for planning travelling path and intelligent equipment travelling path |
| CN111538343A (en) * | 2020-06-22 | 2020-08-14 | 天津联汇智造科技有限公司 | A system, method and storage medium for robot to set traffic rules |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022083292A1 (en) * | 2020-10-23 | 2022-04-28 | 苏州大学 | Smart mobile robot global path planning method and system |
| CN113284240A (en) * | 2021-06-18 | 2021-08-20 | 深圳市商汤科技有限公司 | Map construction method and device, electronic equipment and storage medium |
| CN113485378A (en) * | 2021-08-18 | 2021-10-08 | 昆山市工研院智能制造技术有限公司 | Mobile robot path planning method, system and storage medium based on traffic rules |
| CN113485378B (en) * | 2021-08-18 | 2024-01-16 | 昆山市工研院智能制造技术有限公司 | Mobile robot path planning method, system and storage medium based on traffic rules |
| CN114326717A (en) * | 2021-12-13 | 2022-04-12 | 中国科学院计算技术研究所 | An integrated path planning method and system for intelligent agricultural machinery transition and operation |
| CN114518760A (en) * | 2022-02-16 | 2022-05-20 | 北京航空航天大学 | Unmanned vehicle navigation method and system server based on risk area management |
| CN114518760B (en) * | 2022-02-16 | 2024-06-25 | 北京航空航天大学 | Unmanned vehicle navigation method and system server based on risk zone management |
| CN115326057A (en) * | 2022-08-31 | 2022-11-11 | 深圳鹏行智能研究有限公司 | Path planning method and device, robot and readable storage medium |
| CN116625386A (en) * | 2023-04-21 | 2023-08-22 | 苏州大学 | Map construction method and multi-robot path planning method based on image filtering |
| CN116625386B (en) * | 2023-04-21 | 2024-09-20 | 苏州大学 | Map construction method and multi-robot path planning method based on image filtering |
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| CN112284393B (en) | 2022-12-23 |
| WO2022083292A1 (en) | 2022-04-28 |
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