CN115469662A - Environment exploration method, device and application - Google Patents
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Abstract
The invention relates to an environment exploration method, which comprises the steps of obtaining environment information, generating a generalized voronoi diagram and constructing a GVD node set; selecting nodes from the GVD node set to construct a heuristic boundary point set and fusing the heuristic boundary point set, eliminating redundant nodes and generating a fused heuristic boundary point set; fusing a GVD node set, removing redundant nodes and constructing a GVD characteristic node set; solving a GVD feature node set, a fused heuristic boundary point set and a current pose of the robot to obtain a GVD feature node full set; optionally selecting two characteristic nodes in the GVD characteristic node complete set for collision detection to obtain a characteristic matrix with side information; calculating the income function value of each node in the fused boundary point set according to the information gain and the characteristic matrix; selecting a target node according to the income function value, navigating to the target node, and updating map data; and repeating the exploration until the whole environment exploration is completed. By fusing boundary points and eliminating redundant nodes, the calculation consumption of the robot during boundary decision is reduced, backtracking is reduced, and the exploration efficiency is improved.
Description
Technical Field
The invention relates to the technical field of robot path planning, in particular to an environment exploration method, an environment exploration device and application.
Background
The rapid development of computer technology and artificial intelligence technology brings a great breakthrough to the research of mobile robots, and the market scale of service robots in China increases at a high speed. The autonomous exploration technology of the mobile robot is a key technology in the field of mobile robots, and is widely applied to the fields of tunnel exploration, underwater detection, robot search and rescue and the like. The autonomous exploration of the mobile robot requires that the robot obtains unknown environment information as much as possible by moving the robot per se within a limited time so as to establish a complete environment map. Since the border is a boundary for distinguishing the searched area from the unknown area and the vicinity thereof has more uncertain environment information, a search strategy based on the border is favored by a wide range of students. The boundary-based exploration strategy can be briefly summarized as the following process: the robot detects a boundary in a map according to map data updated by a sensor of the robot, then generates a target point near the boundary, selects the target point with the highest exploration value as the optimal target point by combining an evaluation function, finally controls the robot to move to the optimal target point by utilizing a motion control module, and updates an environment map until the exploration is finished.
However, as the complexity of the environment increases, this method of acquiring boundaries based on image processing increases dramatically in time and space complexity, making autonomous exploration by mobile robots inefficient. The random sampling based algorithm can avoid the construction of a complex space, so that Umari and the like utilize the RRT algorithm to acquire boundary points, namely, two rapid search random trees RRT are grown in an idle area of a map to quickly acquire the boundary points. However, due to the existence of the trap space, the random sampling algorithm based on the RRT is difficult to quickly acquire boundary point information in narrow corridors, labyrinths and other scenes, so that the robot is trapped in place, and even the autonomous exploration fails; secondly, the robot turns to another area without the completion of the exploration of the current area due to the slow extraction of the boundary points, so that the backtracking phenomenon is caused; the random sampling method has greater redundancy for the extraction of boundary points, and the redundant boundary points increase the computational complexity of the mobile robot when selecting an optimal target point, so that the robot has a time-consuming problem during motion decision; finally, the robot adopts the Euclidean distance to calculate the path cost when selecting the exploration target point, neglects the environmental characteristics, and easily causes inaccurate evaluation of the optimal target point, thereby generating a backtracking phenomenon.
Therefore, in the existing autonomous exploration method for the mobile robot, a clustering algorithm is used for clustering boundary points to remove redundant points, the extraction of the boundary points is slow, and large redundancy still exists, so that the robot is slow in exploration and low in efficiency.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the problem of high redundancy in the process of extracting the boundary points in the prior art.
In order to solve the above technical problems, the present invention provides an environment exploration method, comprising:
s1, acquiring environmental information of a current pose of a robot, constructing map data and repositioning the current pose of the robot;
s2, generating a generalized Voronoi diagram according to the environment information, and extracting all boundary points of the generalized Voronoi diagram to construct a GVD node set;
s3, selecting GVD nodes with the unknown grid number exceeding a preset threshold value in the GVD node radius in the GVD node set, and constructing a heuristic boundary point set;
s4, selecting the GVD node with the largest radius in the heuristic boundary point set, adding the GVD node with the largest radius in the heuristic boundary point set into the heuristic boundary point set after fusion, and deleting the GVD node with the largest radius and other GVD nodes in the radius range in the heuristic boundary point set; repeating the steps until the heuristic boundary point set is empty to obtain a fused heuristic boundary point set;
s5, selecting a GVD node with the largest radius from the GVD node set, adding the GVD node into a GVD feature node set, and deleting the GVD node with the largest radius and other nodes within the radius range in the GVD node set; repeating the steps until the GVD node set is empty to obtain a GVD characteristic node set;
s6, merging the GVD feature node set, the current pose of the robot and the fused heuristic boundary point set to obtain a GVD feature node full set;
s7, randomly selecting two GVD feature nodes from the GVD feature node complete set for collision detection, designing a gain function, selecting a target node from the fused heuristic boundary point set according to a gain function value, navigating to the target node, and updating map data and the current pose of the robot;
and S8, repeating the steps from S1 to S7 until the whole environment exploration is completed.
In an embodiment of the present invention, the acquiring the environment information, constructing the map data, and locating the current pose of the robot includes:
acquiring laser data by using a sensor carried by the robot to acquire surrounding environment information;
receiving laser data acquired by the sensor by using a SLAM module, converting the laser data into map data, and constructing a grid map;
and correcting and repositioning the current pose of the robot by using the grid map constructed by the SLAM module.
In an embodiment of the present invention, the generating a generalized voronoi diagram according to the environment information, and extracting all boundary points of the generalized voronoi diagram to construct a GVD node set includes:
acquiring the map data issued by the SLAM module to generate a generalized Voronoi diagram, wherein the map data comprises an unknown area, a free area and an obstacle area;
extracting all boundary points of the generalized voronoi diagram as GVD nodes, and constructing a GVD node set;
wherein the GVD node is denoted as G i ＝(x i ，y i ，r i )，x i ，y i Denotes the position of the GVD node, r i Represents the radius of the GVD node, i.e., the distance of the GVD node to the nearest obstacle.
In an embodiment of the present invention, the constructing a heuristic boundary point set of GVD nodes whose unknown grid number exceeds a preset threshold in the GVD node radius in the GVD node set further includes:
and discarding GVD nodes of which the number of unknown grids does not exceed a preset threshold in the GVD node set GVD node radius range.
In an embodiment of the present invention, the randomly selecting two GVD feature nodes from the GVD feature node corpus to perform collision detection, designing a gain function, selecting a target node from the fused heuristic boundary point set according to the gain function value, navigating to the target node, and updating map data and the current pose of the robot includes:
randomly selecting two GVD feature nodes from the GVD feature node complete set for collision detection, traversing all GVD feature nodes, and generating a feature matrix with side information;
calculating the path cost from the current pose of the robot to each GVD feature node according to the feature matrix with the side information;
designing a gain function, calculating a gain function value of the GVD node in the fused heuristic boundary point set, and selecting a target node according to the gain function value;
and navigating the target nodes, updating the coordinates of the target nodes to be the current pose of the robot and updating map data.
In an embodiment of the present invention, the randomly selecting two GVD feature nodes from the GVD feature node corpus to perform collision detection, traversing all GVD feature nodes, and generating a feature matrix with side information includes:
randomly selecting two GVD feature nodes in the GVD feature node complete set for collision detection;
if the collision detection is passed, generating an edge connecting the two GVD characteristic nodes, and calculating the Euclidean distance of the two GVD characteristic nodes as the length of the edge;
if the collision detection is not passed, recording the length of an edge connecting the two GVD characteristic nodes as infinity;
and traversing all GVD feature nodes in the GVD feature node complete set to generate a feature matrix with side information.
In one embodiment of the present invention, the revenue function is R1 f ＝w 1 *I f -w 2 *N f ；
Wherein, I f The information gain of the target node represents the number of unknown grids in the range of the information gain radius of the target node being 3; n is a radical of f The path cost is obtained by traversing the feature matrix with the side information by utilizing a Dijkstra algorithm and represents the actual path cost of the current pose of the robot and the target node; w is a 1 And w 2 Is a custom weight.
In one embodiment of the present invention, said navigating to said target node comprises:
drawing a path from the current pose of the robot to the target node by using an A-global path planning algorithm;
and (4) combining with a DWA local path planning algorithm to finish obstacle avoidance and guide the robot to navigate to the target node.
The invention also provides an environment exploring apparatus comprising:
the information acquisition module is used for acquiring environmental information;
the map data updating module comprises an SLAM module and is used for updating map data according to the environmental information acquired by the information acquisition module;
the heuristic boundary point extraction and fusion module is used for extracting heuristic boundary points and fusing the heuristic boundary points to generate a fused heuristic boundary point set;
the GVD feature node extraction and fusion module is used for extracting GVD feature nodes and generating a global GVD feature node set by combining the current pose of the robot and the fused heuristic boundary point set;
the collision detection module is used for performing collision detection on the GVD nodes in the GVD characteristic node complete set to generate a characteristic matrix with side information and selecting a target node according to the characteristic matrix with the side information and a revenue function;
and the path generation module is used for planning a path from the current pose of the robot to the target node by using an A-x global path planning algorithm and a DWA local path planning algorithm.
The invention also provides an application of the environment exploring device in the field of tunnel exploration.
Compared with the prior art, the technical scheme of the invention has the following advantages:
the environment exploration method provided by the invention has the advantages that the boundary points with the unknown grid number exceeding the preset threshold value in the radius range are selected from the boundary point set of the generalized Weino graph, the heuristic boundary point set is constructed and fused, redundant nodes with less boundary information are deleted, the extraction speed of the boundary points is accelerated while the heuristic boundary point set is ensured to contain the boundary points of each area, the redundancy is reduced, and the calculation consumption of a robot in the boundary point decision making process is further reduced; by utilizing the designed gain function, the gain function value of each GVD node from the robot to the heuristic boundary point set after fusion is calculated according to the information gain and the path cost, the GVD node with the best gain function value is selected as the target node, the backtracking phenomenon caused by inaccurate evaluation and selection of the optimal boundary point is reduced, and the robot environment exploration efficiency is improved.
Drawings
In order that the present disclosure may be more readily understood, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings
FIG. 1 is a process schematic of the RRTs algorithm;
FIG. 2 is a flowchart of an environment exploration method according to an embodiment of the present invention;
FIG. 3 is a generalized Voronoi diagram provided by an embodiment of the present invention;
fig. 4 is a schematic diagram of a GVD node with environment information according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a set of heuristic boundary points provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of a fused heuristic boundary point set according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a GVD feature node set provided by an embodiment of the present invention;
FIG. 8 is a schematic diagram of collision detection provided by an embodiment of the present invention;
FIG. 9 is a schematic diagram of a path followed by a robot navigating to a target node according to an embodiment of the present invention;
FIG. 10 is a diagram of a simulation environment of a scenario provided by an embodiment of the present invention;
FIG. 11 is a diagram of a simulation environment of scenario two according to an embodiment of the present invention;
fig. 12 is a diagram of a scene triple simulation environment structure provided in the embodiment of the present invention.
Detailed Description
The present invention is further described below in conjunction with the drawings and the embodiments so that those skilled in the art can better understand the present invention and can carry out the present invention, but the embodiments are not to be construed as limiting the present invention.
Referring to fig. 1, the general steps of the existing autonomous exploration algorithm based on fast search random tree include acquiring the surrounding environment information of the robot; performing instant positioning and map construction by using an SLAM algorithm, and accurately positioning the current pose of the robot according to the constructed map; generating two fast search random trees, namely boundary point detectors to extract boundary points; when the boundary points are extracted, a starting point is artificially set in the boundary area of the map and is added into the global tree structure to serve as a root node, and the pose of the robot in the map serves as the root node of the local tree; randomly scattering points in a map area as candidate nodes, and then selecting nodes with the lowest cost from the candidate nodes in the adjacent area of the candidate nodes as growing points; performing collision detection on the growing points and the candidate nodes, if no collision exists, adding the candidate nodes into the tree structure, otherwise, sampling again; filtering and eliminating invalid boundary points; designing a gain function, calculating information gain and path cost, and selecting a target point; and navigating to the target point and updating the map.
Specifically, when the boundary point is extracted, if the candidate point is in the known region, traversing all existing nodes on the tree structure, selecting the node closest to the candidate point as a nearest neighbor point, and using a connection line from the nearest neighbor point to the candidate node as a growth direction; if the distance between the nearest point and the candidate node exceeds the preset step length, growing a step length along the growth direction by the nearest point, and taking the reached point as a growth point; if the distance does not exceed the step size, the candidate point is taken as a growing point. And clustering the detected boundary points by means of a mean-shift clustering algorithm to obtain centroid points, so that a part of boundary points can be filtered, and the calculation consumption is reduced. Meanwhile, at each moment, the grid state of the boundary point and the value in a costmap of the cost are detected, the costmap divides each grid value between 0 and 255, the white value is 255 and represents an idle state; a black value of 0, representing an obstacle; values between 0 and 255 are grey, representing unknown. If the grid state is idle, i.e. the grid point has been explored, and the costmap median exceeds a certain threshold, it indicates that the area where the grid point is located has been explored, and the grid point should be removed as an invalid point.
Due to the randomness of the RRT algorithm, the boundary points of the advancing area of the robot cannot be extracted in time when the boundary points are extracted, in addition, the boundary points extracted by the RRT algorithm have large redundancy, and the information gain evaluation model of the boundary points by the current robot exploration strategy does not relate to the consideration of the surrounding environment, so that the robot is often trapped in place during exploration or turns to other areas when the current area is not explored completely, and the backtracking phenomenon is generated.
In order to solve the problems, the environment exploration method provided by the invention rapidly generates heuristic boundary points by means of the generalized Voronoi diagram GVD and performs fusion, so that the redundancy of the boundary points is reduced; the robot carries out actual path cost calculation by means of the characteristic nodes in the Voronoi diagram during exploration, the exploration boundary point extraction rate is increased, the backtracking phenomenon is reduced, and the exploration efficiency is improved.
Referring to fig. 2, the environment exploration method provided by the present invention specifically includes the steps of:
s1, acquiring environmental information, constructing map data and positioning the current pose of the robot;
the robot is located at a random position in the environment, acquires surrounding environment information through a sensor carried by the robot, and acquires sensor laser data; the SLAM module receives the sensor laser data, converts the laser data into raster map data by utilizing an SLAM algorithm and updates a part of the map into a known area; meanwhile, the position and the pose of the robot are corrected by utilizing the grid map, and the position and the pose of the robot in the environment are accurately positioned;
s2, generating a generalized Voronoi diagram according to the environment information, and extracting all boundary points of the generalized Voronoi diagram to construct a GVD node set;
referring to fig. 3, a subscription sensor receives global map data issued by a SLAM module to generate a generalized voronoi diagram; the global map divides each grid value into-1, 0, 100, -1 to represent an unknown area, 0 to represent a free area, and 100 to represent an obstacle area; referring to fig. 4, extracting a free area and an obstacle area in a global map to generate GVD nodes with environment information, where all GVD nodes form a GVD node set;
wherein the GVD node is denoted G i ＝(x i ，y i ，r i )，x i ，y i Denotes the position of the GVD node, r i Represents the radius of the GVD node, i.e., the distance of the GVD node to the nearest obstacle;
s3, selecting GVD nodes of which the number of unknown grids exceeds a preset threshold value in the GVD node radius in the GVD node set, and constructing a heuristic boundary point set;
referring to fig. 5, reacting GVD nodes in the GVD node set on the global map, performing threshold judgment on the number of grid values of an unknown region within the GVD node radius, extracting GVD nodes whose number of grid values exceeds the threshold within the radius range as heuristic boundary points, and traversing all GVD nodes to form a heuristic boundary point set; discarding GVD nodes of which the number of unknown grids does not exceed a preset threshold in the GVD node set GVD node radius range;
s4, constructing a fused heuristic boundary point set;
referring to fig. 6, selecting a GVD node with the largest radius in a heuristic boundary point set, adding the GVD node with the largest radius in the heuristic boundary point set to the heuristic boundary point set after fusion, and deleting the GVD node with the largest radius and other GVD nodes in the radius range in the heuristic boundary point set; repeating the steps until the heuristic boundary point set is empty to obtain a fused heuristic boundary point set;
s5, constructing a GVD characteristic node set;
referring to fig. 7, selecting a GVD node with the largest radius from the GVD node set, adding the GVD node into the GVD feature node set, deleting the GVD node and the rest of GVD nodes within the radius range from the GVD node set, repeatedly selecting a GVD node with the largest radius from the rest of GVD nodes, and deleting the rest of GVD nodes within the radius range until the GVD node set is empty, thereby obtaining a GVD feature node set;
s6, adding the pose of the robot and the fused heuristic boundary point set into a GVD feature node set to generate a GVD feature node complete set;
s7, performing collision detection on any two nodes in the GVD feature node complete set, designing a gain function, selecting a target node from the fused heuristic boundary point set according to the gain function value, navigating the target node and updating map data;
referring to fig. 8, during collision detection, traversing all grid points on the straight line connecting any two selected GVD nodes, and then determining grid states of the grid points; if the connecting line crosses the barrier, the collision detection is not passed, and the point needs to be picked again; i.e. the grid point has an obstacle, the state of which is occupied, collision detection is not passed. If the connecting line does not touch the obstacle, the collision detection is passed, and the two nodes can be directly connected. And performing collision detection on any two GVD nodes in the GVD feature node complete set, if the collision detection is passed, generating an edge connecting the two nodes, and determining the Euclidean distance between the two points as the length of the edge. If the collision detection fails, the length of the edge is recorded as infinity. And obtaining a characteristic matrix with side information after the collision detection of all the nodes is completed. Traversing a feature matrix with side information by using a Dijkstra algorithm, taking the current pose of the robot as an initial node and any one GVD feature node as an end point, and calculating the cost of the shortest path from the robot to each GVD feature node;
revenue for building GVD feature nodesFunction R1 f ＝w 1 *I f -w 2 *N f Selecting a target node from the fused heuristic boundary point set according to the income function value; wherein, the information gain I of the GVD node f For the number of unknown grids within the GVD node information gain radius r =3, the more the number of unknown grids is, the more information is harvested when reaching the point; path cost N f Actual path cost, w, for the current position of the robot and the position of the boundary point 1 And w 2 Is a constant for self-defining the weight.
Referring to fig. 9, a gain function value of the robot from the current pose to the converged heuristic boundary point set GVD node is calculated according to the shortest path cost and the information gain, and an optimal boundary point is selected as a target node according to the gain function value. Rapidly planning a path from the current position to a target node of the robot in a known environment by utilizing an A-algorithm according to the gain function; combining with a DWA local path planning algorithm to enable the robot to complete obstacle avoidance by using local environment information, navigating to a target node, and updating a map and updating the current coordinate position to be the current pose of the robot after the robot reaches the target node;
and S8, repeating the steps from S1 to S7 until the whole environment exploration is completed.
The robot environment exploration method provided by the embodiment is based on the generalized Veno diagram, and through constructing and fusing a heuristic boundary point set, redundant nodes are deleted, so that the boundary points are ensured to exist in each area, the extraction speed of the boundary points is increased, the redundancy is reduced, and the calculation consumption of the robot in the boundary point decision making is further reduced; the method has the advantages that the cost of the path from the robot to each GVD node is calculated by utilizing the revenue function, the GVD node with the best cost is selected as the target node, backtracking caused by inaccurate evaluation of the optimal boundary point is reduced, and exploration efficiency is improved.
Based on the above embodiment, the embodiment of the present invention further provides an environment exploration apparatus, including an information obtaining module, configured to obtain environment information; the map data updating module comprises an SLAM module and is used for updating map data according to the environmental information acquired by the information acquisition module; the heuristic boundary point extraction and fusion module is used for extracting heuristic boundary points and fusing the heuristic boundary points to generate a fused heuristic boundary point set; the GVD feature node extraction and fusion module is used for extracting GVD feature nodes and generating a GVD feature node complete set by combining the current pose of the robot and the fused heuristic boundary point set; the collision detection module is used for performing collision detection on the GVD nodes in the GVD feature node complete set to generate a feature matrix with side information, and selecting a target node according to the feature matrix with the side information and a revenue function; and the path generation module is used for planning a path from the current pose of the robot to the target node by using an A-x global path planning algorithm and a DWA local path planning algorithm.
Based on the above embodiment, the present invention further provides an environment exploring robot, comprising a memory for storing a computer program; a processor for implementing the steps of a generalized voronoi diagram based environment exploration method as described above when executing said computer program.
Based on the embodiment, the environment exploration method provided by the invention and an autonomous exploration algorithm based on fast search random numbers, namely an RRTs algorithm are subjected to comparison experiments in three simulation scenes; in each experimental scene, five times of experiments are carried out by using the environment exploration method based on the generalized voronoi diagram, five times of experiments are carried out by using the RRTs algorithm, and comparison is carried out according to experimental indexes. The experimental indexes refer to the time for exploring the whole environment and acquiring the complete map data and the total path length of the robot in running.
Referring to table 1, in a first experimental scenario, referring to fig. 10, compared to the RRTs algorithm, the environmental exploration method provided by the present invention has an average exploration time reduced by 15.0% and an average exploration path length reduced by 12.9%.
Table 1 scenario-experimental data
Referring to table 2, in the second experimental scenario and referring to fig. 11, compared to the RRTs algorithm, the environmental search method provided by the present invention has the average search time reduced by 18.6% and the average search path length reduced by 17.0%.
TABLE 2 Scenario two Experimental data
Referring to table 3, in a third experimental scenario, referring to fig. 12, compared to the RRTs algorithm, the environmental exploration method provided by the present invention has an average exploration time reduced by 22.6% and an average exploration path length reduced by 4.5%.
TABLE 3 Scenario three Experimental data
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Various other modifications and alterations will occur to those skilled in the art upon reading the foregoing description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. An environment exploration method, comprising:
s1, acquiring environmental information of a current pose of a robot, constructing map data and repositioning the current pose of the robot;
s2, generating a generalized Voronoi diagram according to the environment information, and extracting all boundary points of the generalized Voronoi diagram to construct a GVD node set;
s3, selecting GVD nodes with the unknown grid number exceeding a preset threshold value in the GVD node radius in the GVD node set, and constructing a heuristic boundary point set;
s4, selecting the GVD node with the largest radius in the heuristic boundary point set, adding the GVD node with the largest radius in the heuristic boundary point set into the heuristic boundary point set after fusion, and deleting the GVD node with the largest radius and other GVD nodes in the radius range in the heuristic boundary point set; repeating the steps until the heuristic boundary point set is empty to obtain a fused heuristic boundary point set;
s5, selecting a GVD node with the largest radius from the GVD node set, adding the GVD node with the largest radius into a GVD characteristic node set, and deleting the GVD node with the largest radius and other nodes within the radius range in the GVD node set; repeating the steps until the GVD node set is empty to obtain a GVD characteristic node set;
s6, merging the GVD feature node set, the current pose of the robot and the fused heuristic boundary point set to obtain a GVD feature node full set;
s7, randomly selecting two GVD feature nodes from the GVD feature node complete set for collision detection, designing a gain function, selecting a target node from the fused heuristic boundary point set according to the gain function value, navigating the target node and updating map data and the current pose of the robot;
and S8, repeating the steps from S1 to S7 until the whole environment exploration is completed.
2. The environment exploration method according to claim 1, wherein the obtaining of environment information of the current pose of the robot, the constructing of map data and the repositioning of the current pose of the robot comprise:
acquiring laser data by using a sensor carried by the robot to acquire surrounding environment information;
receiving laser data acquired by the sensor by utilizing an SLAM module, converting the laser data into map data, and constructing a grid map;
and correcting and repositioning the current pose of the robot by using the grid map constructed by the SLAM module.
3. The environment exploration method according to claim 1, wherein said generating a generalized voronoi diagram according to the environment information, extracting all boundary points of the generalized voronoi diagram to construct a GVD node set comprises:
acquiring the map data issued by the SLAM module to generate a generalized Voronoi diagram, wherein the map data comprises an unknown area, a free area and an obstacle area;
extracting all boundary points of the generalized voronoi diagram as GVD nodes, and constructing a GVD node set;
wherein the GVD node is denoted as G i ＝(x i ，y i ，r i )，x i ，y i Denotes the position of the GVD node, r i Represents the radius of the GVD node, i.e. the distance of the GVD node to the nearest obstacle.
4. The environment exploration method according to claim 1, wherein the constructing of a set of heuristic boundary points for GVD nodes with unknown grid number exceeding a preset threshold within the GVD node radius in said set of GVD nodes further comprises:
and discarding GVD nodes of which the number of unknown grids does not exceed a preset threshold in the GVD node set GVD node radius range.
5. The environment exploration method according to claim 1, wherein the step of arbitrarily selecting two GVD feature nodes from the GVD feature node corpus for collision detection, designing a revenue function, selecting a target node from the fused heuristic boundary point set according to the revenue function value, navigating the target node, and updating map data and the current pose of the robot comprises:
randomly selecting two GVD feature nodes from the GVD feature node complete set for collision detection, traversing all GVD feature nodes, and generating a feature matrix with side information;
calculating the path cost from the current pose of the robot to each GVD feature node according to the feature matrix with the side information;
designing a gain function, calculating a gain function value of the GVD node in the fused heuristic boundary point set, and selecting a target node according to the gain function value;
and navigating the target nodes, updating the coordinates of the target nodes to be the current pose of the robot and updating map data.
6. The environment exploration method according to claim 5, wherein said step of selecting two GVD feature nodes from said global set of GVD feature nodes to perform collision detection and traverse all GVD feature nodes to generate a feature matrix with side information comprises:
randomly selecting two GVD characteristic nodes in the GVD characteristic node complete set to carry out collision detection;
if the collision detection is passed, generating an edge connecting the two GVD characteristic nodes, and calculating the Euclidean distance of the two GVD characteristic nodes as the length of the edge;
if the collision detection is not passed, recording the length of an edge connecting the two GVD characteristic nodes as infinity;
and traversing all GVD characteristic nodes in the GVD characteristic node complete set to generate a characteristic matrix with side information.
7. The environment exploration method according to claim 5, wherein said revenue function is R1 f ＝w 1 *I f -w 2 *N f ；
Wherein, I f The information gain of the target node represents the number of unknown grids in the range of the information gain radius of the target node being 3; n is a radical of f The path cost is obtained by traversing the feature matrix with the side information by utilizing a Dijkstra algorithm and represents the actual path cost of the current pose of the robot and the target node; w is a 1 And w 2 Is a custom weight.
8. The environment exploration method according to claim 5, wherein said navigating to said target node comprises:
marking out a path from the current pose of the robot to the target node by using an A global path planning algorithm;
and (4) combining a DWA local path planning algorithm to complete obstacle avoidance and guide the robot to navigate to the target node.
9. An environment exploration device, comprising:
the information acquisition module is used for acquiring environmental information;
the map data updating module comprises an SLAM module and is used for updating map data according to the environmental information acquired by the information acquisition module;
the heuristic boundary point extraction and fusion module is used for extracting heuristic boundary points and fusing the heuristic boundary points to generate a fused heuristic boundary point set;
the GVD feature node extraction and fusion module is used for extracting GVD feature nodes and generating a GVD feature node complete set by combining the current pose of the robot and the fused heuristic boundary point set;
the collision detection module is used for performing collision detection on the GVD nodes in the GVD feature node complete set to generate a feature matrix with side information, and selecting a target node according to the feature matrix with the side information and a revenue function;
and the path generation module is used for planning a path from the current pose of the robot to the target node by using an A-x global path planning algorithm and a DWA local path planning algorithm.
10. Use of the environment exploring apparatus of claim 9 in the field of tunnel exploration.
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