CN114740844A - Path planning method and device, computer readable storage medium and electronic equipment - Google Patents
Path planning method and device, computer readable storage medium and electronic equipment Download PDFInfo
- Publication number
- CN114740844A CN114740844A CN202210333261.XA CN202210333261A CN114740844A CN 114740844 A CN114740844 A CN 114740844A CN 202210333261 A CN202210333261 A CN 202210333261A CN 114740844 A CN114740844 A CN 114740844A
- Authority
- CN
- China
- Prior art keywords
- path
- temporary
- obstacle
- robot
- static map
- 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
- 238000013439 planning Methods 0.000 title claims abstract description 87
- 238000000034 method Methods 0.000 title claims abstract description 69
- 230000003068 static effect Effects 0.000 claims abstract description 125
- 230000004888 barrier function Effects 0.000 claims description 46
- 238000004590 computer program Methods 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 13
- 238000004891 communication Methods 0.000 description 15
- 238000010586 diagram Methods 0.000 description 14
- 230000008859 change Effects 0.000 description 11
- 230000008569 process Effects 0.000 description 9
- 230000007774 longterm Effects 0.000 description 5
- 235000012054 meals Nutrition 0.000 description 5
- 238000012384 transportation and delivery Methods 0.000 description 5
- 230000007613 environmental effect Effects 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 230000004083 survival effect Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000002238 attenuated effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000008034 disappearance Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 102100029469 WD repeat and HMG-box DNA-binding protein 1 Human genes 0.000 description 1
- 101710097421 WD repeat and HMG-box DNA-binding protein 1 Proteins 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000007429 general method Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000000926 separation method 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 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
- G05D1/0231—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
Abstract
The present disclosure relates to a path planning method, apparatus, computer-readable storage medium and electronic device, including: acquiring a static map constructed according to the environment where the robot is located, acquiring information of temporary obstacles on a temporary obstacle path where obstacle avoidance fails when at least one robot executes a navigation task according to the static map, wherein the temporary obstacle path is a path from a starting position to a target position, replanning a walking path of at least one robot according to the information of the temporary obstacles and the static map, and planning walking paths of other robots according to the information of the temporary obstacles and the static map; when the obstacle avoidance fails when any one robot executes a navigation task according to the static map, the information of the temporary obstacles can be acquired, and then the walking paths of all the robots are re-planned according to the information of the temporary obstacles and the static map, so that the multi-robot cooperation working efficiency is obviously improved.
Description
Technical Field
The present disclosure relates to the field of robots, and in particular, to a path planning method and apparatus, a computer-readable storage medium, and an electronic device.
Background
And constructing a static map according to the environment of the robot, wherein path planning in the static map is the most critical link for the mobile robot to work. For most service robots performing the business of delivering goods, delivering food, welcoming guests, etc., users often have high requirements on the efficiency of moving and performing tasks of the service robots. When a user expects the robot to execute a task, the user can search the optimal or approximately optimal path from the starting position to the target position to walk by himself, and the robot can intelligently adapt to the dynamic change of the environment. Especially, in some scenes with narrow paths, some sudden obstacles often appear to cause some channels to be blocked, and in such a case, an optimal path can still be obtained in response to the continuous change of the scene, or the walking path is updated in time, which is a difficult problem to be solved by the current robot system.
Disclosure of Invention
The purpose of the disclosure is to provide a path planning method, which enables a robot to obtain an optimal path even in response to a continuous change of a scene, or to update a walking path in time.
In order to achieve the above object, in a first aspect, the present disclosure provides a path planning method, including: obtaining a static map; the static map is constructed according to the environment where the robot is located, and comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot; acquiring information of temporary obstacles on a temporary obstacle path with obstacle avoidance failure under the condition that the at least one robot has the obstacle avoidance failure when executing a navigation task according to the static map; the information of the temporary obstacle comprises a life cycle, the life cycle represents the existence duration of the temporary obstacle, and the temporary obstacle path is a path from a starting position to a target position; and replanning the walking path of the at least one robot according to the information of the temporary obstacles and the static map, and planning the walking paths of other robots according to the information of the temporary obstacles and the static map.
Optionally, the step of planning the walking path of the other robot according to the information of the temporary obstacle includes: constructing a temporary obstacle map according to the information of the temporary obstacle; obtaining a global map according to the static map and the temporary obstacle map in an overlapping mode; and planning the walking paths of the other robots according to the global map in the life cycle.
Optionally, the path is composed of a plurality of road segments, and the step of planning the walking path of the other robot according to the information of the temporary obstacle includes: calculating the dynamic path weight of the road section where the temporary barrier is located; and planning the walking paths of the other robots according to the dynamic path weight and the static map.
Optionally, the step of calculating the dynamic path weight of the road segment where the temporary obstacle is located includes: acquiring an initial path weight of a road section where the temporary barrier is located; and obtaining dynamic path weight according to the initial path weight, the life cycle and the existing duration of the temporary barrier.
Optionally, the formula for calculating the dynamic path weight includes:
wherein Weight is the dynamic path Weight, Weight0For the initial path weight, Lim _ Period is the life cycle, and T _ elapse is the duration for which the temporary obstacle currently exists.
Optionally, the static map comprises a starting position and a target position of the robot; the step of planning the walking paths of the other robots according to the dynamic path weights and the static map comprises the following steps: acquiring all feasible paths from the starting position to the target position; acquiring a weight value of each road section of all the feasible paths; the weight value of each road section comprises the value of the current dynamic path weight; calculating the path weight of all the feasible paths according to the weight value of each road section; and planning the walking paths of the other robots according to the path weights of all the feasible paths.
Optionally, the step of planning the walking paths of the other robots according to the dynamic path weights and the static map comprises: controlling the other robots to move according to the walking paths; if at least one of the other robots detects that the temporary obstacle does not disappear, recording the times of the obstacle avoidance failures continuously occurring on the temporary obstacle path; and if at least one of the other robots detects that the temporary obstacle disappears, replanning the walking paths of all the robots according to the static map, and clearing the times of the obstacle avoidance failures continuously occurring on the temporary obstacle path.
Optionally, when the number of times of the obstacle avoidance failures occurring continuously on the temporary obstacle path is greater than a predetermined threshold, adding the temporary obstacle to the static map, or sending a notification requesting for manually assisting to clear the obstacle.
Optionally, the information of the temporary obstacle includes a position of a road segment where the temporary obstacle is located; and sending a notice of requesting manual assistance to clear the obstacle when the road section where the temporary obstacle is located is the only path from the starting position to the target position.
In a second aspect, the present disclosure provides a path planning apparatus, including: the acquisition module is used for acquiring a static map; the static map is constructed according to the environment where the robot is located, and comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot; the acquisition module is further used for acquiring information of temporary obstacles on a temporary obstacle path where obstacle avoidance failure occurs when at least one robot executes a navigation task according to the static map and the obstacle avoidance failure occurs; the information of the temporary obstacle comprises a life cycle, the life cycle represents the existence duration of the temporary obstacle, and the temporary obstacle path is a path from a starting position to a target position; and the processing module is used for replanning the walking path of the at least one robot according to the information of the temporary obstacles and the static map, and replanning the walking paths of other robots according to the information of the temporary obstacles and the static map.
According to a third aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the aforementioned path planning method.
According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: a memory having a computer program stored thereon; a processor for executing the computer program in the memory to implement the steps of the aforementioned path planning method.
According to the technical scheme, a static map constructed according to the environment where the robot is located is obtained, the static map comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot, under the condition that obstacle avoidance failure occurs when at least one robot executes a navigation task according to the static map, information of the temporary obstacle on the temporary obstacle path where the obstacle avoidance failure occurs is obtained, the information of the temporary obstacle comprises a survival cycle, the survival cycle represents the existence duration of the temporary obstacle, the temporary obstacle path is the path from a starting point position to a target position, the walking path of at least one robot is re-planned according to the information of the temporary obstacle and the static map, and the walking paths of other robots are planned according to the information of the temporary obstacle and the static map; when the obstacle avoidance failure occurs when any one robot executes a navigation task according to the static map, the information of the temporary obstacles can be acquired, then the walking paths of all the robots are re-planned according to the information of the temporary obstacles and the static map, and the data of environment change is shared among multiple robots, so that the optimal path can be still acquired when the robots deal with the continuous change of scenes, the walking paths are updated in time, and the multi-robot cooperation working efficiency is obviously improved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
fig. 1 is a schematic view of an application scenario of a robot according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating a path planning method according to an exemplary embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating another path planning method according to an exemplary embodiment of the present disclosure.
Fig. 4 is a schematic diagram of a two-dimensional planar grid map, shown in an exemplary embodiment of the present disclosure.
Fig. 5 is a schematic diagram of a road network planning table according to an exemplary embodiment of the present disclosure.
Fig. 6 is a schematic diagram of a static map shown in an exemplary embodiment of the present disclosure.
Fig. 7 is a schematic diagram of a temporary obstacle map shown in an exemplary embodiment of the present disclosure.
Fig. 8 is a flowchart illustrating a method of planning a walking path according to an exemplary embodiment of the present disclosure.
Fig. 9 is a schematic diagram of one possible path shown in an exemplary embodiment of the present disclosure.
Fig. 10 is a block diagram of a path planning apparatus according to an exemplary embodiment of the present disclosure.
Fig. 11 is a block diagram of an electronic device shown in an exemplary embodiment of the present disclosure.
Fig. 12 is a block diagram of another electronic device shown in an exemplary embodiment of the present disclosure.
Description of the reference numerals
120-a robot; 140-a server; 20-a path planning device; 201-an acquisition module; 203-a processing module; 700-an electronic device; 701-a processor; 702-a memory; 703-multimedia components; 704-I/O interface; 705-a communication component; 1900-an electronic device; 1922-a processor; 1932-memory; 1926-power supply components; 1950-a communication component; 1958-I/O interface.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
It should be noted that all actions of acquiring signals, information or data in the present disclosure are performed under the premise of complying with the corresponding data protection regulation policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
Fig. 1 is a schematic diagram illustrating an application scenario of a robot according to an exemplary embodiment of the present disclosure, where the application scenario of the robot includes a plurality of robots 120 and a server 140.
The robot 120 and the server 140 are connected to each other through a wired or wireless network.
The robot 120 includes a display that can be used to display information such as the robot starting position, target position, and executing tasks.
The robot 120 includes a first memory and a first processor. The first memory stores a static map constructed according to the environment where the robot is located. The first memory may include, but is not limited to, the following: random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Read-Only Memory (EPROM), and electrically Erasable Read-Only Memory (EEPROM).
The first processor may be comprised of one or more integrated circuit chips. Alternatively, the first Processor may be a general purpose Processor, such as a Central Processing Unit (CPU) or a Network Processor (NP). Optionally, the first processor may control the robot to execute a navigation task, a delivery service, a meal delivery service, a guest greeting service, and the like according to the walking path in the static map.
The server 140 includes a second memory and a second processor. The second memory stores a second program, and the second program is called by the second processor to implement the path planning method provided by the present disclosure. Illustratively, the second memory stores a static map constructed according to the environment where the robot is located, and the second program is called to realize the path planning method provided by the present disclosure. Optionally, the second memory may include, but is not limited to, the following: RAM, ROM, PROM, EPROM, EEPROM. Alternatively, the second processor may be a general purpose processor, such as a CPU or NP.
The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a web service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform, but is not limited thereto. The robot and the server may be directly or indirectly connected through wired or wireless communication, and the disclosure is not limited thereto.
Usually, the environment in which the robot is located will have temporary obstacles with randomness and contingency at any time. The robot continuously detects environmental data through the sensor in the process of executing the navigation task to find the change of the environment so as to intelligently avoid obstacles and achieve the purpose of no collision. The appearance and disappearance of these temporary obstacles are both random and short-term in nature and are not suitable for curing down in medium-and long-term maps. In order to improve the working efficiency of the robot, the general method is as follows: global path planning is carried out in advance on a known medium-long term map constructed according to a static environment, and then non-collision navigation is realized through an intelligent obstacle avoidance and path updating algorithm by combining with dynamically detected environment data. The method of static planning and dynamic updating usually adopted in the moving process of the robot considers the environment known property and randomness, but still cannot effectively solve the problem of efficiency reduction caused by environment change (occurrence of temporary obstacles) in a short-term stage.
In an actual working scene of the robot, the number of road segments is often limited (each path is composed of a plurality of road segments), the robot always repeatedly passes through each road segment to execute tasks, the robot may pass through the path 1 at the current moment and be blocked by a barrier to update the path, the same robot or other robots may enter the path 1 at the next moment to find the same barrier, and the robot also enters repeated obstacle avoidance calculation and path update, so that the working efficiency of the robot is low. Such as: during the business peak hours of the restaurant, the robot always repeatedly passes through or enters each same road section according to the calculated optimal path by a fixed algorithm in the process of executing different meal delivery tasks, and the passing road section often has high repeatability. The dynamic detection and obstacle avoidance process of the robot takes a certain time, which is necessary. On one hand, because the intelligence of the robot is limited by hardware, and on the other hand, the obstacle is divided into static (an object which cannot move by itself) and dynamic (a person who can avoid by itself or an object which is moved by a person at the side immediately), the robot generally adopts a certain time-consuming strategy (such as voice reminding) to distinguish and request the person to avoid, the request shows high efficiency after the dynamic obstacle is effectively avoided, but in the short-term existence time of a completely invalid temporary obstacle (such as the movement of a chair occupying a narrow passage), the robot enters the same blocked passage for multiple times and repeatedly detects and avoids obstacle calculation, and the processes of voice reminding strategy and updating the path are slow and are often regarded as inefficient and not intelligent.
In view of the problems and deficiencies presented by the above, the present disclosure provides a more intelligent method for global path planning based on temporary obstacles and static maps.
Fig. 2 shows a flowchart of a path planning method for a robot, which is performed by an electronic device, for example, the robot or the server shown in fig. 1, according to an exemplary embodiment of the present disclosure. The path planning method shown in fig. 2 includes the following steps:
in step S101, a static map is acquired.
The static map is constructed according to the environment where the robot is located, can be constructed according to environment data acquired by various laser radar scanners, depth image cameras and other equipment, and can also be constructed according to laser point cloud data. The static map comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot.
In step S102, when an obstacle avoidance failure occurs while at least one robot executes a navigation task according to a static map, information of a temporary obstacle on a temporary obstacle path where the obstacle avoidance failure occurs is acquired.
When a plurality of robots respectively execute navigation tasks according to the static map, if one of the robots fails to avoid the obstacle, the robot acquires the information of the temporary obstacle on the temporary obstacle path where the obstacle-avoiding failure occurs. The information of the temporary obstacle comprises a life cycle of the temporary obstacle, the attribute of the temporary obstacle and the position of the temporary obstacle, and the life cycle represents the existence duration of the temporary obstacle. The temporary barrier path is a path from a starting point position to a target position, the path comprises a road section where the temporary barrier is located, and the road section where the temporary barrier is located is a road section where the occupied area of the temporary barrier is located.
In step S103, a walking path of at least one robot is re-planned according to the information of the temporary obstacle and the static map, and walking paths of other robots are planned according to the information of the temporary obstacle and the static map.
And after the information of the temporary obstacles is acquired, re-planning the walking path of at least one robot according to the information of the temporary obstacles and the static map, wherein the at least one robot is a robot which fails in obstacle avoidance. The at least one robot is already positioned on the temporary obstacle path and on the road section where the temporary obstacle is positioned due to the fact that obstacle avoidance failure occurs when the at least one robot executes the navigation task according to the static map, at the moment, information of the temporary obstacle is acquired and uploaded to the server, and the server replans the walking path of the at least one robot according to the information of the temporary obstacle and the static map. Because other robots do not drive to the road section where the temporary barrier is located, the server can replan the walking paths of other robots according to the information of the temporary barrier and the static map so as to prevent the robots from repeatedly entering the temporarily blocked road section, and therefore the working efficiency of the robots is improved.
Referring to fig. 3, fig. 3 is a flowchart illustrating another path planning method according to an exemplary embodiment of the disclosure. The method is performed by a computer device, for example, a terminal or a server in the computer system shown in fig. 1.
It should be noted that the content of the path planning method shown in fig. 3 is the same as the content of the path planning method shown in fig. 2, and the parts not mentioned in fig. 3 may refer to the description of fig. 2, and are not described herein again. The path planning method shown in fig. 3 includes the following steps:
in step S201, a static map is acquired.
The static map is constructed according to the environment where the robot is located, for example, environment data can be obtained through various devices such as laser radar scanners and depth image (RGB-D) cameras, or the environment data can be obtained through sensors such as Inertial Measurement Units (IMUs) of the robot, Global Positioning Systems (GPS), odometers, and the like, and then the map optimization is performed on the obtained environment data through an editing tool; accurately identifying the building layout of a work scene and the positions of fixed obstacles with long-term performance and stability in a static map, such as the positions of a VIP room layout in a restaurant environment, the positions of tables and chairs, a robot walking channel, a meal preparation place, a meal delivery place and the like in the restaurant environment, and planning the walking path of the robot according to the positions of the fixed obstacles; the robot static map thus constructed and edited in advance includes fixed obstacles having long-term and fixed characteristics, and the positions of these fixed obstacles do not change randomly in a short period of time, and may be referred to as a medium-and-long-term map, or a map set in which a plurality of floor maps are collected for one multi-floor building.
The point cloud data of the environment where the robot is located can be obtained through laser, and accurate positioning and navigation of the mobile robot can be achieved through a grid map obtained after the point cloud data are rasterized. The grid map represents the whole working environment by a plurality of grids with the same size, and the precision of the grid map can be reflected by the size of the grids. Each grid corresponds to a group of numerical values, the numerical values can reflect coordinates (x, y) of the point location on a grid map, and also can reflect space attributes and other information corresponding to the point location, the space attributes represent space occupation characteristics of the point location, for example, 0 can be used for identifying that the point location is not occupied by a barrier, 100 is used for identifying that the point location is occupied by the barrier, and-1 is used for identifying an unknown point location, so that whether the point location corresponding to the grid is a passable ground or a barrier point can be judged through the numerical values in the grid, and a basic situation of the robot environment passage is formed. The grid map can be regarded as a matrix structure formed by grid element values, each grid element value can be associated with a complex space attribute table, and various information attributes of point positions are identified. And taking the obtained grid map as a static map.
The static map accurately identifies the building layout of a working scene and some fixed obstacles with long-term performance and stability, so the static map can also be called as a medium-long term map, when the medium-long term map of the robot is constructed in advance by using a drawing tool and a manual editing method, all environment information can be collected by manually controlling the mobile robot in the working scene through a simultaneous positioning and mapping construction (SLAM)/visual-based positioning and drawing construction (VSLAM) technology, an overall environment map is constructed in a coordinate system, and noise points, walking paths, starting point positions, target positions, virtual walls and the like are eliminated through a manual editing method. When the robot medium-and-long-term map is created and filed, different map layers of the robot medium-and-long-term map, such as an original laser point cloud map layer with complete information, a grid map layer subjected to rasterization, a road network map layer for analyzing and dividing traffic information such as road sections and road network capacity (the road sections can contain the number of robots passing at the same time), a sparse point cloud map layer/dense point cloud map layer constructed by a VSLAM technology, and the like, can be respectively used for different purposes such as robot navigation or three-dimensional presentation.
For example, referring to fig. 4, fig. 4 is a schematic diagram of a two-dimensional planar grid map according to an exemplary embodiment of the present disclosure. On the basis of the grid map shown in fig. 4, a road network planning table shown in fig. 5 may be extracted, where the road network planning table includes all walking paths of the robot, road segments of each walking path, connection and separation relationships between road segments, road network capacity, weight, start point, end point, and service point; the weight may be a value for measuring a distance of a road segment or a value for measuring a time required to pass through the road segment, the weight of one path is a sum of weights of all road segments constituting the path, the path with the smallest weight value may be generally used as an optimal path, and the service point may be a location where the robot is charged. The road network planning table shown in fig. 5 can be expressed as:
in step S202, when an obstacle avoidance failure occurs while at least one robot performs a navigation task according to a static map, information of a temporary obstacle on a temporary obstacle path where the obstacle avoidance failure occurs is acquired.
For example, the environment in which the robot is located may be at any time with temporary obstacles, which may be random or accidental. The robot continuously detects environmental data through the sensor in the process of executing the navigation task to find the change of the environment so as to intelligently avoid obstacles and achieve the purpose of no collision. The appearance and disappearance of these temporary obstacles are both random and short-term in nature and not in a static map as a medium-and long-term map. When a plurality of robots respectively execute navigation tasks according to a static map, if one of the robots detects a temporary obstacle and fails in obstacle avoidance, the robot acquires information of the temporary obstacle.
The information of the temporary obstacle comprises the life cycle of the temporary obstacle, the attribute of the temporary obstacle and the position of the temporary obstacle; the life cycle represents the existence duration of the temporary barrier, which is predetermined according to the attribute of the temporary barrier, for example, a moving table and chair, a forgotten dining car may be moved by a person after several minutes, and then the corresponding life cycle may be 3 minutes, 5 minutes, and the like, and for example, two robots moving in opposite directions in a narrow road section cannot pass after collision, either one of the two robots uploads the information of the other robot to the server, the server updates the walking paths of the two robots according to the information of the robot, or controls one of the robots to send an alarm notification, so that the user moves the positions of the two robots, in this case, the corresponding life cycle may be 10 seconds, 20 seconds, and the like. The temporary obstacle path is a path from the starting point position to the target position, the path comprises a road section where the temporary obstacle is located, and the road section where the temporary obstacle is located is a road section where the occupied area of the temporary obstacle is located.
In step S203, a walking path of at least one robot is re-planned according to the information of the temporary obstacle and the static map, and walking paths of other robots are planned according to the information of the temporary obstacle and the static map.
And after the information of the temporary obstacles is acquired, re-planning the walking path of at least one robot according to the information of the temporary obstacles and the static map, wherein the at least one robot is a robot which fails in obstacle avoidance.
There are two ways to re-plan the travel path of the robot based on the information of the temporary obstacle and the static map:
firstly, constructing a temporary obstacle map according to the information of the temporary obstacle, wherein the temporary obstacle map comprises the information of the temporary obstacle, specifically the life cycle, the attribute and the position of the temporary obstacle; and obtaining a global map according to the superposition of the static map and the temporary barrier map, and planning the walking path of the robot according to the global map in the life cycle of the temporary barrier.
When the robot executes the navigation task for the first time, the map layer of the temporary obstacle map is blank, and the global map is equal to the static map at the moment. As shown in the static map of fig. 6, assuming that the robot needs to perform a navigation task from S point to T point, when navigating for the first time, it is assumed that its optimal path is S → a (link AB) → b (link BC) → c (link CT). Assuming that an obstacle avoidance failure event occurs at a point B in the process of executing a navigation task by the robot, triggering and constructing a temporary obstacle map, detecting a temporary obstacle and failing to avoid the obstacle at the point B in the navigation process, and generating a local temporary obstacle map according to a robot multi-sensor fusion algorithm, as shown in fig. 7. And superposing the static map and the temporary barrier map with the life cycle after the coordinate system conversion to obtain a global map, and planning the walking path of the robot according to the global map. The life cycle is the existence duration of a preset temporary barrier, the existence of the temporary barrier in the life cycle exceeds the life cycle time limit, the failure of the temporary barrier map is judged, at the moment, the road section where the temporary barrier is located reenters the path planning of the robot, the robot can try to pass through the road section again, whether the temporary barrier exists or not is detected through the sensor, and if the temporary barrier does not exist, the failure of the temporary barrier map is judged; if the temporary obstacle map is valid, recording the times of obstacle avoidance failures continuously occurring on a road section where the temporary obstacle is located, and when the times are larger than a preset threshold value, adding the temporary obstacle into the static map as a fixed obstacle of a medium-long term, or controlling the robot by the server to send a notice of requesting manual assistance to clear the obstacle, so that the temporary obstacle is cleared manually.
And secondly, calculating the dynamic path weight of the road section where the temporary barrier is located, and planning the walking paths of other robots according to the dynamic path weight and the static map.
The calculation method of the dynamic path weight comprises the following steps: and acquiring the initial path weight of the road section where the temporary barrier is located, and acquiring the dynamic path weight according to the initial path weight, the life cycle and the existing duration of the temporary barrier. The method includes that a robot continuously detects environmental data through a sensor in the process of executing a navigation task to find out environmental changes so as to intelligently avoid obstacles, when an obstacle avoidance failure of a temporary obstacle is detected, information of the temporary obstacle is uploaded to a server, the server resets the weight of a road section where the temporary obstacle is located to be a larger virtual weight, the weight value of a general normal road section is 5, 6, 8 and 2, the value of the virtual weight can be 100, the virtual weight can also be called as an initial path weight, the initial path weight can be gradually attenuated in the life cycle of the temporary obstacle, and therefore the weight of the road section where the temporary obstacle is located is dynamically changed
Wherein Weight is the dynamic path Weight, Weight0For the initial path weight, Lim _ Period is a memory cycle, and T _ elapse is a time length for which a temporary obstacle currently exists. Can clean upWhen the T _ elapse is equal to the Lim _ Period, the current existing duration of the temporary barrier is just equal to the life cycle of the temporary barrier, the value of the dynamic path weight is 0, and the weight of the road section where the temporary barrier is located is restored to a normal weight value; after the weight value of the temporary barrier is recovered to a normal weight value, the road section where the temporary barrier is located enters the path planning of the robot again, or the initial path weight is attenuated to a certain range, so that the road section where the temporary barrier is located can enter the path planning of the robot again, the robot can try to pass through the road section again under a certain probability, whether the temporary barrier exists or not is detected through a sensor, and if the temporary barrier does not exist, the road section where the temporary barrier is located is judged to enter the normal path planning; if the dynamic path weight can be maintained to be effective, the times of obstacle avoidance failures continuously occurring on a road section where the temporary obstacle is located are recorded, and when the times are larger than a preset threshold value, the temporary obstacle is added to a static map as a fixed obstacle of a medium-long term, or a server controls a robot to send a notice of requesting manual assistance for clearing the obstacle, so that the temporary obstacle is cleared manually.
Referring to fig. 8, a flowchart illustrating a method for planning a walking path according to an exemplary embodiment is disclosed. The step of planning the walking paths of other robots according to the dynamic path weight and the static map comprises the following steps:
in step S301, all feasible paths from the starting position to the target position are obtained.
The server may plan a new walking path from an overall perspective according to a service condition of the current robot, and obtain all feasible paths from a starting position to a target position of each robot, as shown in a feasible path diagram shown in fig. 9, where the robot needs to send a meal to a T02 seat number from a starting position S, and as shown in fig. 9, there are two different feasible paths, one of the feasible paths is a path a-a path b-a path f-a path n-a path T02, and the other feasible path is a path a-a path b-a path d-a path g-a path h-a target position T02.
In step S302, a weight value of each link of all feasible paths is obtained.
And acquiring a weight value of each road section of each feasible path, and if a temporary barrier exists on a certain road section, taking the current dynamic path weight of the road section as the weight value of the road section.
Step S303, calculating the path weight of all feasible paths according to the weight value of each road section.
The weight may be a value for measuring a distance of a link or a time required to traverse the link, and in one embodiment, a sum of weights of all links constituting a feasible path may be used as a path weight of the feasible path, for example, a sum of weights of one feasible path is 6+5+25+10 + 46 for the link a + the link b + the link f + the link n, and a sum of weights of another feasible path is 6+5+12+15+35+10 + 83 for the link a + the link b + the link d + the link g + the link h.
And step S304, planning the walking paths of other robots according to the path weights of all feasible paths.
In one embodiment, the path with the smallest weight and the smallest weight can be used as the optimal path and used as the walking path when the robot performs the navigation task.
It should be noted that, when the robot performs the navigation task according to the static map and an obstacle avoidance failure occurs, no matter which method is adopted to perform new path planning, if the road segment where the temporary obstacle is located is the only path from the starting position to the target position, a notification requesting manual assistance for obstacle clearance needs to be sent out, so that the temporary obstacle is manually cleared. For example, in a typical restaurant actual scene, a passage is narrow in some places, the width of the narrow passage can only accommodate one robot to pass through, if a seat of a guest is inadvertently pulled to occupy the passage, or a small cart is likely to block the narrow passage, or two robots run in opposite directions in the narrow passage, in order to ensure that the robots can walk in order in the working environment and that the robots do not collide with each other, the server can control the robots to send out a notice requesting manual assistance to clear obstacles, so that temporary obstacles can be cleared manually.
The path planning method comprises the steps of obtaining a static map constructed according to the environment where a robot is located, wherein the static map comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot, obtaining information of a temporary obstacle on the temporary obstacle path where obstacle avoidance fails when at least one robot executes a navigation task according to the static map, wherein the information of the temporary obstacle comprises a survival cycle, the survival cycle represents the existence duration of the temporary obstacle, the temporary obstacle path is the path from a starting point position to a target position, replanning the walking path of at least one robot according to the information of the temporary obstacle and the static map, and planning the walking paths of other robots according to the information of the temporary obstacle and the static map; when the obstacle avoidance failure occurs when any one robot executes a navigation task according to the static map, the information of the temporary obstacles can be acquired, then the walking paths of all the robots are re-planned according to the information of the temporary obstacles and the static map, and the data of environment change is shared among multiple robots, so that the optimal path can be still acquired when the robots deal with the continuous change of scenes, the walking paths are updated in time, and the multi-robot cooperation working efficiency is obviously improved.
Fig. 10 is a block diagram of a path planning apparatus according to an exemplary embodiment of the present disclosure. Referring to fig. 10, the apparatus 20 includes an acquisition module 201 and a processing module 203.
The obtaining module 201 is configured to obtain a static map; the static map is constructed according to the environment where the robot is located, and comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot;
the obtaining module 201 is further configured to obtain information of a temporary obstacle on a temporary obstacle path where an obstacle avoidance failure occurs when at least one robot performs a navigation task according to the static map; the information of the temporary obstacle comprises a life cycle, the life cycle represents the existence duration of the temporary obstacle, and the temporary obstacle path is a path from a starting position to a target position;
the processing module 203 is configured to replan the walking path of the at least one robot according to the information of the temporary obstacle and the static map, and to plan the walking paths of other robots according to the information of the temporary obstacle and the static map.
Optionally, the processing module 203 is further configured to construct a temporary obstacle map according to the information of the temporary obstacle;
obtaining a global map according to the static map and the temporary obstacle map in an overlapping mode;
and planning the walking paths of the other robots according to the global map in the life cycle.
Optionally, the processing module 203 is further configured to calculate a dynamic path weight of a road segment where the temporary obstacle is located;
and planning the walking paths of the other robots according to the dynamic path weight and the static map.
Optionally, the processing module 203 is further configured to obtain an initial path weight of a road segment where the temporary obstacle is located;
and obtaining dynamic path weight according to the initial path weight, the life cycle and the existing duration of the temporary barrier.
Optionally, the formula for calculating the dynamic path weight includes:
wherein Weight is the dynamic path Weight, Weight0For the initial path weight, Lim _ Period is the life cycle, and T _ elapse is the duration for which the temporary obstacle currently exists.
Optionally, the processing module 203 is further configured to obtain all feasible paths from the starting position to the target position;
acquiring a weight value of each road section of all the feasible paths; the weight value of each road section comprises the value of the current dynamic path weight;
calculating the path weight of all the feasible paths according to the weight value of each road section;
and planning the walking paths of the other robots according to the path weights of all the feasible paths.
Optionally, the processing module 203 is further configured to control the other robots to move according to the walking path;
if at least one of the other robots detects that the temporary obstacle does not disappear, recording the times of the obstacle avoidance failures continuously occurring on the temporary obstacle path;
and if at least one of the other robots detects that the temporary obstacle disappears, replanning the walking paths of all the robots according to the static map, and clearing the times of the obstacle avoidance failures continuously occurring on the temporary obstacle path.
Optionally, when the number of times of the obstacle avoidance failures occurring continuously on the temporary obstacle path is greater than a predetermined threshold, adding the temporary obstacle to the static map, or sending a notification requesting for manually assisting to clear the obstacle.
Optionally, when the road segment where the temporary obstacle is located is the only path from the starting position to the target position, sending a notification requesting manual assistance to clear the obstacle.
With regard to the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be described in detail here.
Fig. 11 is a block diagram illustrating an electronic device 700 in accordance with an example embodiment. As shown in fig. 11, the electronic device 700 may be the robot shown in fig. 1, and the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the path planning method. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 705 may thus include: Wi-Fi module, Bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described path planning method.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the path planning method described above is also provided. For example, the computer readable storage medium may be the above-mentioned memory 702 comprising program instructions that are executable by the processor 701 of the electronic device 700 to perform the above-mentioned path planning method.
Fig. 12 is a block diagram illustrating an electronic device 1900 according to an example embodiment. For example, the electronic device 1900 may be provided as a server. Referring to fig. 12, an electronic device 1900 includes a processor 1922, which may be one or more in number, and a memory 1932 for storing computer programs executable by the processor 1922. The computer program stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processor 1922 may be configured to execute the computer program to perform the path planning method described above.
Additionally, electronic device 1900 may also include a power component 1926 and a communication component 1950, the power component 1926 may be configured to perform power management of the electronic device 1900, and the communication component 1950 may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 1900. In addition, the electronic device 1900 may also include input/output (I/O) interfaces 1958. The electronic device 1900 may operate based on an operating system, such as Windows Server, stored in memory 1932TM，Mac OS XTM，UnixTM，LinuxTMAnd so on.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the path planning method described above is also provided. For example, the computer readable storage medium may be the memory 1932 including program instructions executable by the processor 1922 of the electronic device 1900 to perform the path planning method described above.
In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described path planning method when executed by the programmable apparatus.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Examples
1. A path planning method is applied to a server, and comprises the following steps:
obtaining a static map; the static map is constructed according to the environment where the robot is located, and comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot;
acquiring information of temporary obstacles on a temporary obstacle path with obstacle avoidance failure under the condition that the at least one robot executes a navigation task according to the static map and the obstacle avoidance failure occurs; the information of the temporary obstacle comprises a life cycle, the life cycle represents the existence duration of the temporary obstacle, and the temporary obstacle path is a path from a starting position to a target position;
and replanning the walking path of the at least one robot according to the information of the temporary obstacles and the static map, and planning the walking paths of other robots according to the information of the temporary obstacles and the static map.
2. The method according to embodiment 1, wherein the step of planning the walking path of the other robot according to the information of the temporary obstacle comprises:
constructing a temporary obstacle map according to the information of the temporary obstacle;
obtaining a global map according to the static map and the temporary obstacle map in an overlapping mode;
and planning the walking paths of the other robots according to the global map in the life cycle.
3. The method according to embodiment 1, wherein the path is composed of a plurality of segments, and the step of planning the walking path of the other robot according to the information of the temporary obstacle comprises:
calculating the dynamic path weight of the road section where the temporary barrier is located;
and planning the walking paths of the other robots according to the dynamic path weight and the static map.
4. The method according to embodiment 3, wherein the step of calculating the dynamic path weight of the road segment where the temporary obstacle is located comprises:
acquiring an initial path weight of a road section where the temporary barrier is located;
and obtaining dynamic path weight according to the initial path weight, the life cycle and the existing duration of the temporary barrier.
5. The method of embodiment 4, wherein the formula for calculating the dynamic path weights comprises:
wherein Weight is the dynamic path Weight, Weight0For the initial path weight, Lim _ Period is the life cycle, and T _ elapse is the duration for which the temporary obstacle currently exists.
6. The method of embodiment 3, wherein the static map comprises a start location and a target location of the robot; the step of planning the walking paths of the other robots according to the dynamic path weights and the static map comprises the following steps:
acquiring all feasible paths from the starting position to the target position;
acquiring a weight value of each road section of all the feasible paths; the weight value of each road section comprises the value of the current dynamic path weight;
calculating the path weight of all the feasible paths according to the weight value of each road section;
and planning the walking paths of the other robots according to the path weights of all the feasible paths.
7. The method of embodiment 2 or 6, wherein the step of planning the walking path of the other robot according to the dynamic path weight and the static map comprises:
controlling the other robots to move according to the walking paths;
if at least one of the other robots detects that the temporary obstacle does not disappear, recording the times of the obstacle avoidance failures continuously occurring on the temporary obstacle path;
and if at least one of the other robots detects that the temporary obstacle disappears, replanning the walking paths of all the robots according to the static map, and clearing the times of the obstacle avoidance failures continuously occurring on the temporary obstacle path.
8. The method of embodiment 7, wherein,
when the number of times of obstacle avoidance failures continuously occurring on the temporary obstacle path is larger than a preset threshold value, adding the temporary obstacle to the static map, or sending a notice requesting for manually assisting in clearing obstacles.
9. The method according to embodiment 2 or 6, wherein the information of the temporary obstacle includes a position of a road section where the temporary obstacle is located;
and sending a notice of requesting manual assistance to clear the obstacle when the road section where the temporary obstacle is located is the only path from the starting position to the target position.
10. A path planning device is applied to a server, and comprises:
the acquisition module is used for acquiring a static map; the static map is constructed according to the environment where the robot is located, and comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot;
the acquisition module is further used for acquiring information of temporary obstacles on a temporary obstacle path where obstacle avoidance failure occurs when at least one robot executes a navigation task according to the static map and the obstacle avoidance failure occurs; the information of the temporary obstacle comprises a life cycle, the life cycle represents the existence duration of the temporary obstacle, and the temporary obstacle path is a path from a starting position to a target position;
and the processing module is used for replanning the walking path of the at least one robot according to the information of the temporary obstacles and the static map, and replanning the walking paths of other robots according to the information of the temporary obstacles and the static map.
11. A computer-readable storage medium, on which a computer program is stored, wherein the program realizes the steps of the method of any of the embodiments 1-9 when executed by a processor.
12. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of any of embodiments 1-9.
Claims (12)
1. A path planning method is applied to a server and is characterized by comprising the following steps:
obtaining a static map; the static map is constructed according to the environment where the robot is located, and comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot;
acquiring information of temporary obstacles on a temporary obstacle path with obstacle avoidance failure under the condition that the at least one robot has the obstacle avoidance failure when executing a navigation task according to the static map; the information of the temporary obstacle comprises a life cycle, the life cycle represents the existence duration of the temporary obstacle, and the temporary obstacle path is a path from a starting position to a target position;
and replanning the walking path of the at least one robot according to the information of the temporary obstacles and the static map, and planning the walking paths of other robots according to the information of the temporary obstacles and the static map.
2. The method of claim 1, wherein the step of planning the walking path of the other robot according to the information of the temporary obstacle comprises:
constructing a temporary obstacle map according to the information of the temporary obstacle;
obtaining a global map according to the static map and the temporary obstacle map in an overlapping mode;
and planning the walking paths of the other robots according to the global map in the life cycle.
3. The method of claim 1, wherein the path is composed of a plurality of segments, and the step of planning the walking path of the other robot according to the information of the temporary obstacle comprises:
calculating the dynamic path weight of the road section where the temporary barrier is located;
and planning the walking paths of the other robots according to the dynamic path weight and the static map.
4. The method of claim 3, wherein the step of calculating the dynamic path weight for the road segment on which the temporary obstacle is located comprises:
acquiring an initial path weight of a road section where the temporary barrier is located;
and obtaining dynamic path weight according to the initial path weight, the life cycle and the existing duration of the temporary barrier.
5. The method of claim 4, wherein the formula for calculating the dynamic path weight comprises:
wherein Weight is the dynamic path Weight, Weight0For the initial path weight, Lim _ Period is the life cycle, and T _ elapse is the duration for which the temporary obstacle currently exists.
6. The method of claim 3, wherein the static map comprises a starting location and a target location of the robot; the step of planning the walking paths of the other robots according to the dynamic path weights and the static map comprises the following steps:
acquiring all feasible paths from the starting position to the target position;
acquiring the weight value of each road section of all the feasible paths; the weight value of each road section comprises the value of the current dynamic path weight;
calculating the path weight of all the feasible paths according to the weight value of each road section;
and planning the walking paths of the other robots according to the path weights of all the feasible paths.
7. The method according to claim 2 or 6, wherein the step of planning the walking path of the other robot according to the dynamic path weight and the static map is followed by:
controlling the other robots to move according to the walking paths;
if at least one of the other robots detects that the temporary obstacle does not disappear, recording the times of the obstacle avoidance failures continuously occurring on the temporary obstacle path;
and if at least one of the other robots detects that the temporary obstacle disappears, replanning the walking paths of all the robots according to the static map, and clearing the times of the obstacle avoidance failures continuously occurring on the temporary obstacle path.
8. The method of claim 7,
when the number of times of obstacle avoidance failures continuously occurring on the temporary obstacle path is larger than a preset threshold value, adding the temporary obstacle to the static map, or sending a notice requesting for manually assisting in clearing obstacles.
9. The method according to claim 2 or 6, characterized in that the information of the temporary obstacle comprises the position of the road section on which the temporary obstacle is located;
and sending a notice of requesting manual assistance to clear the obstacle when the road section where the temporary obstacle is located is the only path from the starting position to the target position.
10. A path planning device applied to a server is characterized by comprising:
the acquisition module is used for acquiring a static map; the static map is constructed according to the environment where the robot is located, and comprises the position of a fixed obstacle in the environment where the robot is located and the walking path of the robot;
the acquisition module is further used for acquiring information of temporary obstacles on a temporary obstacle path where obstacle avoidance failure occurs under the condition that the at least one robot performs a navigation task according to the static map and the obstacle avoidance failure occurs; the information of the temporary obstacle comprises a life cycle, the life cycle represents the existence duration of the temporary obstacle, and the temporary obstacle path is a path from a starting position to a target position;
and the processing module is used for replanning the walking path of the at least one robot according to the information of the temporary obstacles and the static map, and replanning the walking paths of other robots according to the information of the temporary obstacles and the static map.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 9.
12. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210333261.XA CN114740844A (en) | 2022-03-30 | 2022-03-30 | Path planning method and device, computer readable storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210333261.XA CN114740844A (en) | 2022-03-30 | 2022-03-30 | Path planning method and device, computer readable storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114740844A true CN114740844A (en) | 2022-07-12 |
Family
ID=82278577
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210333261.XA Pending CN114740844A (en) | 2022-03-30 | 2022-03-30 | Path planning method and device, computer readable storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114740844A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117250965A (en) * | 2023-11-20 | 2023-12-19 | 广东电网有限责任公司佛山供电局 | Robot obstacle avoidance rapid path reconstruction method and system |
-
2022
- 2022-03-30 CN CN202210333261.XA patent/CN114740844A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117250965A (en) * | 2023-11-20 | 2023-12-19 | 广东电网有限责任公司佛山供电局 | Robot obstacle avoidance rapid path reconstruction method and system |
| CN117250965B (en) * | 2023-11-20 | 2024-02-23 | 广东电网有限责任公司佛山供电局 | Robot obstacle avoidance rapid path reconstruction method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10845821B2 (en) | Route planning for a mobile robot using configuration-based preferences | |
| US11199412B2 (en) | Collaborative creation of indoor maps | |
| EP3485661B1 (en) | Electronic device, server, and method for determining presence or absence of user within specific space | |
| CN104567878B (en) | Map handling for location-based services in compliance with localized environments | |
| KR101398047B1 (en) | Methods and apparatuses for determining if access to a region is feasible or infeasible for a user of a mobile device | |
| US20110172906A1 (en) | Scalable Routing For Mobile Station Navigation With Location Context Identifier | |
| WO2011088397A1 (en) | Using filtering with mobile device positioning in a constrained environment | |
| US20180252535A1 (en) | Techniques for Dynamic Mapping of a Facility Using Patient Transport Apparatuses | |
| Dudas et al. | ONALIN: Ontology and algorithm for indoor routing | |
| US20120232795A1 (en) | Method for creating a map relating to location-related data on the probability of future movement of a person | |
| KR20140056320A (en) | Likelihood of mobile device portal transition | |
| CN110345935B (en) | Indoor map matching and positioning method | |
| KR101868695B1 (en) | Method for controlling mobile robot unit based on bayesian network learning | |
| US9702705B2 (en) | Crowd-assisted micro-navigation | |
| CN110470308A (en) | A kind of obstacle avoidance system and method | |
| CN114740844A (en) | Path planning method and device, computer readable storage medium and electronic equipment | |
| Fernández et al. | Indoor location system based on zigbee devices and metric description graphs | |
| Wang et al. | Enhancing the map usage for indoor location-aware systems | |
| WO2023275760A1 (en) | Systems and methods for navigation of an autonomous system | |
| Ghantous et al. | Augmented reality indoor navigation based on Wi-Fi trilateration | |
| US20210329407A1 (en) | Constrained user device location using building topology | |
| KR102504941B1 (en) | Object address based delivery method and apparatus using autonomous driving robot | |
| US10228441B2 (en) | Accurate tracking information system | |
| WO2024075477A1 (en) | Evacuation information generation system, evacuation information generation device, autonomous traveling device, evacuation information generation method, and evacuation information generation program | |
| CN116787449B (en) | Method and device for processing running path blockage, electronic equipment and storage medium |
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 |