Robot indoor and outdoor fixed-point inspection method and system based on GNSS and 2D laser radar

Abstract

The invention provides a robot indoor and outdoor fixed-point inspection method based on a GNSS and a 2D laser radar, and relates to the technical field of positioning navigation. The method comprises the following steps that S1, indoor and outdoor environment information is collected, a map is established, key nodes are selected indoors and outdoors, and key node information is collected; s2, constructing a topological map; s3, the robot receives all the specified inspection sites transmitted by the client, plans an inspection route passing through all the specified inspection sites, and starts to operate; s4, the robot arrives at the first designated inspection site, obtains key node information, receives the next inspection site as a new target site, judges whether the new target site is indoor or outdoor, adjusts the robot positioning method and goes to the new target site until the robot passes through all the designated inspection sites in sequence; and S5, determining whether the robot inspects again or not according to requirements. According to the invention, the robot can be accurately positioned in different indoor and outdoor environments, and positioning and navigation methods can be timely and accurately switched according to the environment.

Description

Technical Field

[0001] The present invention relates to the field of positioning and navigation technology, and in particular to a robot indoor and outdoor fixed-point inspection method and system based on GNSS and 2D laser radar. Background Art

[0002] Existing robot navigation methods include: LiDAR navigation, visual navigation, GNSS navigation, channel navigation, etc. Among them, laser navigation and GNSS navigation are the most widely used navigation methods nowadays. LiDAR navigation, which is currently the most widely used in indoor navigation, can achieve good positioning accuracy indoors, but in open outdoor environments, the positioning system will have large errors or even be unable to perform positioning due to environmental reasons, and will cause the robot's on-board computer to have a huge amount of calculations; the principle of the GNSS positioning system in outdoor navigation is simple and clear, but at the junction of indoor and outdoor environments, it will lose positioning due to building obstruction, changes in sky visibility, etc. The above two navigation methods can perform navigation tasks in a single environment, such as a single indoor environment or outdoor environment, but when the environment changes, navigation may be unable to perform positioning, inaccurate positioning, or lose positioning.

[0003] In actual applications, inspection robots often need to perform daily inspection work in different environments such as indoor machine rooms and outdoor facilities. For example, publication number CN114739404A discloses a live working robot positioning system, including a host, a communication box, an industrial computer and a sensor. The host, the communication box and the sensor are all installed on the outdoor live working robot. The host integrates the inertial measurement unit, the GNSS-RTK positioning module, the main control chip and the peripheral hardware interface. The main control chip is provided with the ESKF filter. The communication box, the industrial computer and the sensor are all communicated with the host, and the industrial computer is communicated with the laser radar module, which can meet the positioning needs of the live robot in various scenarios. However, the following problems still exist: the GNSS-RTK positioning module and the laser radar module are always in the positioning working state in different scenarios, which causes a waste of resources to a certain extent. The robot's on-board computer has a huge amount of calculation, or needs to be replaced with a computer with stronger computing power, thereby increasing the production cost. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to achieve accurate positioning of the inspection robot in different indoor and outdoor environments, and to achieve timely and accurate switching of positioning and navigation methods to avoid unnecessary waste of resources.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar, comprising the following steps:

[0006] S1: Collect indoor and outdoor environmental information and build maps, select key nodes indoors and outdoors and collect key node information;

[0007] S2: Determine the connectivity between key nodes and construct the corresponding topological map;

[0008] S3: The robot receives all designated inspection locations transmitted by the client, and plans an inspection route that passes through all designated inspection locations according to the global path planning algorithm, and then the robot starts running;

[0009] S4: After the robot arrives at the first designated inspection location, it obtains key node information of the inspection location, receives the next inspection location as a new target location and obtains key node information, determines whether the new target location is indoors or outdoors, adjusts the robot positioning method and goes to the new target location, until the robot passes through each designated inspection location on the inspection route in turn;

[0010] S5: Navigation ends, and the robot decides whether to perform inspection work again based on actual requirements.

[0011] Preferably, in step S1, the specific process of mapping the indoor and outdoor environments, selecting key nodes, and collecting key node information is as follows:

[0012] S101: First, collect and map the indoor environment; construct a 2D grid map of the indoor environment through a 2D laser radar, and use the SLAM algorithm in the robot operating system to construct a 2D grid map including a pgm image file and a yaml map parameter file; select all indoor inspection locations, channel corners, entrances and exits of the robot as key nodes, and map the corresponding coordinates (x a ,y a ) are numbered, recorded and stored in the robot's onboard computer;

[0013] S102: Collect and map the outdoor environment; select all outdoor inspection locations and road intersections of the robot as key nodes, use the robot's GNSS module to read the longitude and latitude information of the key nodes in the WGS-84 coordinate system and record them as coordinates (x b ,y b ), and (x b ,y b ) is converted to the northeast celestial coordinate, that is, the UTM coordinate under ENU (E i ,N i ), the coordinates of each key node are numbered, recorded and stored in the robot's onboard computer.

[0014] Preferably, after steps S101 and S102, each key node contains the following information: (xai ,y ai ,E i ,N i ,a), where x ai ,y ai is the coordinate of the robot in the 2D laser grid map, E i ,N i is the coordinate of the robot in the UTM coordinate system, and a is the number of the 2D grid map. If there are multiple indoor buildings that need to be inspected, use a to sort the different maps by number. For key outdoor nodes, x ai ,y ai is set to infinity, a is set to 0; for the key nodes in the room, E i ,N i Set to infinity, a is the number corresponding to the grid map where it is located; for the key nodes at the interface between indoor and outdoor, record the coordinates of the two coordinate systems at the same time; among them, the indoor key node (x ai ,y ai ) and (x aj ,y aj ) is: The distance between key outdoor locations is calculated based on the coordinates of two points in the UTM coordinate system (E i ,N i ) and (E j ,N j ) is used for calculation, the distance between two points outdoors is:

[0015]

[0016] Preferably, in step S2, the topological map constructed is G=<V,E,W> , where V is the vertex set, E is the edge set, and W is the weight.

[0017] Preferably, in step S3, the specific process of the robot planning an inspection route passing through all designated inspection locations is as follows:

[0018] S301: Classify key nodes according to whether they are designated inspection locations according to all designated inspection locations input by the client;

[0019] S302: Starting from the current starting point, search for key nodes around and determine whether they are designated inspection locations; if so, record the distance from the current starting point to the designated inspection location; if not, continue searching until the distances from the current starting point to all designated inspection locations are recorded; then sort the distances from the current starting point to all designated inspection locations by length, and select the designated inspection location with the shortest distance to the current starting point as the new starting point;

[0020] S303: starting from the new starting point determined in step S302, re-execute step S302;

[0021] S304: Repeat S302-S303 until all designated inspection locations are visited; then find the shortest path from the last designated inspection location visited to the final target location, merge all segmented paths, and finally obtain a shortest path from the starting point to the final target location that is approximately the optimal solution.

[0022] Preferably, step S3 also includes a process in which the robot calculates the shortest path for different working environments. The specific process is: if the robot works only indoors or only outdoors, then according to the formula or Normal calculation; if the robot works in both indoor and outdoor environments, add designated outdoor points at the entrances and exits of the building, and calculate the shortest indoor and outdoor paths respectively; if the robot needs to work in multiple indoor environments, add designated points at the entrances and exits of the building, calculate the outdoor path first, obtain the shortest outdoor path, and conduct inspections according to the order of the building entrances and exits on the obtained shortest path.

[0023] Preferably, in step S4, the process of determining whether the new target location is indoors or outdoors is as follows: when the robot reaches a target node (x ai ,y ai ,E i ,N i ,a i ), the next designated inspection location in the inspection route is taken as the new target node, and the key node information (x aj ,y aj ,E j ,N j ,a j ) to the computer,

[0024] If a i -a j =0, the robot is in a single indoor or outdoor environment, the current navigation method remains unchanged, and the robot moves to the next target node;

[0025] If a i -a j <0, the robot moves from the outdoors to the indoors, turns off the GNSS module, and starts the lidar positioning algorithm for indoor positioning;

[0026] If a i -a j >0, the robot moves from indoors to outdoors, turns off the lidar positioning algorithm, and starts the GNSS module for outdoor positioning.

[0027] Preferably, in step S4, when the robot is working on the planned inspection route, a laser radar is activated as a sensor for local obstacle avoidance.

[0028] Preferably, after the navigation is completed, the robot stops patrolling; if the robot needs to patrol again, the robot will re-plan a global path from the end point of the current path and start patrolling; if the robot needs to be charged, the robot will plan a path directly to the charging location and go there on its own.

[0029] Corresponding to the above method, the present invention also provides a robot indoor and outdoor fixed-point inspection system based on GNSS and 2D laser radar, comprising the following modules:

[0030] Mapping module: used to collect indoor and outdoor environmental information and build maps, select key nodes indoors and outdoors and collect key node information;

[0031] Topology map construction module: used to determine the connectivity between key nodes and construct the corresponding topology map;

[0032] Inspection route planning module: After the robot receives all designated inspection locations transmitted by the client, it plans an inspection route that passes through all designated inspection locations according to the global path planning algorithm, and then the robot starts running;

[0033] Adjust the robot positioning method module: used to obtain the key node information of the first designated inspection location after the robot arrives at the inspection location, receive the next inspection location as the new target location and obtain the key node information, determine whether the new target location is indoors or outdoors, adjust the robot positioning method and go to the new target location, until the robot passes through each designated inspection location on the planned inspection route in turn;

[0034] Module for judging whether to execute inspection: It is used to decide whether to execute inspection again according to actual requirements at the end of navigation.

[0035] The advantages of the present invention are:

[0036] (1) In view of the current working limitations of robots due to different indoor and outdoor positioning methods, the present invention can simultaneously utilize two navigation methods to enable the robot to perform inspection work in different indoor and outdoor environments;

[0037] (2) In view of the actual work budget, the computing power of the robot's on-board computer is usually weak and cannot perform complex positioning and navigation systems. The present invention combines GNSS with 2D laser radar algorithms and can switch positioning and navigation methods in a timely and accurate manner, thereby avoiding the problem of huge computing power of the on-board computer when the robot uses laser radar for outdoor inspections, effectively reducing the computer's computing power and operating costs, and can be run on a vehicle-mounted computer with lower computing power;

[0038] (3) The present invention has excellent flexibility in actual operation, can flexibly select the robot's work location according to the needs of the staff and work tasks, and can achieve autonomous obstacle avoidance during work. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flowchart of the overall embodiment 1 of the present invention;

[0040] Figure 2 The 2D grid map obtained by the laser radar SLAM algorithm in Example 1 of the present invention;

[0041] Figure 3 This is a schematic diagram of key outdoor navigation nodes on a satellite map in Example 1 of the present invention;

[0042] Figure 4 A topological map of key points of a 2D grid map in Embodiment 1 of the present invention;

[0043] Figure 5 A topological diagram of key outdoor navigation nodes in Embodiment 1 of the present invention;

[0044] Figure 6 This is a flow chart of indoor and outdoor fixed-point navigation in Example 1 of the present invention;

[0045] Figure 7 This is a flow chart of an extended Kalman filter for an outdoor navigation system in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] like Figure 1As shown, it is a flowchart of the whole embodiment 1 of the present invention. The present invention provides a robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar, comprising the following steps:

[0049] S1: Collect indoor and outdoor environmental information and build maps, select key nodes indoors and outdoors and collect key node information. The specific process is as follows:

[0050] S101: First, collect and map the indoor environment information, build a two-dimensional grid map through the Cartographer laser radar SLAM algorithm, and use the Robot Operating System (ROS) to create a 2D laser grid map file containing a pgm image file and a yaml map parameter file, such as Figure 2 As shown in the figure, the robot's indoor inspection locations, channel corners, and entrances and exits are taken as key nodes, and the corresponding coordinates (x a ,y a ) are numbered, recorded and stored in the robot's onboard computer. When performing indoor laser radar mapping, a small part of the outdoor area is also mapped to facilitate the calculation of indoor and outdoor navigation paths and module switching;

[0051] S102: Collect outdoor environment information and create a map, such as Figure 3 As shown in the figure, the robot's outdoor inspection location and the intersection of the road are taken as key nodes. The robot is operated to pass through the outdoor key nodes, and the latitude and longitude information received by the robot's GNSS module is read and recorded as (x b ,y b ), in order to import the coordinate information into the robot's own / odom coordinate system, it is necessary to convert the longitude and latitude coordinates (x bi ,y b ) is converted to the Unified Transverse Mercator (UTM) coordinates (E i ,N i ), get the new coordinates, this step can be achieved through the function package in Python; store each node number and coordinate information in the robot's onboard computer;

[0052] S103: After the above two steps, each key node contains the following information: (x ai ,y ai ,E i ,N i ,a), where x ai ,y ai is the coordinate of the robot in the 2D laser grid map, E i ,N iis the coordinate of the robot in the UTM coordinate system, a is the number of the 2D grid map. If there are multiple indoor buildings that need to be inspected, the different maps are numbered and sorted. For outdoor nodes, x ai ,y ai is set to infinity, a is set to 0; for indoor nodes, E i ,N i Set to infinity, a to the number corresponding to the grid map where it is located; for the nodes at the interface between indoor and outdoor, record the coordinates of both coordinate systems at the same time, so that the connection relationship can be quickly determined.

[0053] S2: Determine the connectivity between key nodes and build the corresponding topological map; Figure 4 and Figure 5 , according to the actual situation of the robot's working place and the connectivity relationship between each point, construct an undirected graph G =<V,E,W> , where V is the vertex set, E is the edge set, and W is the weight. The abstract topological graph of the inspection robot working environment is obtained. Among them, the indoor key nodes (x ai ,y ai ) and (z aj ,y aj ) is: The distance between key outdoor locations is calculated based on the coordinates of two points in the UTM coordinate system (E i ,N i ) and (E j ,N j ) is used for calculation, the distance between two points outdoors is:

[0054] S3: The robot receives all designated inspection locations from the client and uses the global path planning algorithm to:

[0055] S301: Classify key nodes according to whether they are designated inspection locations according to all designated inspection locations input by the client;

[0056] S302: Starting the Dijkstra algorithm from the current starting point, searching for key nodes around the current starting point and determining whether it is a designated inspection location; if so, recording the distance from the current starting point to the designated inspection location; if not, continuing the search until the distances from the current starting point to all designated inspection locations are recorded; then sorting the distances from the current starting point to all designated inspection locations by length, and selecting the designated inspection location with the shortest distance to the current starting point as the new starting point;

[0057] S303: starting from the new starting point determined in step S302, re-execute step S302;

[0058] S304: Repeat S302-S303 until all designated inspection locations are visited; then find the shortest path from the last designated inspection location visited to the final target location, merge all segmented paths, and finally obtain a shortest path from the starting point to the final target location that is close to the optimal solution;

[0059] When planning inspection routes, the robot calculates the shortest path in different working environments in different processes, specifically:

[0060] If the robot only works in a single environment, such as only indoors or outdoors, then the formula is: and Normal calculation;

[0061] If the robot works in both indoor and outdoor environments, add outdoor designated points at the entrance and exit of the building, and calculate the shortest path between the designated inspection location indoors and the outdoor designated point; the shortest path between the designated inspection location outdoors and the outdoor designated point;

[0062] If the robot needs to inspect multiple indoor environments, add designated points at the building entrances and exits, calculate the outdoor path first, obtain the shortest outdoor path, and inspect the indoor environment according to the order of the building entrances and exits on the obtained shortest path;

[0063] After planning a shortest inspection route that passes through all designated inspection locations, the robot starts running.

[0064] S4: Figure 6 As shown in the figure, the robot passes through the target nodes on the inspection route in sequence. The specific process is as follows:

[0065] S401: After the robot arrives at the first designated inspection location, it obtains the key node information of the inspection location (x ai ,y ai ,E i ,N i ,a i ), receive the next inspection location as the new target location and obtain key node information (x aj ,y aj ,E j ,N j ,a j ), after determining whether the new target location is indoors or outdoors, adjust the robot positioning method. The specific adjustment process is:

[0066] (1) If a i -a j =0, the robot is in a single indoor or outdoor environment, the current navigation method remains unchanged, and the robot moves to the next target node;

[0067] (2) If a i -a j <0, the robot enters the room from the outdoor, converts the outdoor navigation to the indoor navigation, turns off the GNSS part of the extended Kalman filter algorithm, starts the lidar positioning algorithm, reads the corresponding 2D grid map, performs initial repositioning, determines the robot's position in the indoor 2D grid map, and then moves to the next target location:

[0068] (3) If a i -a j >0, the robot moves from indoors to outdoors, switches from indoor navigation to outdoor navigation, turns off the lidar positioning algorithm, starts the extended Kalman filter algorithm combined with GNSS information for outdoor positioning, and moves to the next target location;

[0069] S402: If it is determined that the robot enters indoors from outdoors and needs to perform indoor inspection, when the robot arrives at the key node of the building entrance and exit, the building where the robot is located is determined, the corresponding topological map and the coordinate information of the key node are read, and indoor fixed-point navigation is performed. The GNSS part of the extended Kalman filter algorithm is turned off, and the lidar adaptive Monte Carlo positioning (AMCL) algorithm is started to perform initial repositioning to determine the position of the robot in the indoor 2D grid map;

[0070] When the robot is in an indoor environment, it performs fixed-point navigation based on the existing topological map and key node coordinates. The robot takes the designated inspection locations on the indoor part of the inspection route as the end points of a single planned path and passes through them in sequence. During the operation of the robot, the laser radar is activated as a sensor for local obstacle avoidance. When an unknown obstacle appears within the working range of the laser radar, the robot avoids the obstacle through a dynamic window local path planning algorithm, bypasses the obstacle, and continues to move toward the local target location.

[0071] S403: If it is determined that the robot enters the outdoors from indoors, the robot needs to conduct outdoor inspections. The robot combines the information provided by sensors such as GNSS, IMU, and odometer to improve the outdoor positioning accuracy. The output result is the corrected odometer information and GNSS information, such as Figure 7 As shown; with the help of the corrected information, the robot follows the inspection path obtained in S3, and takes the points on the path as the end points of the local path in turn. During the driving process, the robot also uses the dynamic window method for local path planning and the laser radar for local obstacle avoidance;

[0072] When the robot is outdoors and the signal is good, for the outdoor GNSS positioning system, the extended Kalman filter (EKF) algorithm is used to fuse GNSS, wheel odometers, IMU and other sensors to obtain more accurate location information; the robot passes through the outdoor designated inspection locations on the inspection route in turn, converts the UTM coordinate information of the outdoor nodes into the robot's own / odom coordinate system, sends the coordinates of the target node, and the robot moves towards the target location;

[0073] S404: After arriving at a target location, the robot receives key node information of arriving at the target location, and after confirming that the robot has arrived at the target node, it takes the current node as the starting point and re-executes S4.

[0074] S5: After passing each node on the global path, navigation ends, and the decision on whether to execute the work in a loop is made based on the requirements. The robot's next action is set based on the specific needs of the work: if a loop inspection is required, the robot will re-plan an inspection route starting from the end of the current path based on all designated inspection locations reset by the operator this time, and repeat the above fixed-point navigation process; if charging is required, the robot can plan a path directly to the charging location and go there on its own.

[0075] In this embodiment, a human-computer interaction interface is also included. After connecting the robot's onboard computer to the local computer through SSH or other means, key nodes and indoor and outdoor maps can be easily set and adjusted. It can also monitor the robot's operating status, real-time trajectory and other information, and issue inspection tasks according to actual requirements.

[0076] In actual operation, this embodiment can flexibly select the robot's positioning method according to the needs of the staff and work tasks, and can give full play to the advantages of GNSS and 2D lidar, so that the robot can perform inspection work in different indoor and outdoor environments. The robot can switch positioning and navigation methods in a timely and accurate manner for different environments, and can avoid unnecessary waste of resources by using two or more methods at the same time. It can also avoid obstacles autonomously during work and has excellent flexibility.

[0077] Example 2

[0078] Corresponding to the first embodiment of the present invention, the second embodiment of the present invention further provides a robot indoor and outdoor fixed-point inspection system based on GNSS and 2D laser radar, including the following modules:

[0079] Mapping module: used to collect indoor and outdoor environmental information and build maps, select key nodes indoors and outdoors and collect key node information; specifically includes the following units:

[0080] The acquisition and mapping unit is used to perform the following steps: first, the indoor environment information is acquired and mapped, a two-dimensional grid map is constructed by the Cartographer laser radar SLAM algorithm, and a 2D laser grid map file containing a pgm image file and a yaml map parameter file is created using the Robot Operating System (ROS), such as Figure 2 As shown in the figure, the robot's indoor inspection locations, channel corners, and entrances and exits are taken as key nodes, and the corresponding coordinates (x a ,y a ) are numbered, recorded and stored in the robot's onboard computer. When performing indoor laser radar mapping, a small part of the outdoor area is also mapped to facilitate the calculation of indoor and outdoor navigation paths and module switching;

[0081] Then collect and map the outdoor environment information, such as Figure 3 As shown in the figure, the robot's outdoor inspection location and the intersection of the road are taken as key nodes. The robot is operated to pass through the outdoor key nodes, and the latitude and longitude information received by the robot's GNSS module is read and recorded as (x b ,y b ), in order to import the coordinate information into the robot's own / odom coordinate system, it is necessary to convert the longitude and latitude coordinates (x bi ,y b ) is converted to the Unified Transverse Mercator (UTM) coordinates (E i ,N i ), get the new coordinates, this step can be achieved through the function package in Python; store each node number and coordinate information in the robot's onboard computer;

[0082] After the above two steps, each key node contains the following information: (x ai ,y ai ,E i ,N i ,a), where x ai ,y ai is the coordinate of the robot in the 2D laser grid map, E i ,N i is the coordinate of the robot in the UTM coordinate system, a is the number of the 2D grid map. If there are multiple indoor buildings that need to be inspected, the different maps are numbered and sorted. For outdoor nodes, x ai ,y ai is set to infinity, a is set to 0; for indoor nodes, E i ,N iSet to infinity, a to the number corresponding to the grid map where it is located; for the nodes at the interface between indoor and outdoor, record the coordinates of both coordinate systems at the same time, so that the connection relationship can be quickly determined.

[0083] Topology map construction module: used to determine the connectivity between key nodes and build the corresponding topology map; combined with Figure 4 and Figure 5 , according to the actual situation of the robot's working place and the connectivity relationship between each point, construct an undirected graph G =<V,E,W> , where V is the vertex set, E is the edge set, and W is the weight. The abstract topological graph of the inspection robot working environment is obtained. Among them, the indoor key nodes (x ai ,y ai ) and (x aj ,y aj ) is: The distance between key outdoor locations is calculated based on the coordinates of two points in the UTM coordinate system (E i ,N i ) and (E f ,N j ) is used for calculation, the distance between two points outdoors is:

[0084] Inspection route planning module: After the robot receives all designated inspection locations transmitted by the client, it plans an inspection route that passes through all designated inspection locations according to the global path planning algorithm, and then the robot starts running; it specifically includes the following units:

[0085] Determine designated inspection location unit: used to classify key nodes according to whether they are designated inspection locations based on all designated inspection locations input by the client;

[0086] Find the shortest path unit: It is used to start the Dijkstra algorithm from the current starting point, search for key nodes around the current starting point and determine whether it is a designated inspection location; if so, record the distance from the current starting point to the designated inspection location; if not, continue searching until the distances from the current starting point to all designated inspection locations are recorded; then sort the distances from the current starting point to all designated inspection locations by length, and select the designated inspection location with the shortest distance to the current starting point as the new starting point;

[0087] Starting from the determined new starting point, the shortest path search unit works again;

[0088] Obtaining the final path unit: It is used to repeatedly work in the unit for determining the designated inspection location and the unit for finding the shortest path until all designated inspection locations are visited; then the shortest path from the designated inspection location visited last to the final target location is found, and all segmented paths are merged to finally obtain a shortest path from the starting point to the final target location that is close to the optimal solution;

[0089] Adjusting the robot positioning method module: used to obtain the key node information of the first designated inspection location after the robot arrives at the inspection location, receive the next inspection location as the new target location and obtain the key node information, determine whether the new target location is indoors or outdoors, adjust the robot positioning method, and then the robot passes through each designated inspection location on the planned inspection route in turn; specifically includes the following units:

[0090] Target location location information unit: used to obtain the key node information of the inspection location after the robot arrives at the first designated inspection location (x ai ,y ai ,E i ,N i ,a i ), receive the next inspection location as the new target location and obtain key node information (x aj ,y aj ,E j ,N j ,a j ), after determining whether the new target location is indoors or outdoors, adjust the robot positioning method. The specific adjustment process is:

[0091] (1) If a i -a k =0, the robot is in a single indoor or outdoor environment, the current navigation method remains unchanged, and the robot moves to the next target node;

[0092] (2) If a i -a k <0, the robot enters the room from the outdoor, converts the outdoor navigation to the indoor navigation, turns off the GNSS part of the extended Kalman filter algorithm, starts the lidar positioning algorithm, reads the corresponding 2D grid map, performs initial repositioning, determines the robot's position in the indoor 2D grid map, and then moves to the next target location:

[0093] (3) If a i -a j >0, the robot moves from indoors to outdoors, switches from indoor navigation to outdoor navigation, turns off the lidar positioning algorithm, starts the extended Kalman filter algorithm combined with GNSS information for outdoor positioning, and moves to the next target location;

[0094] Module for judging whether to execute inspection: It is used to decide whether to execute the work in a loop according to the requirements after the navigation is completed. According to the specific needs of the work, set the next action of the robot: if loop inspection is required, the robot will re-plan an inspection route from the end of the current path according to all the designated inspection locations reset by the operator this time, and repeat the above fixed-point navigation process; if charging is required, the robot can plan a path directly to the charging location and go there by itself.

[0095] Through the operation and coordination of various modules, this embodiment enables the robot to perform inspections in different indoor and outdoor environments. The robot combines GNSS with 2D lidar and switches navigation and positioning technology in a timely manner, which can avoid the problem of huge computational load of the on-board computer when using 2D lidar alone to inspect outdoor environments. It can run on a vehicle-mounted computer with lower computing power, effectively reducing the computer's computing load and operating costs.

[0096] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar, characterized by: The following steps are involved: S1: Collect indoor and outdoor environmental information and build maps, select key nodes indoors and outdoors and collect key node information; S2: Determine the connectivity between key nodes and construct the corresponding topological map; S3: The robot receives all designated inspection locations transmitted by the client, and plans an inspection route that passes through all designated inspection locations according to the global path planning algorithm, and then the robot starts running; S4: After the robot arrives at the first designated inspection location, it obtains key node information of the inspection location, receives the next inspection location as a new target location and obtains key node information, determines whether the new target location is indoors or outdoors, adjusts the robot positioning method and goes to the new target location, until the robot passes through each designated inspection location on the inspection route in turn; S5: Navigation ends, and the robot decides whether to perform inspection work again based on actual requirements.

2. The robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar according to claim 1 is characterized in that: In step S1, the specific process of mapping the indoor and outdoor environments, selecting key nodes, and collecting key node information is as follows: S101: First, collect and map the indoor environment; construct a 2D grid map of the indoor environment through a 2D laser radar, and use the SLAM algorithm in the robot operating system to construct a 2D grid map including a pgm image file and a yaml map parameter file; Select all indoor inspection locations, channel corners, entrances and exits of the robot as key nodes, and map the corresponding coordinates (x a ,y a ) are numbered, recorded and stored in the robot's onboard computer; S102: collect and map the outdoor environment; All outdoor inspection locations and road intersections of the robot are selected as key nodes. The robot's GNSS module is used to read the longitude and latitude information of the key nodes in the WGS-84 coordinate system and record them as coordinates (x b ,y b ), and (x b ,y b ) is converted to the northeast celestial coordinate, that is, the UTM coordinate under ENU (E i ,N i ), the coordinates of each key node are numbered, recorded and stored in the robot's onboard computer.

3. The robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar according to claim 2 is characterized in that: After steps S101 and S102, each key node contains the following information: (x ai ,y ai ,E i ,N i ,a), where x ai ,y ai is the coordinate of the robot in the 2D laser grid map, E i ,N i is the coordinate of the robot in the UTM coordinate system, and a is the number of the 2D grid map. If there are multiple indoor buildings that need to be inspected, use a to sort the different maps by number. For key outdoor nodes, x ai ,y ai is set to infinity, a is set to 0; for the key nodes in the room, E i ,N i Set to infinity, a is the number corresponding to the grid map where it is located; for the key nodes at the interface between indoor and outdoor, record the coordinates of the two coordinate systems at the same time; among them, the indoor key node (x ai ,y ai ) and (x aj ,y aj ) is: The distance between key outdoor locations is calculated based on the coordinates of two points in the UTM coordinate system (E i ,N i ) and (E j ,N j ) is used for calculation, the distance between two points outdoors is:

4. The robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar according to claim 1 is characterized in that: In step S2, the constructed topological map is G=<V,E,W> , where V is the vertex set, E is the edge set, and W is the weight.

5. The robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar according to claim 1 is characterized in that: In step S3, the specific process of the robot planning an inspection route passing through all designated inspection locations is as follows: S301: Classify key nodes according to whether they are designated inspection locations according to all designated inspection locations input by the client; S302: Starting from the current starting point, search for key nodes around it and determine whether it is a designated inspection location; If yes, then record the distance from the current starting point to the designated inspection location. If no, continue searching until the distances from the current starting point to all designated inspection locations are recorded. Then sort the distances from the current starting point to all designated inspection locations by length, and select the designated inspection location with the shortest distance to the current starting point as the new starting point. S303: starting from the new starting point determined in step S302, re-execute step S302; S304: Repeat S302-S303 until all designated inspection locations are visited; then find the shortest path from the last designated inspection location visited to the final target location, merge all segmented paths, and finally obtain a shortest path from the starting point to the final target location that is approximately the optimal solution.

6. The robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar according to claim 5 is characterized in that: The step S3 also includes a process in which the robot calculates the shortest path for different working environments. The specific process is: if the robot works only indoors or only outdoors, then according to the formula or Normal calculation; if the robot works in both indoor and outdoor environments, add outdoor designated points at the entrance and exit of the building to calculate the shortest paths indoors and outdoors respectively; If the robot needs to work in multiple indoor environments, add designated points at the entrances and exits of the building, calculate the outdoor path first, obtain the shortest outdoor path, and then inspect the building entrances and exits in the order of the shortest path.

7. The robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar according to claim 1 is characterized in that: In step S4, the process of determining whether the new target location is indoors or outdoors is as follows: when the robot reaches a target node (x ai ,y ai ,E i ,N i ,a i ), the next designated inspection location in the inspection route is taken as the new target node, and the key node information (x aj ,y aj ,E j ,N j ,a j ) to the computer, If a i -a j =0, the robot is in a single indoor or outdoor environment, the current navigation method remains unchanged, and the robot moves to the next target node; If a i -a j <0, the robot moves from the outdoors to the indoors, turns off the GNSS module, and starts the lidar positioning algorithm for indoor positioning; If a i -a j >0, the robot moves from indoors to outdoors, turns off the lidar positioning algorithm, and starts the GNSS module for outdoor positioning.

8. The robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar according to claim 7 is characterized in that: In step S4, when the robot is working on the planned inspection route, the laser radar is activated as a sensor for local obstacle avoidance.

9. The robot indoor and outdoor fixed-point inspection method based on GNSS and 2D laser radar according to claim 1 is characterized in that: In step S5, after the navigation is completed, the robot stops patrolling; if the robot needs to patrol again, the robot will re-plan a global path from the end point of the current path and start patrolling; if the robot needs to be charged, the robot will plan a path directly to the charging location and go there on its own.

10. A robot indoor and outdoor fixed-point inspection system based on GNSS and 2D laser radar, characterized in that: Includes the following modules: Mapping module: used to collect indoor and outdoor environmental information and build maps, select key nodes indoors and outdoors and collect key node information; Topology map construction module: used to determine the connectivity between key nodes and construct the corresponding topology map; Inspection route planning module: After the robot receives all designated inspection locations transmitted by the client, it plans an inspection route that passes through all designated inspection locations according to the global path planning algorithm, and then the robot starts running; Adjust the robot positioning method module: used to obtain the key node information of the first designated inspection location after the robot arrives at the inspection location, receive the next inspection location as the new target location and obtain the key node information, determine whether the new target location is indoors or outdoors, adjust the robot positioning method and go to the new target location, until the robot passes through each designated inspection location on the planned inspection route in turn; Module for judging whether to execute inspection: It is used to decide whether to execute inspection again according to actual requirements at the end of navigation.

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