Potential point greedy algorithm-based region filling track empty cut stroke optimization method

Abstract

The invention relates to the technical field of machining, in particular to a potential point greedy algorithm-based area filling track empty cutting stroke optimization method, which comprises the following steps of: firstly, selecting a plurality of profile curves of all areas to be machined, intersecting parallel scanning lines and offset profile curves, and performing grouping processing according to different generated intersection points; and then, aiming at the abnormal condition that the scanning lines coincide with the profile curve or the intersection points generated by the scanning lines and the profile curve are odd numbers in the grouping process, a method for shifting the scanning lines by a small section without influencing the processing distance is adopted, so that the execution efficiency of the method in the case of abnormal conditions is improved. Further, a potential point greedy algorithm is used for selecting different postures of the scanning line filling track in each group, the scanning line filling track in the group closest to the current group is connected each time so as to construct a jump cutter null cutting track, and the selection made each time is locally optimal, so that the null cutting stroke of the machining track is optimized, and the machining efficiency is improved. The overall processing efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of mechanical processing, and in particular to a method for optimizing the empty cutting stroke of a region filling trajectory based on a potential point greedy algorithm. Background Art

[0002] With the development of technology, machining has changed from manual operation to a highly automated process. In this process, trajectory planning plays a vital role, which determines the motion trajectory of the tool during the entire machining process and directly affects the machining efficiency, accuracy and cost.

[0003] Tool path generation is the core function of CAM software, which generates the tool's motion trajectory on the workpiece surface through algorithms. Tool path planning optimization technology aims to reduce processing time and improve processing quality. Through algorithm optimization, the empty cutting stroke of the processing trajectory can be reduced, while ensuring that the cutting force and heat during the processing are controlled within a reasonable range.

[0004] When planning the filling trajectory of a complex area to be processed, it is necessary not only to ensure the processing integrity of the entire selected area, that is, there must be no unprocessed area or areas beyond the selected area, but also to shorten the number of tool jumps and the distance of empty cuts between different processing areas as much as possible, that is, the empty cut stroke of the processing trajectory needs to be optimized. Therefore, how to quickly generate effective filling trajectories in complex areas, and how to optimize the connection order of filling trajectories in different areas, thereby reducing the empty cut stroke of the processing trajectory to improve the overall processing efficiency, is a difficult problem that needs to be solved at present. Summary of the invention

[0005] The purpose of the present invention is to provide a method for optimizing the empty cutting stroke of an area filling trajectory based on a potential point greedy algorithm, which adopts a grouping strategy to divide and conquer to construct the area filling trajectory, and optimizes the connection order of the area filling trajectory based on the potential point greedy algorithm, thereby reducing the empty cutting stroke of the processing trajectory and improving the overall processing efficiency.

[0006] To achieve the above object, the present invention provides a method for optimizing the empty cutting stroke of a region filling trajectory based on a potential point greedy algorithm, comprising the following steps:

[0007] Step 1: Select multiple contour curves of all areas to be processed;

[0008] Step 2: Determine the inner and outer contours of the plurality of contour curves and perform contour offset;

[0009] Step 3: Use the parallel scan lines to intersect with the offset contour curve to form a grouped scan line filling trajectory;

[0010] Step 4: Use the potential point greedy algorithm to perform the machining trajectory air cutting stroke optimization, and finally connect them to form a high-efficiency machining trajectory.

[0011] Optionally, the contour curve is a regional contour curve drawn or imported in CAD / CAM software, which is used to generate a trajectory for regional grooving processing.

[0012] Optionally, the nesting relationship between the contour curves in step 2 is divided into the following two categories:

[0013] Separation: If the contour L 1 and contour L 2 do not contain each other, then the contour L 1 With contour L 2 For separation relationship;

[0014] Within / Contains: If the contour L 1 In the outline L 2 The interior of the contour L 1 With contour L 2 is an internal relationship; then the contour L 2 With contour L 1 It is a containment relationship.

[0015] Optionally, the execution process of step 2 includes the following steps:

[0016] Step 2.1: For the contour curve set collected in step 1, traverse the nested relationship between the first contour curve and other contour curves, and traverse all contour curves in sequence;

[0017] Step 2.2: Construct a contour curve depth information tree to obtain the depth information relationship between each contour curve and other contour curves, so as to determine the information of the inner and outer contours. The formula is as follows:

[0018]

[0019] Where n is the depth of the contour curve. When n is equal to 0, it means that the contour curve is the outermost contour curve.

[0020] Step 2.3: Perform contour offset on all contour curves. Specifically, according to the information in step 2.2, perform contour offset twice, where the outer contour is offset inward and the inner contour is offset outward, and the offset distance is f.

[0021] Optionally, the execution process of step 3 includes the following steps:

[0022] Step 3.1: Get the minimum bounding box of the curve of the outermost contour, and calculate the number of scan lines N according to the data. The formula is as follows:

[0023]

[0024] Where N is the total number of scan lines, y max and min is the maximum and minimum value of the y-axis of the bounding box, and d is the offset distance;

[0025] Step 3.2: From y min Start generating a scan line parallel to the X axis from the position, and each time the scan line is offset by a distance of (N-1)*d, repeating steps 3.3 and 3.4 until N scan lines are generated;

[0026] Step 3.3: Intersect the scan line with all the offset contour curves, retain the coordinates of the intersections of the scan line and the contour curves, and sort them by the X-axis size of the intersection coordinates. The sorting rule is from small to large, and the number of intersections is recorded;

[0027] Step 3.4: When the number of intersections between the scan line and the contour curve is an odd number, or the scan line and the contour curve coincide, enter the loop operation, the loop content is: discard the intersection information generated by the scan line, and offset the scan line by a small distance that does not affect the processing, and then repeat step 3.3. Stop the loop until the number of intersections between the scan line and the contour curve is an even number, and the scan line and the contour curve do not coincide.

[0028] Step 3.5: Group the scan lines according to the number of intersections between the scan lines and the contour curves; let L i is the number of intersections between the i-th scan line and the contour curve, where {L i ,0<=i<=N}, when L i With L i+1 If they are not equal, a grouping operation occurs, and L i The previous curve is taken as a large group. Repeating this operation, all scan lines can be divided into m large groups. The grouping formula is as follows:

[0029]

[0030] Step 3.6: After completing the large groups, it is necessary to form an effective scan line filling trajectory based on the intersection of each large group, and then further divide it into small groups.

[0031] Optionally, step 4.1: construct a group information structure container, wherein the group information structure includes the following information: record whether it is the first point or the last point number F 1 , record the posture number F 2 , the potential two points are P Start1 and P Start2 , the potential two end points are P End1 and P End2 ;

[0032] Step 4.2: Traverse all group members, store the information in the group information structure container, and use the P of the first group End1 As a starting point, start looking for starting point information;

[0033] Step 4.3: For the remaining groups, use the potential point greedy algorithm to select the group closest to the starting point, and determine whether the group should maintain the original posture or reverse the posture based on the potential point distance comparison;

[0034] Step 4.4: When the distance between two points is less than 1.5d, the default is that the filling track can be directly connected; when it is greater than 1.5d, perform a jump knife operation, and then repeat step 4.3 until all group information is traversed;

[0035] Step 4.5: After step 4.4, only two adjacent scan lines in each group have not been connected. There are two ways to connect two adjacent scan lines: connecting the first point with the first point or connecting the last point with the last point, which corresponds to the bridge curve L Mode1 and L Mode2 Two types;

[0036] Step 4.6: Based on the information obtained in step 4.3, we know which group has maintained the original posture or reversed the posture, and then use the bridge curve to connect them. The connection methods for the two cases are as follows:

[0037] The original group connection method: odd scan lines connected to even scan lines is L Mode2 Connection, even number of scan lines connected to odd number of scan lines is L Mode1 connect;

[0038] The group connection method of posture reversal: odd number of scan lines connected to even number of scan lines is L Mode1 Connection, even number of scan lines connected to odd number of scan lines is L Mode2 connect;

[0039] Step 4.7: Repeat step 4.6 until all group members are traversed to achieve the generation of the final processing trajectory.

[0040] Optionally, the potential point distance comparison operation in step 4.3 corresponds to the following four cases:

[0041] If P Start1 If the last one is the last, keep the original posture and change the next starting point to P End1 ;

[0042] If P Start2 Recently, the posture is reversed and the next starting point becomes P End2 ;

[0043] If P End1 If it is the closest, keep the original posture and change the previous starting point to P Start1 ;

[0044] If P End2 Recently, the posture is reversed and the previous starting point becomes P Start2 .

[0045] The present invention provides a method for optimizing the empty cutting stroke of a region filling trajectory based on a potential point greedy algorithm. First, multiple contour curves of all the regions to be processed are selected; multiple contour curves are judged as inner and outer contours and contour offsets are performed; parallel scan lines are used to intersect with the offset contour curves to form a grouped scan line filling trajectory; the potential point greedy algorithm is used to perform the empty cutting stroke optimization of the processing trajectory, and finally connected to form a high-efficiency processing trajectory. Specifically, parallel scan lines are used to intersect with the offset contour curves, and grouping processing is performed according to the different numbers of intersections generated. Then, the entire region to be processed is divided into multiple small regions for group numbering to improve the generation efficiency of the filling trajectory. Since there are abnormal situations in the grouping process where the scan line and the contour curve overlap or the intersection points generated by the scan line and the contour curve are odd numbers, a method of offsetting the scan line by a small section without affecting the processing distance is adopted to improve the execution efficiency of the method when facing abnormal situations. Furthermore, the potential point greedy algorithm is used to select different postures for the scan line filling trajectory in each group. Each time, the scan line filling trajectory under the group closest to the current group is connected to construct a tool jumping and empty cutting trajectory. Each selection is made locally optimal, thereby optimizing the empty cutting stroke of the processing trajectory, significantly reducing the number of tool jumping and empty cutting distance of the processing trajectory, and improving the overall processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a schematic flow chart of the steps of a method for optimizing the empty cutting stroke of a region filling trajectory based on a potential point greedy algorithm of the present invention.

[0048] Figure 2 Schematic diagram of the design pattern contour curve in the embodiment of the present invention.

[0049] Figure 3 Schematic diagram of the profile curve depth information tree in an embodiment of the present invention.

[0050] Figure 4 Schematic diagram of the minimum bounding box of the contour curve in an embodiment of the present invention.

[0051] Figure 5 It is a schematic diagram when the number of intersections between the scan line and the contour curve is odd in the embodiment of the present invention.

[0052] Figure 6 It is a schematic diagram of the case where the scanning line and the contour curve coincide with each other in an embodiment of the present invention.

[0053] Figure 7 It is a schematic diagram of partitions in the partition directional scanning in an embodiment of the present invention.

[0054] Figure 8 Schematic diagram of potential endpoints in a group in an embodiment of the present invention.

[0055] Fig. 9 It is a schematic diagram of the original posture of the group in the embodiment of the present invention.

[0056] Fig.10 Schematic diagram of posture reversal in a group in an embodiment of the present invention.

[0057] Fig.11 Schematic diagram of two bridging curves in an embodiment of the present invention.

[0058] Fig.12 It is a schematic diagram of the final effect in an embodiment of the present invention.

[0059] Fig.13 This is a second schematic diagram of the final effect in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0061] See also Figure 1 The present invention provides a method for optimizing the empty cutting stroke of a region filling trajectory based on a potential point greedy algorithm, comprising the following steps:

[0062] S1: Select multiple contour curves of all areas to be processed;

[0063] S2: judging the inner and outer contours of the plurality of contour curves and performing contour offset;

[0064] S3: using the parallel scan lines to intersect with the offset contour curve to form a grouped scan line filling trajectory;

[0065] S4: Using the potential point greedy algorithm, the machining trajectory air cutting stroke optimization is performed, and finally connected to form a high-efficiency machining trajectory.

[0066] The following is a further description of the implementation steps and specific examples. Figures 2 to 13 :

[0067] Step S1: Select multiple contour curves of all areas to be processed;

[0068] In this embodiment, firstly, several area contour curves are drawn or imported into the CAD / CAM software, such as Figure 2 As shown in the design pattern contour curve, it is used to generate trajectories including but not limited to regional grooving processing.

[0069] Step S2: Determine the inner and outer contours of the plurality of contour curves and perform contour offset. The specific operation is as follows:

[0070] S201. The nesting relationship between contour curves can be divided into the following two categories:

[0071] 1. Separation. If the contour L 1 and contour L 2 do not contain each other, then the contour L 1 With contour L 2 For separation relationship

[0072] 2. Within / Contained. If the contour L 1 In the outline L 2 The interior of the contour L 1 With contour L 2 is an internal relationship; then the contour L 2 With contour L 1 It is a containment relationship.

[0073] S202, first collect the following information according to S1: Figure 2 The set of contour curves shown in the figure starts from the first contour curve and traverses the nested relationship with other contour curves, and all contour curves are traversed in this way.

[0074] S203, construct Figure 3 The contour curve depth information tree shown in the figure obtains the contour curve depth information of each contour curve including other contours, so as to judge the information of inner and outer contours. The formula is as follows:

[0075]

[0076] Where n is the depth of the contour curve. If n is equal to 0, it means that the contour curve is the outermost contour curve.

[0077] S204, performing contour offset on all contour curves. According to the information in S203, contour offset is performed twice, wherein the outer contour is offset inwards and the inner contour is offset outwards, and the offset distance is f.

[0078] Step S3: Using the parallel scan lines and the offset contour curve to intersect, a grouped scan line filling trajectory is formed. The specific operation is as follows:

[0079] S301, according to the information of S2, obtain the minimum bounding box of the curve of the outermost contour, as follows Figure 4 As shown, according to the data, the number of scan lines N is calculated as follows:

[0080]

[0081] Where N is the total number of scan lines, y max and min are the maximum and minimum values ​​of the y-axis of the bounding box, and d is the offset distance.

[0082] S302: Based on the number of scan lines N obtained in S301, min The scan line parallel to the X axis is generated from the position, and the scan line is offset by a distance of (N-1)*d each time, and steps S303 and S304 are repeated until N scan lines are generated.

[0083] S303, intersecting the scan line with all the offset contour curves, retaining the coordinates of the intersections of the scan line and the contour curves, and sorting them by the X-axis size of the intersection coordinates, with the sorting rule being from small to large, and recording the number of intersections;

[0084] S304, if Figure 5 As shown in , when the number of intersections between the scan line and the contour curve is odd, or as Figure 6 As shown, when the scan line coincides with the contour curve, a loop operation is entered, and the loop content is: discard the intersection information generated by the scan line, and offset the scan line by a small distance that does not affect the processing, and then repeat step S303. The loop stops when the number of intersections generated by the scan line and the contour curve is an even number and the scan line and the contour curve do not coincide.

[0085] S305, group the scan lines according to the number of intersections between the scan lines and the contour curves. i is the number of intersections between the i-th scan line and the contour curve, where {L i ,0<=i<=N}, when L i With L i+1 If they are not equal, a grouping operation occurs, and L i The previous curve is taken as a large group. Repeating this operation, all scan lines can be divided into m large groups. The grouping formula is as follows:

[0086]

[0087] S306, after the large groups are divided, it is necessary to form an effective scan line filling trajectory according to the intersection points of each large group, and then further divide it into small groups. Although the number of intersection points between the scan line and the contour curve in the same large group is equal, it is necessary to divide it into small groups according to the different positions of the filling line segments formed by the pairing principle of odd and even intersection points on the scan line. For example, the number of intersection points in a large group is 4, and the intersection point generated by one scan line and the contour curve is P 1 , P 2 , P 3 , P 4 Four intersection points, according to the principle of pairing two odd and even intersection points, line segment P 1 P 2 For the first group, line segment P 3 P 4 For the second group, repeat this method until all the scan lines under the current large group are traversed and finally divided into two small groups. Figure 7 As shown, it is finally divided into 5 groups, where the grouping dividing lines shown in the figure do not exist, but are only used to illustrate the segmentation area identification.

[0088] Step S4: Utilize the potential point greedy algorithm to perform the optimization of the machining trajectory air cutting stroke, and finally connect them to form a high-efficiency machining trajectory. The specific operations are as follows:

[0089] S401, construct a group information structure container, wherein the group information structure includes information such as whether it is the first point or the last point number F 1 , record the posture number F 2 , the potential two points are P Start1 and P Start2 , the potential two end points are P End1 and P End2 The group information is defined as follows: Figure 8 As shown, under each group of group members, there are four endpoints and four potential points. The top two points are set as potential first points, and the bottom two points are set as potential end points, and the relationships are matched. Further, as Fig. 9 Shown and Fig.10 As shown, P Start1 and P End1 It corresponds to the corresponding first and last points in the original posture, P Start2 and P End2 It corresponds to the corresponding first and last points in the reverse posture.

[0090] S402, traverse all group members, store the information into the group information structure container, and use the P of the first groupEnd1 As a starting point, start looking for starting point information.

[0091] S403, for the remaining groups, select the group closest to the starting point. Here, the greedy algorithm of potential points is used. For each group, there are four potential points. According to the four potential point information of each group collected in step S401, the distance is compared to check which of the four potential points is closest to the current starting point? There are four specific situations as follows:

[0092] 1. If P Start1 If the last one is the last, keep the original posture and change the next starting point to P End1 .

[0093] 2. If P Start2 Recently, the posture is reversed and the next starting point becomes P End2 .

[0094] 3. If P End1 If it is the closest, keep the original posture and change the previous starting point to P Start1 .

[0095] 4. If P End2 Recently, the posture is reversed and the previous starting point becomes P Start2 .

[0096] S404. When the distance between two points is less than 1.5d, for this type of empty cutting stroke, it is defaulted to a filling trajectory and can be directly connected, thereby reducing the number of tool jumps; when it is greater than 1.5d, a tool jump operation is performed, and then step S403 is repeated until all group information is traversed.

[0097] S405, further connect using the bridge curve. After step S404, only two adjacent scan lines in each group have not been connected. There are two ways to connect two adjacent scan lines, corresponding to connecting the first point with the first point or connecting the last point with the last point, which is defined as the bridge curve L Mode1 and L Mode2 Two types, specifically Fig.11 shown.

[0098] S406. Based on the information obtained in step S403, it is determined which group maintains the original posture or reverses the posture, and the bridge curve is used to connect the two groups. The connection methods for the two situations are as follows:

[0099] The original group connection method: odd scan lines connected to even scan lines is L Mode2 Connection, even number of scan lines connected to odd number of scan lines is L Mode1 connect.

[0100] The group connection method of posture reversal: odd number of scan lines connected to even number of scan lines is L Mode1 Connection, even number of scan lines connected to odd number of scan lines is L Mode2 connect;

[0101] S407, repeat step S406 until all group members are traversed to achieve the generation of the final processing trajectory. The final effect of the algorithm is as follows Fig.12 and Fig.13 As shown, Fig.13 The observation effect under the three-dimensional perspective can more conveniently observe the tool jumping trajectory. It can be seen that the selected processing area is covered by the scan line filling trajectory, and there is no unprocessed area or beyond the selected area. In addition, the potential point greedy algorithm is used to optimize the connection between the filling trajectories of different areas, which effectively reduces the number of tool jumping and the empty cutting distance of the processing trajectory, and improves the overall processing efficiency.

[0102] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for optimizing the empty cutting stroke of a region filling trajectory based on a potential point greedy algorithm, characterized in that: The following steps are involved: Step 1: Select multiple contour curves of all areas to be processed; Step 2: Determine the inner and outer contours of the plurality of contour curves and perform contour offset; Step 3: Use the parallel scan lines to intersect with the offset contour curve to form a grouped scan line filling trajectory; Step 4: Use the potential point greedy algorithm to perform the machining trajectory air cutting stroke optimization, and finally connect them to form a high-efficiency machining trajectory.

2. The method for optimizing the empty cutting stroke of the area filling trajectory based on the potential point greedy algorithm according to claim 1, characterized in that: The contour curve is a regional contour curve drawn or imported in CAD / CAM software, and is used to generate a trajectory for regional grooving processing.

3. The method for optimizing the empty cutting stroke of the area filling trajectory based on the potential point greedy algorithm according to claim 2, characterized in that: The nesting relationship between contour curves in step 2 is divided into the following two categories: Separation: If contour L1 and contour L2 do not contain each other, then contour L1 and contour L2 are in a separation relationship; Within / Contained: If contour L1 is inside contour L2, then contour L1 and contour L2 are in a within relationship; if contour L2 and contour L1 are in a contained relationship.

4. The method for optimizing the empty cutting stroke of the area filling trajectory based on the potential point greedy algorithm according to claim 3, characterized in that: The execution process of step 2 includes the following steps: Step 2.1: For the contour curve set collected in step 1, traverse the nested relationship between the first contour curve and other contour curves, and traverse all contour curves in sequence; Step 2.2: Construct a contour curve depth information tree to obtain the depth information relationship between each contour curve and other contour curves, so as to determine the information of the inner and outer contours. The formula is as follows: Where n is the depth of the contour curve. When n is equal to 0, it means that the contour curve is the outermost contour curve. Step 2.3: Perform contour offset on all contour curves. Specifically, according to the information in step 2.2, perform contour offset twice, where the outer contour is offset inward and the inner contour is offset outward, and the offset distance is f.

5. The method for optimizing the empty cutting stroke of the area filling trajectory based on the potential point greedy algorithm according to claim 4, characterized in that: The execution process of step 3 includes the following steps: Step 3.1: Get the minimum bounding box of the curve of the outermost contour, and calculate the number of scan lines N according to the data. The formula is as follows: Where N is the total number of scan lines, y max and min is the maximum and minimum value of the y-axis of the bounding box, and d is the offset distance; Step 3.2: From y min Start generating a scan line parallel to the X axis from the position, and each time the scan line is offset by a distance of (N-1)*d, repeating steps 3.3 and 3.4 until N scan lines are generated; Step 3.3: Intersect the scan line with all the offset contour curves, retain the coordinates of the intersections of the scan line and the contour curves, and sort them by the X-axis size of the intersection coordinates. The sorting rule is from small to large, and the number of intersections is recorded; Step 3.4: When the number of intersections between the scan line and the contour curve is an odd number, or the scan line and the contour curve coincide, enter a loop operation, the loop content is: discard the intersection information generated by the scan line, and offset the scan line by a small distance that does not affect the processing, and then repeat step 3.3 until the number of intersections between the scan line and the contour curve is an even number, and the scan line and the contour curve do not coincide, then stop the loop; Step 3.5: Group the scan lines according to the number of intersections between the scan lines and the contour curves; let L i is the number of intersections between the i-th scan line and the contour curve, where {L i ,0<=i<=N}, when L i With L i+1 If they are not equal, a grouping operation occurs, and L i The previous curve is taken as a large group. Repeating this operation, all scan lines can be divided into m large groups. The grouping formula is as follows: Step 3.6: After completing the large groups, it is necessary to form an effective scan line filling trajectory based on the intersection of each large group, and then further divide it into small groups.

6. The method for optimizing the empty cutting stroke of the area filling trajectory based on the potential point greedy algorithm according to claim 5, characterized in that: Step 4.1: Construct a group information structure container, where the group information structure includes the following information: a number F1 to record whether it is the first point or the last point, a number F2 to record the posture, and a number P for the potential two first points. Start1 and P Start2 , the potential two end points are P End1 and P End2 ; Step 4.2: Traverse all group members, store the information in the group information structure container, and use the P of the first group End1 As a starting point, start looking for starting point information; Step 4.3: For the remaining groups, use the potential point greedy algorithm to select the group closest to the starting point, and determine whether the group should maintain the original posture or reverse the posture based on the potential point distance comparison; Step 4.4: When the distance between two points is less than 1.5d, the default is that the filling track can be directly connected; when it is greater than 1.5d, perform a jump knife operation, and then repeat step 4.3 until all group information is traversed; Step 4.5: After step 4.4, only two adjacent scan lines in each group have not been connected. There are two ways to connect two adjacent scan lines: connecting the first point with the first point or connecting the last point with the last point, which corresponds to the bridge curve L Mode1 and L Mode2 Two types; Step 4.6: Based on the information obtained in step 4.3, we know which group has maintained the original posture or reversed the posture, and then use the bridge curve to connect them. The connection methods for the two cases are as follows: The original group connection method: odd scan lines connected to even scan lines is L Mode2 Connection, even number of scan lines connected to odd number of scan lines is L Mode1 connect; The group connection method of posture reversal: odd number of scan lines connected to even number of scan lines is L Mode1 Connection, even number of scan lines connected to odd number of scan lines is L Mode2 connect; Step 4.7: Repeat step 4.6 until all group members are traversed to achieve the generation of the final processing trajectory.

7. The method for optimizing the empty cutting stroke of the area filling trajectory based on the potential point greedy algorithm according to claim 6, characterized in that: The potential point distance comparison operation in step 4.3 corresponds to the following four cases: If P Start1 If the last one is the last, keep the original posture and change the next starting point to P End1 ; If P Start2 Recently, the posture is reversed and the next starting point becomes P End2 ; If P End1 If it is the closest, keep the original posture and change the previous starting point to P Start1 ; If P End2 Recently, the posture is reversed and the previous starting point becomes P Start2 .

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