CN109916393B - Multi-grid-value navigation method based on robot pose and application thereof - Google Patents
Multi-grid-value navigation method based on robot pose and application thereof Download PDFInfo
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Abstract
The invention discloses a multi-grid-value navigation method based on robot poses and application thereof, and belongs to the technical field of robot navigation. The multi-grid-value navigation method based on the robot pose enlarges the passing area of the robot in the grid map and enhances the connectivity of the topological map. Meanwhile, in the BIM information-based indoor wall surface construction autonomous mobile robot navigation method, working points are calculated based on the extracted BIM information, and then local navigation routes between adjacent working points are set according to the position information and the working task sequence of the working points and a robot pose-based multi-grid value navigation method; and positioning and detecting obstacle information around the autonomous mobile robot for indoor wall construction in real time, updating the grid map, and resetting a local navigation route between adjacent working points based on the updated grid map. The implementation of the invention improves the navigation working efficiency and accuracy.
Description
Technical Field
The invention relates to the technical field of robot navigation, in particular to a multi-grid-value navigation technology based on robot poses.
Background
With the development of computer technology, the technology of robot SLAM (singular Localization And Mapping) has matured, and commonly used technologies include VSLAM (Visual singular Localization And Mapping) based on computer vision And SLAM based on multi-sensor fusion of laser radar. The technologies are applied to various fields, such as sweeping robots, outer space exploration robots and the like.
The map constructed by the traditional 2D laser SLAM technology is a grid map, and the grid map only has scale information but not semantic information. So that it is difficult to better guide the robot to work.
In the common grid map-based robot navigation, the robot can hardly safely reach circumscribed obstacle areas and non-free space. Further described are: in a conventional grid map, each grid has a grid value, as shown in fig. 3. This grid value is related to the map environment and the physical dimensions of the robot. When the grid value is 254, the obstacle is a fatal obstacle, and the obstacle coincides with the center of the robot, so that the robot inevitably collides with the obstacle. The grid value 253 represents an inscribed obstacle, the obstacle is positioned in an inscribed circle of the robot outline at this time, and the grid values 252 to 128 represent circumscribed obstacles, so that the robot and the obstacle are in close contact at this time and are not always collided. The non-free space is 128-0, and is a buffer space from the robot outer circumscribe obstacle to the free area. The free zone is 0 and the robot can run freely, and the unknown zone is 255 and is represented as the detected zone. Conventional grid map based navigation should try to avoid the robot entering into non-free space and not entering into circumscribed obstacles.
In addition, in practical engineering application, sometimes the robot must enter a circumscribed obstacle area to complete corresponding work. The practical example is that the building plastering robot only leaves the wall for 2 cm when plastering, and the robot enters the circumscribed obstacle area, namely the navigation problem of the autonomous mobile robot for indoor wall construction cannot be solved in the prior art.
Disclosure of Invention
The invention aims to: in order to solve the existing problems, the multi-grid-value navigation method based on the robot pose is characterized by comprising the following steps of:
in the grid map, besides grid value information is stored for each grid, an array which represents whether the robot collides in different angle states is additionally defined;
preprocessing the grid map: marking whether the collision identifiers of all free areas in the grid map are non-collision identifiers, and setting whether the collision identifiers of the fatal obstacles and the internal tangent obstacles in the grid map are collision identifiers;
respectively judging whether each vertex of the robot falls on a grid of the fatal obstacle or not based on the angles x of the robot at different poses, and if all the vertices do not fall on the grid of the fatal obstacle, setting whether a collision identifier corresponding to the angle x is a non-collision identifier or not; otherwise, setting whether the collision identifier is the collision identifier; obtaining a plurality of reachability maps related to the angles of the robot, and recording the reachability maps as reachability maps Map [ n ], wherein n is a corresponding angle distinguisher;
setting a local path from the point A to the point B based on the current starting point A and the target point B of the robot:
map0 represents a Map composed of obstacles and free areas without limiting the angle of the robot;
if the point A is in the free area, the point B is in the free area, and an reachable path can be found in the Map0 from the point A to the point B, the navigation is carried out in a non-limited angle change mode; if the reachable path cannot be found in the Map0, traversing and searching whether the reachable path from the point A to the point B exists in each reachability Map [ n ], wherein each reachability Map [ n ] only corresponds to one reachable path; searching an accessibility map corresponding to the reachable path of the shortest path from all reachable paths obtained by searching, taking the corresponding angle as the navigation angle of the robot, and navigating to the point B by limiting the angle of the robot;
if the point A is in a free area and the point B is in a non-free area, based on the position of the point B, taking the reachability Map with the collision identifier as the non-collision identifier in all the reachability maps Map [ n ], namely, based on the collision identifier of the point B being the non-collision identifier, adding the corresponding Map into a sub-set of the path to be searched; traversing whether an reachable path from the point A to the point B exists in each reachability map in the first reachability map subset; searching an accessibility map corresponding to the reachable path of the shortest path from all reachable paths obtained by searching, taking the corresponding angle as the navigation angle of the robot, and navigating to the point B by limiting the angle of the robot;
if the point A is in a non-free area and the current robot angle is R, searching a reachability Map [ R ] corresponding to the angle R, judging whether a reachable path exists in the reachability Map [ R ], and if so, navigating to the point B in a mode of limiting the robot angle to be R; otherwise, the path from the point a to the point B is considered to be unreachable, that is, the local path planning from the point a to the point B fails.
The multi-grid-value navigation method based on the robot pose enlarges the passing area of the robot in the grid map and enhances the connectivity of the topological map.
Meanwhile, the invention also provides an indoor wall construction autonomous mobile robot navigation method based on the BIM (Building Information Modeling) Information. The invention extracts and utilizes the BIM information for robot navigation, so as to enhance the accuracy of the navigation and positioning of the robot in the scene of building construction, optimize the working sequence of the robot and improve the cooperative working capability of the robot. The technical scheme adopted by the method is as follows:
extracting BIM information of an indoor environment of indoor wall construction autonomous mobile robot operation, constructing an indoor map of the operation, calculating a work task of each indoor wall construction operation based on the indoor map, and obtaining position information of a work point of each work task; setting a work task sequence for all work tasks;
according to the position information of the working points and the sequence of the working tasks, the local navigation route between the adjacent working points is set based on the multi-grid-value navigation method based on the pose of the robot;
and positioning and detecting the information of obstacles around the autonomous mobile robot for indoor wall construction in real time, updating a grid map, and setting a local navigation route between adjacent working points based on the updated grid map and the multi-grid-value navigation method based on the pose of the robot.
Furthermore, construction cavities and unknown ground obstacle information which can be detected by the depth camera can be used as obstacle information around the indoor wall construction autonomous mobile robot.
Compared with the prior art, the invention has the following beneficial effects:
(1) The multi-grid-value navigation method based on the robot pose enlarges the passing area of the robot in the grid map and enhances the connectivity of the topological map;
(2) BIM information is applied to robot navigation for the first time, and the problems that construction part information is difficult to acquire by a conventional navigation technology in a building construction environment and the like are solved. And an optimal working path can be calculated according to the BIM information, so that the working efficiency of the robot is improved.
(3) The invention utilizes ultrasonic waves and RGB-D depth cameras to detect unknown dangers in the environment and ensure the man-machine safety.
Drawings
Fig. 1 is an overall architecture diagram of an indoor wall construction autonomous mobile robot navigation system based on BIM information according to the present invention.
FIG. 2 is a flow chart of the indoor wall construction autonomous mobile robot navigation system based on BIM information.
Fig. 3 is a schematic diagram of a grid map value-taking principle.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following embodiments and accompanying drawings.
The BIM information-based indoor wall construction autonomous mobile robot navigation method extracts the BIM information based on the preset plug-in for extracting the BIM information, calculates the working point through the BIM information, and plans the optimal path. And the map generated through the BIM information is displayed to the remote control terminal in real time, so that the man-machine interaction is facilitated. The robot realizes positioning through a laser radar and an IMU, detects surrounding obstacles through ultrasonic waves, and plans a route again in time when finding the obstacles. And moreover, a temporary excavated construction cavity which is not contained in the BIM information is detected by using an RGB-D depth camera, so that dangers are avoided. The robot cluster synchronizes the working state information of the robot to the master control terminal, and the master control terminal reasonably distributes tasks. By adopting the invention, the use of the construction robot can be simplified, the operation difficulty of a user is reduced, the working task of the robot can be automatically calculated, and the navigation working efficiency is improved.
During specific implementation, the navigation system corresponding to the system comprises a laser radar, an IMU (inertial measurement Unit), an RGB-D (Red Green blue-D) depth information camera, a 4-wheel robot moving platform (a robot for short), an industrial personal computer (a system of an industrial control computer), a hardware system of a master control terminal, and specifically executes the following operations:
firstly, map information of a construction environment and information such as characteristic attributes (such as doors, windows, columns, wall surfaces and the like), size and position of a component to be constructed are obtained based on a preset plug-in for extracting BIM information. And performing the following processing on the extracted BIM information:
and displaying the map information of the construction environment, the position and the working state of the robot in the working environment in real time. And the robot can be monitored in real time through a remote terminal. The remote terminal may be a PC, a mobile device, a VR (Virtual Reality) device, or the like.
The industrial personal computer calculates specific tasks of each job (indoor wall construction operation) according to the BIM information, sets each corresponding work sequence, and sets an optimized work sequence according to preset rules.
And secondly, acquiring real-time information of the working environment through a laser radar, sending the real-time information to an industrial personal computer, and realizing robot positioning through the industrial personal computer. The specific implementation process can be as follows:
displaying map information acquired by a laser radar in real time, and setting a navigation path of the robot according to the map information acquired by the laser radar, wherein the navigation path setting method can adopt any conventional mode, and generally can consider a navigation mode with obstacle avoidance processing;
the robot synchronizes the current position coordinate information and the working state information to the master control terminal in real time, and the master control terminal coordinates the working tasks in a unified manner;
and displaying the construction cavity detected by the RGB-D depth camera and the unknown ground obstacle on the remote terminal in real time.
And secondly, a method of combining a laser radar sensor and an IMU sensor is adopted, so that the positioning precision of the robot and the reliability of real-time mapping are improved. Wherein the IMU includes inertial navigation information, geomagnetic sensor information, and the like. The specific implementation process is as follows:
and the industrial personal computer calculates the received robot position information and BIM information according to the SLAM mapping information, plans an optimal navigation path for the robot based on a preset navigation path planning mode and feeds back a corresponding movement instruction to the robot. The optimal path is the most efficient path in a plurality of paths capable of completing construction tasks.
And the robot performs autonomous navigation according to the movement instruction sent by the industrial personal computer.
Then, ground obstacles and excavated construction holes which are not contained in the BIM are detected by using an RGB-D depth camera, so that danger is avoided.
Then, the indoor environment is classified by using the BIM information, a grid map is constructed by using the BIM information, and a building information semantic tag is given to each grid in the grid map. And updating map data in real time by using the laser radar, and generating a semantic topological map on the basis of the grid map to provide a basis for the navigation of the robot.
The semantic topological map is used for global path planning, and the grid map is used for local path planning. Because there are many topology nodes in a large-scale construction scene, in this embodiment, an ant colony algorithm with better performance is used to find a better route.
In the specific implementation of the local path planning, the invention also provides an improved navigation method for enabling the robot to safely reach the non-free space grid and the circumscribed obstacle grid. A multi-grid-value navigation method based on robot poses enables a robot capable of freely translating in x and y directions to safely enter a non-free space grid and an outer tangent obstacle grid. The specific implementation process is as follows;
in the grid map, each grid stores grid value information, and additionally defines an array A [ N ] with the size of N, wherein each element A [ x ] of the array represents a collision identifier of whether the robot collides with an obstacle under the state of an angle x, and in the specific embodiment, the collision is represented by '-1'; "1" means no collision. Compared with the common grid map which is a two-dimensional array and the data stored in each map node is a grid value, each node of the improved grid map not only stores one grid value, but also stores a one-dimensional array A [ x ] which represents whether the corresponding node collides under an angle x. The method is equivalent to storing 1+N maps into a two-dimensional array, wherein the maps comprise a common grid map with a robot capable of freely moving and N maps with limited angles.
And then preprocessing the grid map, firstly setting collision identifiers (A values) of all free areas to be 1, and setting the fatal obstacle and the internal tangent obstacle to be-1. The circumscribed obstacles and the grid of non-free space are traversed and reachability determinations are made thereon. If a [ x ] =1, it means that the robot does not collide with the obstacle in the current grid at the x angle. And-1 indicates a collision with it. The specific judging method comprises the following steps:
(1) The number of the vertexes of the robot is represented by M, the center position is represented by W0 (x 0, y 0), and the positions of the other vertexes are respectively W1 (x 1, y 1) … WM (xM, yM);
when the robot angle is x, respectively judging whether each vertex falls on a grid of the fatal obstacle, if all the vertexes do not fall on the grids of the fatal obstacle, A [ x ] =1, otherwise, A [ x ] = -1; thus, N reachability maps Map [1] and Map [2] … Map [ N ] related to the angle of the robot are generated, and the robot can find the reachable path through the reachability maps.
(2) The specific steps of the robot from the point A to the point B for local path planning are as follows:
map0 represents a Map composed of obstacles and free areas without limiting the angle of the robot; and the Map limiting the robot angle is Map [ N ], where N =1,2, … …, N;
if the point A is in the free area, the point B is in the free area, and a reachable path can be found from the point A to the point B in the Map0, the navigation is carried out in a mode without limiting the angle change. If not, then traverse the search Map N]Traversing and searching whether reachable paths from the point A to the point B exist in each reachability map or not (namely, only one reachable path exists in each reachability map, which is also called as an optimal reachable path), and obtaining a reachable path set { P) based on the reachable paths obtained through searching 1 ，…,P K K represents the number of reachable paths from point a to point B included in the N reachability maps; then the reachable path with the shortest path (which can be defined as P) min ) The corresponding reachability Map (which may be defined as Map min)]) And the corresponding angle (namely min) is used as the navigation angle of the robot, and the navigation is carried out to the point B by limiting the angle of the robot.
If the point A is in a free area and the point B is in a non-free area, searching a reachability Map subset of the point B, wherein the point A [ x ] =1, and searching whether an reachable path from the point A to the point B exists in each reachability Map in the Map subset in a traversing manner, searching a reachability Map corresponding to a reachable path of the shortest path from all reachable paths obtained by searching, and marking the reachability Map as Map [ min ], and then taking an angle min corresponding to the reachability Map as a navigation angle of the robot, and navigating to the point B in a manner of limiting the robot angle.
If the point A is in a non-free area and the current robot angle is R, searching a Map [ R ], judging whether the Map is reachable (namely, whether a reachable path exists), and if so, navigating to the point B by limiting the robot angle to be R; otherwise point B is not reachable.
Examples
In the embodiment, a distributed robot operation system is adopted, data among different robot nodes are shared through the system, the experimental environment is a semi-finished house which is actually built, and plastering construction operation is carried out on the semi-finished house by the robot.
In order to better acquire environmental information of a construction site, a 2D Lidar is installed on the top of a robot (1.8 m high). In order to ensure the safety in the construction process, ultrasonic sensors are arranged around the construction machine to detect other obstacles such as people around, and the route is re-planned when the obstacles meet other obstacles. Through earth magnetism sensor, acquire the dolly angle in real time, when taking place strong magnetic interference, stop earth magnetism sensor data to do the correction of zeroing. And when the accumulated error is not large, positioning the robot by using the robot odometer and the inertial navigation odometer. And when the accumulated error exceeds the SLAM system precision of the 2D Lidar, the 2D Lidar based SLAM system and a closed loop detection algorithm are utilized to reposition the accumulated error, and the accumulated error is eliminated. When the working point is reached, the accumulated error can be further eliminated because the robot needs to perform further accurate positioning.
The RGB-D depth camera is used for detecting indoor ground obstacles and temporarily excavated construction holes, so that dangers are avoided.
Referring to fig. 1, in this embodiment, a navigation system for implementing the navigation of the autonomous mobile robot for indoor wall construction of the present invention includes 5 modules;
the human-computer interaction module is mainly used for parameter setting, work target selection and real-time display of a working state of the robot, and is a main module for interaction between an operator and the robot and giving instructions to the robot;
the mapping module is used for constructing a corresponding operation environment map based on the extracted BIM information; the system comprises a building information extraction and map construction module, an angular point feature extraction and closed loop detection module;
the path planning module is used for controlling the robot to plan an optimal path according to instructions given by operators; the method comprises three submodules, namely, task calculation of a working point and the working point, optimal path planning and new task path receiving and replanning;
the navigation module is used for positioning in real time to ensure that the robot is on a correct route and issuing a correct instruction to a mobile platform of the robot; the system comprises a submodule for estimating an initial state pose, calculating the pose of the robot in real time and synchronizing data, wherein the synchronizing data is used for realizing data synchronization of a local terminal and a remote terminal;
in the obstacle avoidance and safety system, the robot can ensure the safety of human and machine. The method comprises ultrasonic obstacle avoidance, system restart and recovery and depth camera detection (detection depth information).
Specifically, in the embodiment, in the mapping, path planning, and navigation modules, the specific implementation is developed based on an ROS (Robot Operating System) framework. And in the aspect of trolley control, communication control is realized through an RS232 serial port. Under an ROS framework, a speed instruction is controlled to be linear and used for controlling the linear speed in the XYZ direction, and the unit is m/s; the velocity command controls angular for controlling angular velocity in XYZ direction, in rad/s. The position represents the current position coordinates of the robot, including XYZ three-axis positions and orientation parameters of the robot, and a covariance matrix for correcting errors. twist is the current motion state of the robot, including linear and angular velocities of XYZ three axes, and a covariance matrix for correcting errors. The ROS framework communicates through subscribing and publishing topics, which include: tf-is used for the transformation between the coordinate system of the laser radar, the coordinate system of the base and the coordinate system of the odometer. scan-data of lidar scans. The published topics include: map _ metadata-publishing map metadata. map-publishing map raster data. entrypy-an estimate of the robot pose distribution entropy.
Referring to fig. 2, a flow chart of the robot navigation process according to the present invention is shown. The control terminal of the invention adopts a cross-platform form and comprises a mobile terminal and a PC terminal. The position information of the trolley, the real-time map information and the like are transmitted to the man-machine interaction module through the TCP in a light data exchange format Json format and are used for displaying the working state of the robot in real time.
And in the map construction aspect, the map is divided into a BIM information map and a real-time environment map constructed by a laser radar SLAM.
Firstly, a plug-in capable of extracting BIM information is preset, and the extracted information is used as a basis for constructing a map. The method comprises the steps of determining working points according to building information and practical requirements of building construction, and calculating specific working tasks and working time of each working point.
A real-time environment map constructed by the laser radar SLAM is a SLAM algorithm based on a particle filter, the actual probability distribution is approximated by appointing a random sample which is spread in a state vector space, and the weighted average of the samples is used for replacing integral operation, so that the minimum variance estimation of the system state is realized.
The grid map information is stored in a two-dimensional array comprising nodes, the information of the nodes comprising: the grid value, the array a [ N ], a [ N ] =1 indicates that the robot will collide with an obstacle at angle N, -1 indicates not.
And then preprocessing the grid map: the A values of all free areas are set to be 1, and the fatal obstacle and the internal cutting obstacle are set to be-1.
Traversing the circumscribed obstacle and the grid of non-free space, and making a reachability determination thereon:
when the robot angle is x, respectively judging whether each vertex of the robot falls on a grid of the fatal obstacle, if all vertexes do not fall on the grids of the fatal obstacle, A [ x ] =1, otherwise, A [ x ] = -1; thus, N reachability maps Map [1], map [2] … Map [ N ] related to the angles of the robot are generated, and the robot can find the reachable paths through the reachability maps.
And (3) local path planning processing of the robot from the point A to the point B:
map0 represents a Map composed of obstacles and free areas without limiting the angle of the robot; and the Map limiting the robot angle is Map [ N ], where N =1,2, … …, N;
planning a local path between a starting point and a target point based on the position information of the starting point (A) and the target point (B) of the robot:
if the point A is in the free area, the point B is in the free area, and a reachable path can be found from the point A to the point B in the Map0, the navigation is carried out in a mode without limiting the angle change. If not, traversing and searching Map [ N ], traversing and searching whether an reachable path from the point A to the point B exists in each reachable Map, searching a reachable Map corresponding to the reachable path of the shortest path from all reachable paths obtained through searching, recording the reachable Map as Map [ min ], then speaking an angle min corresponding to the reachable Map as a navigation angle of the robot, and navigating to the point B in a mode of limiting the angle of the robot.
If the point A is in a free area and the point B is in a non-free area, searching a reachability Map subset of the point B, wherein the point A [ x ] =1, traversing and searching whether an reachable path from the point A to the point B exists in each reachability Map in the Map subset, searching a reachability Map corresponding to a reachable path of the shortest path from all reachable paths obtained through searching, marking the reachability Map as Map [ min ], and then taking an angle min corresponding to the reachability Map as a navigation angle of the robot to navigate to the point B in a manner of limiting the robot angle.
If the point A is in a non-free area and the current robot angle is R, searching a Map [ R ], judging whether the Map is reachable, and if the Map is reachable, navigating to the point B by limiting the robot angle to be R; otherwise, it indicates that the path between the two points is not reachable.
Compared with the prior art, the invention has the following beneficial effects:
(1) The multi-grid-value navigation method based on the robot pose enlarges the passing area of the robot in the grid map and enhances the connectivity of the topological map;
(2) BIM information is applied to robot navigation for the first time, and the problems that construction part information is difficult to acquire by a conventional navigation technology in a building construction environment and the like are solved. And an optimal working path can be calculated according to the BIM information, so that the working efficiency of the robot is improved.
(3) The robot adopts a distributed system, tasks can be reasonably distributed to the robot cluster according to the BIM information, the robot can also synchronize the working state of the robot to the master control terminal, and the master control uniformly coordinates the working tasks, so that the conflict is avoided, and the efficiency is improved.
(4) The invention utilizes ultrasonic waves and RGB-D depth cameras to detect unknown dangers in the environment and ensure the man-machine safety.
(5) The invention can generate the grid map containing the semantic label of the building information to guide the building construction, and generates the semantic topological map on the basis of the grid map, thereby improving the efficiency of path planning. Whereas conventional lidar SLAMs can only generate ordinary grid maps that do not contain semantic tags.
While the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.
Claims (3)
1. A multi-grid value navigation method based on robot poses is characterized by comprising the following steps:
in the grid map, besides grid value information is stored for each grid, an array which represents whether the robot collides in different angle states is additionally defined;
preprocessing the grid map: marking whether the collision identifiers of all free areas in the grid map are non-collision identifiers, and setting whether the collision identifiers of the fatal obstacles and the internal tangent obstacles in the grid map are collision identifiers;
respectively judging whether each vertex of the robot falls on a grid of the fatal obstacle or not based on the angles x of the robot at different poses, and if all the vertices do not fall on the grid of the fatal obstacle, setting whether a collision identifier corresponding to the angle x is a non-collision identifier or not; otherwise, setting whether the collision identifier is the collision identifier; obtaining a plurality of reachability maps related to the angles of the robot, and recording the reachability maps as reachability maps Map [ n ], wherein n is a corresponding angle distinguisher;
setting a local path from the point A to the point B based on the current starting point A and the target point B of the robot:
map0 represents a Map composed of obstacles and a free area, which does not limit the angle of the robot;
if the point A is in the free area, the point B is in the free area, and an reachable path can be found in the Map0 from the point A to the point B, the navigation is carried out in a non-limited angle change mode; if the reachable path can not be found in the Map0, traversing and searching whether the reachable path from the point A to the point B exists in each reachable Map [ n ], wherein each reachable Map [ n ] only corresponds to one reachable path; searching an accessibility map corresponding to the reachable path of the shortest path from all reachable paths obtained by searching, taking the corresponding angle as the navigation angle of the robot, and navigating to the point B by limiting the angle of the robot;
if the point A is in a free area and the point B is in a non-free area, taking the reachability Map with the collision identifier as the non-collision identifier in all the reachability maps Map [ n ] as a first reachability Map subset based on the position of the point B; traversing whether a reachable path from the point A to the point B exists in each reachability map in the first reachability map subset; searching an accessibility map corresponding to the reachable path of the shortest path from all reachable paths obtained by searching, taking the corresponding angle as the navigation angle of the robot, and navigating to the point B by limiting the angle of the robot;
if the point A is in a non-free area and the current robot angle is R, searching a reachability Map [ R ] corresponding to the angle R, judging whether a reachable path exists in the reachability Map [ R ], and if so, navigating to the point B in a mode of limiting the robot angle to be R; otherwise, the path from point a to point B is considered unreachable.
2. The BIM information-based indoor wall surface construction autonomous mobile robot navigation method is characterized by comprising the following steps:
extracting BIM information of an indoor environment of indoor wall construction autonomous mobile robot operation, constructing an indoor map of the operation, calculating a work task of each indoor wall construction operation based on the indoor map, and obtaining position information of a work point of each work task; setting a work task sequence for all work tasks;
according to the position information and the work task sequence of the working points and the multi-grid-value navigation method based on the robot pose as claimed in claim 1, setting local navigation routes between adjacent working points;
the method comprises the steps of positioning and detecting obstacle information around the autonomous mobile robot for indoor wall construction in real time, updating a grid map, and setting a local navigation route between adjacent working points according to the multi-grid-value navigation method based on the pose of the robot in claim 1 again based on the updated grid map.
3. The method of claim 2, wherein the construction cavity detected by the depth camera and the unknown obstacle information of the ground are used as the obstacle information around the autonomous mobile robot for indoor wall construction.
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