A drilling and anchoring robot arm trajectory planning method, system and electronic device

Abstract

The application discloses a drilling and anchoring robot drilling arm trajectory planning method and system and electronic equipment, relates to the technical field of trajectory planning, and comprises the following steps: acquiring a three-dimensional working area model of a drilling and anchoring robot, a working starting point and a drilling point; determining an initial drilling arm path of the drilling and anchoring robot by using a rapidly-exploring random tree algorithm; the initial drilling arm path is a path of a drilling arm of the drilling and anchoring robot from the working starting point to the drilling point in the three-dimensional working area model; removing redundant points in the initial drilling arm path of the drilling and anchoring robot by using a connection method to obtain a first optimized path; and fitting the first optimized path by using a second-order Bezier curve to obtain a final drilling arm path of the drilling and anchoring robot. The drilling and anchoring robot drilling arm trajectory is optimized to assist in realizing fast and accurate hole finding and drilling.

Description

Technical Field

[0001] This invention relates to the field of trajectory planning technology, and in particular to a method, system and electronic equipment for planning the trajectory of a drilling arm of a drilling and anchoring robot. Background Technology

[0002] The level of intelligence of the equipment in the fully mechanized tunneling face is the main reason restricting the intelligent development of coal mines, and the low efficiency of roadway anchoring support is a key influencing factor. Automated control of roadway anchoring support is the main link to realize intelligent and unmanned roadway tunneling in coal mines.

[0003] Currently, when drilling and anchoring robots in coal mines perform anchoring operations, they first need to locate and drill holes at the top of the roadway, evenly filling the entire roadway top before filling with anchoring agent and driving in the anchor rod for fixation. Throughout this process, hole location and drilling are done manually, consuming a significant amount of time and resulting in low efficiency. This low efficiency in the support and anchoring of fully mechanized tunneling faces leads to low levels of automation, mining imbalances, and consequently affects the progress of coal mine tunneling.

[0004] In determining multiple boreholes, the trajectory of the drilling robot arm needs to be planned. Traditional path planning algorithms include artificial potential field methods, fuzzy rule methods, genetic algorithms, neural networks, and ant colony optimization algorithms. However, these methods all require modeling obstacles within a defined space, and their computational complexity is exponentially related to the robot's degrees of freedom, making them unsuitable for planning multi-DOF robots in complex environments. A path planning algorithm based on rapidly exploring random trees (RRT) avoids spatial modeling by performing collision detection on sampled points in the state space, effectively solving path planning problems in high-dimensional spaces and with complex constraints. This method is characterized by its ability to quickly and effectively search high-dimensional spaces. By randomly sampling points in the state space, the search is directed to blank areas, thus finding a planned path from the starting point to the target point. It is suitable for solving path planning problems for multi-DOF robots in complex and dynamic environments. This algorithm has high coverage and a wide search range. However, the drawback of the RRT algorithm is that the obtained path quality is not optimal and is not smooth, potentially having sharp edges, which is detrimental to the actual operation of the drilling robot arm. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and electronic device for planning the trajectory of the drill arm of a drilling and anchoring robot, which optimizes the trajectory of the drill arm of the drilling and anchoring robot to help achieve fast and accurate hole finding and drilling.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] This invention provides a method for planning the trajectory of a drill arm in a drilling and anchoring robot, comprising:

[0008] Obtain a 3D working area model, working starting point, and drilling point for the drilling and anchoring robot;

[0009] The initial drill arm path of the drilling and anchoring robot is determined by a fast expanding random tree algorithm; the initial drill arm path is the path of the drill arm of the drilling and anchoring robot from the working starting point to the drilling point in the three-dimensional working area model.

[0010] The connection method is used to remove redundant points in the initial drill arm path of the drilling and anchoring robot to obtain the first optimized path;

[0011] The first optimized path is fitted with a second-order Bezier curve to obtain the final drill arm path of the drilling and anchoring robot.

[0012] Optionally, the three-dimensional working area model includes an obstacle area and a free area;

[0013] The step of using a fast expanding random tree algorithm to determine the initial drill arm path of the drilling and anchoring robot specifically includes:

[0014] The starting point of the work is taken as the starting point, the drilling point is taken as the target point, and the starting point of the work is taken as the root node of the initial expansion of the random tree;

[0015] For each expansion of the random tree, a random node is determined within the free region, and a nearest node in the random tree is determined based on the random node; the nearest node is the root node of the random tree with the smallest distance to the random node in the current expansion.

[0016] A node to be used is randomly determined on the line connecting the nearby node and the random node; the distance between the node to be used and the nearby node is a preset step size;

[0017] Determine whether the line connecting the nearby node and the random node intersects with the obstacle area to obtain a first result;

[0018] If the first result indicates an intersection, then discard the node to be used and return to the step of determining a random node in the free area and determining the nearest node in the random tree based on the random node;

[0019] If the first result indicates no intersection, the node to be used is added to the random tree as a new root node, and the next expansion is entered when the distance between the new root node and the target point is greater than or equal to a preset step size, until the number of expansions reaches the preset number of expansions or the distance between the new root node and the target point is less than a preset step size, at which point the expansion stops.

[0020] The root nodes of the random tree are traversed in a loop and connected to the target point to obtain the initial drill arm path of the drilling and anchoring robot.

[0021] Optionally, determining the nearest node in the random tree based on the random node specifically includes:

[0022] Using the NEAREST function and Euclidean distance, the root node of the random tree with the smallest distance to the random node in the current expansion is selected.

[0023] Optionally, a connection method is used to remove redundant points in the initial drill arm path of the drilling and anchoring robot to obtain a first optimized path, specifically including:

[0024] The initial drill arm path is divided into multiple segments; each segment includes a starting point and an ending point.

[0025] Use the working starting point as the optimization starting point, and connect the optimization starting point to the end point of the i-th road segment; i≥1;

[0026] Determine whether there is an obstacle between the line connecting the optimization starting point and the end point of the i-th road segment to obtain a second result;

[0027] If the second result indicates the presence of an obstacle, then mark the line connecting the optimization starting point and the starting point of the i-th road segment as the optimized road segment, update the optimization starting point to the starting point of the i-th road segment, and return to the step of connecting the optimization starting point and the ending point of the i-th road segment.

[0028] If the second result indicates that there is no obstacle, determine whether the end point of the i-th road segment is a drilling point to obtain the third result;

[0029] If the third result indicates no, then update the i-th road segment to the (i+1)-th road segment and return to the step of connecting the starting point of the work with the end point of the i-th road segment;

[0030] If the third result indicates that the line connecting the optimization starting point and the end point of the i-th road segment is marked as the optimized road segment;

[0031] Extract all the optimized road segments to determine the first optimized path.

[0032] Optionally, the formula for calculating the second-order Bézier curve is:

[0033] B(t)=(1-t) 2 *P0+2t(1-t)*P1+t 2 *P2 0≤t≤1

[0034] Where P0 represents the starting point of the fitted curve, P1 represents the control point of the fitted curve, P2 represents the ending point of the fitted curve, t is the coefficient, and B(t) represents the fitted curve with point P0 as the starting point and point P2 as the ending point.

[0035] Optionally, the three-dimensional working area model of the drilling and anchoring robot is drawn using MATLAB software.

[0036] To achieve the above objectives, the present invention also provides the following technical solutions:

[0037] A drilling arm trajectory planning system for a drilling and anchoring robot includes:

[0038] The model building module is used to obtain the three-dimensional working area model, working starting point, and drilling point of the drilling and anchoring robot;

[0039] The first path optimization module is used to determine the initial drill arm path of the drilling and anchoring robot using a fast expanding random tree algorithm; the initial drill arm path is the path of the drill arm of the drilling and anchoring robot from the working starting point to the drilling point in the three-dimensional working area model;

[0040] The second path optimization module is used to remove redundant points in the initial drill arm path of the drilling and anchoring robot using a connection method to obtain the first optimized path.

[0041] The third path optimization module is used to fit the first optimized path with a second-order Bezier curve to obtain the final drill arm path of the drilling and anchoring robot.

[0042] An electronic device, comprising a memory and a processor;

[0043] The memory is used to store computer programs, and the processor is used to run the computer programs to execute a drill arm trajectory planning method for a drilling and anchoring robot.

[0044] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0045] This invention provides a method, system, and electronic device for planning the trajectory of a drilling and anchoring robot's drill arm. Based on a preset starting point and drilling point, a fast expanding random tree algorithm is initially used to quickly determine the initial drill arm path of the drilling and anchoring robot in the complex three-dimensional space of an underground coal mine. Then, a connection method is used to remove redundant points, and a second-order Bézier curve is used to optimize the initial drill arm path to obtain an optimal and smooth drill arm path. This enables fast and accurate hole finding and drilling, thereby improving anchoring efficiency and enhancing the intelligence level of the equipment in the tunneling face. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the drilling arm trajectory planning method for the drilling and anchoring robot of the present invention.

[0048] Figure 2 This is a schematic diagram illustrating the principle of the RRT algorithm of this invention;

[0049] Figure 3 This is a schematic diagram of path R in the present invention;

[0050] Figure 4 This is a schematic diagram illustrating the principle of removing redundant points in this invention;

[0051] Figure 5 This is a schematic diagram of path R1 in this invention;

[0052] Figure 6 This is a schematic diagram of path R2 in this invention;

[0053] Figure 7 This is a schematic diagram of the drilling arm trajectory planning system for the drilling and anchoring robot of the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] When a drilling robot arm in a coal mine performs anchoring operations, it first needs to locate and drill holes at the top of the roadway. The distance between each pair of holes is equal, typically about 90cm. Holes need to be drilled evenly throughout the entire roadway top before anchoring agent can be filled and the anchor rod driven in for fixation. Finding the first drilling point is crucial during the drilling robot arm's hole-finding process. Therefore, this invention proposes a drilling robot arm trajectory planning method, system, and electronic equipment. The RRT algorithm is used to obtain the path planning map, then the path is optimized by using a connecting-point method to remove redundant points, and finally, the path is smoothed using Bezier curve fitting. In other words, this invention combines the advantages of RRT, the connecting-point method for removing redundant points, and second-order Bezier curve fitting to automatically and quickly find the first drilling point and optimize the trajectory, resulting in faster trajectory planning, higher quality, and a smoother path.

[0056] To make the objectives, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Example 1

[0058] like Figure 1 As shown, this embodiment provides a method for planning the trajectory of a drill arm in a drilling and anchoring robot, including:

[0059] Step 100: Obtain the 3D working area model, starting point, and drilling point of the drilling and anchoring robot. Specifically, draw the 3D model of the underground coal mine in MATLAB software, and set the starting point and a drilling point (the first hole) at the top of the roadway. The 3D working area model includes obstacle areas and free areas.

[0060] Step 200: The initial drill arm path of the drilling and anchoring robot is determined by the Rapid Expanding Random Tree (RRT) algorithm; the initial drill arm path is the path of the drill arm of the drilling and anchoring robot from the working starting point to the drilling point in the three-dimensional working area model.

[0061] Step 200 specifically includes:

[0062] 1) Take the working starting point as the starting point, the drilling point as the target point, and take the working starting point as the root node of the initial expansion of the random tree.

[0063] 2) For each expansion of the random tree, a random node is determined within the free region, and a nearest node in the random tree is determined based on the random node; the nearest node is the root node of the random tree with the smallest distance to the random node in the current expansion; specifically, the root node of the random tree with the smallest distance to the random node in the current expansion is selected based on the Euclidean distance using the NEAREST function.

[0064] 3) Randomly determine a node to be used on the line connecting the nearby node and the random node; the distance between the node to be used and the nearby node is a preset step size.

[0065] 4) Determine whether the line connecting the nearest node and the random node intersects with the obstacle area to obtain a first result. If the first result indicates an intersection, discard the candidate node and return to the step of determining a random node within the free area and determining the nearest node in the random tree based on the random node; if the first result indicates no intersection, add the candidate node as a new root node to the random tree, and proceed to the next expansion when the distance between the new root node and the target point is greater than or equal to a preset step size, until the number of expansions reaches the preset number of expansions or the distance between the new root node and the target point is less than a preset step size, at which point the expansion stops.

[0066] 5) Iterate through all root nodes of the random tree and connect them with the target point to obtain the initial drill arm path of the drilling and anchoring robot.

[0067] The RRT algorithm generates random trees through an iterative, incremental approach. The node expansion process of the RRT algorithm is as follows: Figure 2 As shown. First, initialization is performed using Xstart as the starting point and the root node of the random tree T, setting the maximum number of iterations (depending on the specific situation), and then entering the loop. In the loop example below, the random tree T includes the root node corresponding to Xstart and the root node corresponding to Xnear, and the loop process is as follows:

[0068] A random node Xrand is generated within the 3D working area model. Xrand belongs to the set RANDOM. The NEAREST function uses Euclidean distance to select the root node in the random tree T that is closest to Xrand, which is the Xnear node in the graph. If the random tree has only one root node, Xstart, then the Xnear node is the Xstart node.

[0069] Then connect Xrand and Xnear, with the direction of the connection line being the direction of random tree growth. Set a step size u as the step size for each growth of the tree, and grow the tree by one step size in the growth direction. At the end of the growth, a new node Xnew will be generated.

[0070] Check if there is an obstacle on the line connecting Xrand and Xnear. If there is an obstacle, discard the new node Xnew; otherwise, add the node Xnew to the tree.

[0071] Randomly sample state points from the 3D working area model and repeat the above steps until the distance from the newly generated node of the random tree to the drilling point is less than one step size u or the maximum number of iterations is reached, then terminate the tree growth. Directly connect the new node to the drilling point.

[0072] Once the random tree is determined, mark the drilling points and return. Iterate through the parent nodes, exploring to the root node of the random tree T. At this point, find a feasible path in the random tree that connects all root nodes and drilling points without collisions or obstacles.

[0073] In a specific application, a trajectory planning program for the RRT algorithm was written in MATLAB software. Running the program automatically generated a path from the starting point O to the drilling point A, denoted as path R. Figure 3 As shown, all subsequent drilling points are evenly spaced 90cm apart. Therefore, the planning of path R is particularly important. Due to the inherent limitations of the RRT algorithm, the resulting path R is not the optimal (shortest) path and is quite coarse, which can cause vibrations and affect drilling accuracy. Therefore, path R needs to be optimized. To address the issue of the non-optimal path, a connecting method is used to remove redundant points, minimizing the path, improving drilling efficiency, and saving time.

[0074] Step 300: Use the connection method to remove redundant points in the initial drill arm path of the drilling and anchoring robot to obtain the first optimized path.

[0075] Step 300 specifically includes:

[0076] 1) Divide the initial drill arm path into multiple segments; each segment includes a starting point and an ending point.

[0077] 2) Take the working starting point as the optimization starting point and connect the optimization starting point to the end point of the i-th road segment; i≥1.

[0078] 3) Determine whether there is an obstacle between the line connecting the optimization starting point and the end point of the i-th road segment to obtain the second result.

[0079] 4) If the second result indicates the presence of an obstacle, mark the line connecting the optimization starting point and the starting point of the i-th road segment as the optimized road segment, update the optimization starting point to the starting point of the i-th road segment, and return to the step of connecting the optimization starting point and the ending point of the i-th road segment.

[0080] 5) If the second result indicates that there is no obstacle, determine whether the end point of the i-th road segment is a drilling point to obtain a third result; if the third result indicates no, update the i-th road segment to the (i+1)-th road segment and return to the step of connecting the working start point with the end point of the i-th road segment; if the third result indicates yes, mark the line connecting the optimization start point and the end point of the i-th road segment as the optimized road segment.

[0081] 6) Extract all the optimized road segments to determine the first optimized path.

[0082] The path obtained by the RRT algorithm contains many sections with very slight curves, some of which could be completely straightened. Below is an example... Figure 4 For example, the principle of removing redundant points using the connection method is illustrated below:

[0083] Figure 4 The starting point of the path is Xstart. A step size X is set, and the path is evenly divided into several segments according to step size X, resulting in points X1, X2, X3, X4, X5, etc. Figure 4 The bold squares in the diagram represent obstacles. Connect points Xstart and X1; if there are no obstacles between them, discard point X1; then connect Xstart and X2, and repeat the above judgment operation. For example... Figure 4 As shown, the path from Xstart to X4 can be optimized into a single path. If there are obstacles between them, it means that this short path is already the optimal path. Next, using X4 as the starting point, connect X4 and X5, and continue the optimization process. Repeat this process until all the points divided by the path have been visited. The resulting path will be of higher quality and more optimized than the initial path obtained by the RRT algorithm.

[0084] In a practical application, by importing path R into MATLAB and writing a program to remove redundant points, the optimization results after simulation can be observed, showing that path R1 shortens the trajectory of path R. Figure 5 As shown.

[0085] Step 400: The first optimized path is fitted with a second-order Bezier curve to obtain the final drill arm path of the drilling and anchoring robot, thereby solving the problems of uneven trajectory and potential sharp edges. Based on the second-order Bezier curve fitting scheme, the smoothed path deviates from the path points of the planned path, resulting in a shorter curve.

[0086] A continuous function can be written as the sum of several Bernstein polynomials, and as n→∞, this polynomial converges uniformly to the original function; this is Bernstein's approximation property. The second-order expression for the Bezier curve is:

[0087] B(t)=(1-t) 2 *P0+2t(1-t)*P1+t 2 *P2 0≤t≤1

[0088] Where P0 represents the starting point of the fitted curve, P1 represents the control point of the fitted curve, P2 represents the ending point of the fitted curve, t is a coefficient (which can also be understood as a percentage), representing the change process from 0 to 1; B(t) represents the fitted curve with point P0 as the starting point and point P2 as the ending point.

[0089] In a specific application, to address the issue of vibration caused by the relatively rough path R1, Python software is used to fit the path R1, thus obtaining the final optimal path R2, as shown below. Figure 6 As shown.

[0090] In summary, to address the issue of suboptimal route quality obtained by the RRT algorithm, a method of removing redundant points using a connection method is employed to optimize the path. To address the problem of non-smooth paths with potential sharp corners, the Bezier curve fitting method is used. The above algorithms are implemented using MATLAB and Python software, enabling rapid trajectory planning for the drill arm of the drilling robot and obtaining a smooth drill arm path.

[0091] Example 2

[0092] like Figure 7 As shown, in order to execute the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, this embodiment provides a drilling arm trajectory planning system for a drilling and anchoring robot, including:

[0093] The model building module 101 is used to obtain the three-dimensional working area model, working starting point and drilling point of the drilling and anchoring robot.

[0094] The first path optimization module 201 is used to determine the initial drill arm path of the drilling and anchoring robot using a fast extended random tree algorithm; the initial drill arm path is the path of the drill arm of the drilling and anchoring robot from the working starting point to the drilling point in the three-dimensional working area model.

[0095] The second path optimization module 301 is used to remove redundant points in the initial drill arm path of the drilling and anchoring robot using a connection method to obtain the first optimized path.

[0096] The third path optimization module 401 is used to fit the first optimized path with a second-order Bezier curve to obtain the final drill arm path of the drilling and anchoring robot.

[0097] Example 3

[0098] This embodiment provides an electronic device, which includes a memory and a processor.

[0099] The memory is used to store computer programs, and the processor is used to run the computer programs to execute the drilling arm trajectory planning method of the drilling and anchoring robot in Embodiment 1.

[0100] Compared with the prior art, the present invention also has the following advantages:

[0101] This invention presents a path planning algorithm based on the Regression-Range Theory (RRT). By performing collision detection on sampled points in the state space, it effectively solves path planning problems in high-dimensional spaces and with complex constraints. However, the RRT algorithm has drawbacks: the resulting path quality is not optimal and is not smooth, potentially containing sharp edges. Therefore, to address the issue of suboptimal path quality, a method of removing redundant points using a connection method is employed to optimize the path. To address the issue of uneven path smoothness and potential sharp edges, a second-order Bezier curve fitting method is used to smooth the resulting path planning curve. This enables the rapid and accurate planning of an optimal smooth path in the complex three-dimensional space of underground coal mines, facilitating rapid and accurate hole finding and drilling, thereby improving anchoring efficiency and enhancing the intelligence level of fully mechanized tunneling face equipment.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0103] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A drill-anchor robot drill arm trajectory planning method, characterized by, The drill-anchor robot drill arm trajectory planning method comprises the following steps: acquire a three-dimensional working area model of a drill-anchor robot, a working starting point and a drilling point; the three-dimensional working area model comprises an obstacle region and a free region; determine an initial drill arm path of the drill-anchor robot by using a rapidly-exploring random tree algorithm; the initial drill arm path is a path of a drill arm of the drill-anchor robot from the working starting point to the drilling point in the three-dimensional working area model; the step of determining the initial drill arm path of the drill-anchor robot by using the rapidly-exploring random tree algorithm specifically comprises the following steps: take the working starting point as a starting point, take the drilling point as a target point, and take the working starting point as a root node of initial expansion of a random tree; for each expansion of the random tree, determine a random node in the free region, and determine a nearest node in the random tree according to the random node; the nearest node is a root node of the random tree having a minimum distance to the random node in the current expansion; randomly determine a standby node on a line connecting the nearest node and the random node; the distance between the standby node and the nearest node is a preset step length; determine whether the line connecting the nearest node and the random node intersects with the obstacle region to obtain a first result; if the first result indicates intersection, discard the standby node, and return to the step of determining the random node in the free region and determining the nearest node in the random tree according to the random node; if the first result indicates non-intersection, add the standby node as a new root node to the random tree, and enter the next expansion when the distance between the new root node and the target point is greater than or equal to a preset step length, until the number of expansions reaches a preset expansion number or the distance between the new root node and the target point is less than a preset step length, and stop the expansion; loop through all the root nodes of the random tree, and connect the root nodes with the target point to obtain the initial drill arm path of the drill-anchor robot; remove redundant points in the initial drill arm path of the drill-anchor robot by using a connection method to obtain a first optimized path; fit the first optimized path by using a second-order Bezier curve to obtain a final drill arm path of the drill-anchor robot.

2. The drill-anchor robot drill arm trajectory planning method of claim 1, wherein, the step of determining the nearest node in the random tree according to the random node specifically comprises the following steps: select a root node of the random tree having a minimum distance to the random node in the current expansion based on a NEAREST function by using an Euclidean distance.

3. The drill-anchor robot drill arm trajectory planning method of claim 1, wherein, the step of removing the redundant points in the initial drill arm path of the drill-anchor robot by using the connection method to obtain the first optimized path specifically comprises the following steps: divide the initial drill arm path into a plurality of segments; each segment comprises a segment starting point and a segment ending point; take the working starting point as an optimized starting point, and connect the optimized starting point with a segment ending point of an i-th segment; i≥1; determine whether there is an obstacle between a line connecting the optimized starting point and the segment ending point of the i-th segment to obtain a second result; If the second result indicates that there is an obstacle, a line connecting the optimization starting point and a road segment starting point of the i th road segment is marked as an optimization road segment, and the optimization starting point is updated to the road segment starting point of the i th road segment, and the step of connecting the optimization starting point and a road segment ending point of the i th road segment is returned; If the second result indicates that there is no obstacle, it is determined whether the road segment ending point of the i th road segment is a drilling point to obtain a third result; If the third result indicates no, the i th road segment is updated to an i+1 th road segment, and the step of connecting the working starting point and the road segment ending point of the i th road segment is returned; If the third result indicates yes, a line connecting the optimization starting point and the road segment ending point of the i th road segment is marked as an optimization road segment; All the optimization road segments are extracted to determine a first optimization path.

4. The drill-anchor robot drill-arm trajectory planning method of claim 1, wherein, The calculation formula of the second-order Bezier curve is: B(t) = (1 - t) 2 *P0 + 2t(1 - t) *P1 + t 2 *P2 0≤t≤1 Wherein, P0 represents a starting point of a fitting curve, P1 represents a control point of the fitting curve, P2 represents an ending point of the fitting curve, t is a coefficient, and B(t) represents a fitting curve with the starting point P0 and the ending point P2.

5. The drill-anchor robot drill arm trajectory planning method of claim 1, wherein, The three-dimensional working area model of the drill-anchor robot is drawn by using MATLAB software.

6. A drill-anchor robot drill boom trajectory planning system applying the drill-anchor robot drill boom trajectory planning method of claim 1, characterized in that, The drill-anchor robot drill arm trajectory planning system comprises: A model construction module is configured to acquire a three-dimensional working area model of a drill-anchor robot, a working starting point, and a drilling point. A first path optimization module is configured to determine an initial drill arm path of the drill-anchor robot by using a rapidly-exploring random tree algorithm; the initial drill arm path is a path of a drill arm of the drill-anchor robot from the working starting point to the drilling point in the three-dimensional working area model. A second path optimization module is configured to remove redundant points in the initial drill arm path of the drill-anchor robot by using a connection method to obtain a first optimization path. A third path optimization module is configured to fit the first optimization path by using a second-order Bezier curve to obtain a final drill arm path of the drill-anchor robot.

7. An electronic device, comprising: The electronic device comprises a memory and a processor; The memory is configured to store a computer program, and the processor is configured to run the computer program to execute the drill-anchor robot drill arm trajectory planning method in any one of claims 1-5.

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