Robot path planning control method and system based on laser navigation

Abstract

The invention discloses a robot path planning control method and system based on laser navigation, and belongs to the technical field of robots, and the system comprises an initial path determination module which is used for determining an initial moving path of a robot; the laser radar module is used for acquiring obstacle information on a path; the path analysis module performs analysis according to the acquired obstacle information so as to judge whether the planned path is abnormal or not, and when the path is judged to be abnormal, a control instruction is generated; and the execution module drives the robot to move correspondingly according to the control instruction. According to the invention, the initial path determination module plans the moving path most suitable for the movement of the robot according to the overall image of the working area of the robot, so that the influence on the robot during the movement can be reduced; meanwhile, when the robot moves, the moving speed of the robot can be correspondingly controlled according to the moving obstacle information on the planned path, collision between the robot and the obstacle is avoided, and accidents are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of robots, and particularly relates to a robot path planning and control method and system based on laser navigation. Background Art

[0002] With the growth of industrial automation and intelligent service requirements, autonomous mobile robots have been widely used in many fields such as logistics, manufacturing, cleaning, and agriculture. In order to achieve efficient and safe autonomous navigation, robots must possess accurate environmental perception capabilities and intelligent path planning and control strategies.

[0003] When existing robots plan routes, they can only roughly plan the range of route travel and cannot accurately determine the optimal travel path of the robot. In addition, when the robot travels on the path, it is easily affected by external moving obstacles, which can easily affect the movement of the robot and even pose a safety hazard. At the same time, when existing robots are traveling, they can only move according to the path plan and cannot give early warnings about their own operating conditions during movement. Summary of the Invention

[0004] The purpose of the present invention is to provide a robot path planning and control method and system based on laser navigation to solve the problems faced in the above background art.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A robot path planning and control system based on laser navigation, the system includes:

[0007] An initial path determination module, which is used to determine the initial movement path of the robot;

[0008] A lidar module, which is used to scan the environment in real time to obtain obstacle information on the path;

[0009] A path analysis module, which is used to analyze according to the obtained obstacle information to judge whether the planned path is abnormal. When it is judged that the path is abnormal, a control instruction is generated;

[0010] An execution module, which is used to control the robot to move according to the initial movement path. At the same time, when a control instruction is generated, the execution module drives the robot to perform corresponding movements according to the control instruction.

[0011] Further, the working method of the initial path determination module is as follows:

[0012] Obtain the overall image of the robot's working area, and obtain the obstacle-free contour information in the overall image through image processing technology, and determine it as the moving area;

[0013] The moving area is evenly divided into multiple sub - moving areas, and the sub - moving areas are divided into equal grids to obtain a set composed of multiple grid intersection points;

[0014] According to the appropriate coefficient sizes of each grid intersection point in the set, select a grid intersection point with the largest appropriate coefficient in the set and determine it as the path connection point;

[0015] Connect the path connection points of each sub - moving area in sequence, and connect the head and tail to the target starting point and ending point respectively to obtain a complete route, thereby determining this route as the initial moving path.

[0016] Furthermore, the method for obtaining the appropriate coefficient is as follows:

[0017] Based on the maximum moving width L of the robot, with the grid intersection point as the center, Determine the moving area region of each grid intersection point with as the radius, obtain the number of obstacles N in each moving area region, and the volume size V of each obstacle i ;

[0018] Obtain the appropriate coefficient through the formula ;

[0019] Among them, sl is the connection distance between the current grid intersection point and the path connection point in the previous sub - moving area, α and β are preset coefficients, maxV is the maximum volume, a1 and a2 are weight coefficients, and i ∈ [1, N].

[0020] Furthermore, the obstacle information includes the position information and moving information of the obstacle.

[0021] Furthermore, the method for the path analysis module to judge whether the planned path is abnormal is as follows:

[0022] According to the position information and moving information of the obstacle, obtain the relative distance P between the obstacle and the robot L and the relative speed P V ;

[0023] Obtain the collision time P through the formula and compare it with the preset safe collision time BP T , T and make a comparison:

[0024] When P T <BP T , it is judged that the planned path is abnormal, and a control instruction is generated.

[0025] Furthermore, the working method of the execution module is as follows:

[0026] Drive the robot to move on the initial path at the set moving speed vx;

[0027] When generating the control instruction and B1P T <P T <BP T At this time, through the formula Adjust the moving speed of the robot to vu;

[0028] When generating the control instruction and B1P T ≥P T At this time, set the moving speed of the robot to zero;

[0029] Among them, B1P T Is another safety collision time set by the system, ε is the conversion coefficient, R is the road condition complexity, R0 is the set road condition complexity comparison value, W is the weight of the goods carried by the robot, and W0 is the set weight comparison value of the carried goods.

[0030] Furthermore, the system further includes an early warning module, and the early warning module is used to give an early warning of the robot status according to the moving situation of the robot on the planned path. The early warning method is as follows:

[0031] In the case where the robot completes the operation once, obtain the positions of the robot at the path connection points of each sub-moving area, and obtain the position deviation value fl of each sub-moving area according to the distance between the current position and the center point of the path connection point j ;

[0032] Through the formula Obtain the deviation coefficient τ of the robot when it completes the operation once, where n is the number of sub-moving areas and j ∈ [1, n];

[0033] At the same time, obtain the deviation coefficient of the robot when it completes the operation m times, so as to formulate the curve function τ(x) of the deviation coefficient changing with the number of times;

[0034] Through the formula Obtain the early warning risk value Q;

[0035] When Q > Q0, it is determined that there is an abnormal risk in the operation status of the robot;

[0036] Among them, Q0 is the abnormal risk threshold preset by the system, x1 is the first time to complete the operation, x2 is the last abnormal time to complete the operation, and τ0(x) is the curve function of the standard deviation coefficient changing with the number of times set by the system.

[0037] A robot path planning control method based on laser navigation, and the control method is controlled and implemented through the robot path planning control system based on laser navigation described above.

[0038] Advantages of the present invention:

[0039] Through the initial path determination module, the present invention plans the most suitable movement path for the robot according to the overall image of the robot's working area, which can reduce the influence on the robot during movement. At the same time, when the robot is moving, the movement speed of the robot can be controlled accordingly according to the moving obstacle information on the planned path, avoiding collisions between the robot and obstacles and reducing the occurrence of accidents.

[0040] The present invention also uses the warning module to warn the robot's condition according to the movement of the robot on the planned path when the robot is moving normally, to judge whether there is an abnormal risk in the operation of the robot, and thus to repair and maintain the robot with abnormal risk in time to ensure the normal operation of the robot.

[0041] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Brief Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1 It is the system block diagram of the present invention. Detailed Embodiments

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0045] In one embodiment, a robot path planning control system based on laser navigation is disclosed, as Figure 1 shown. The control system mainly includes the following modules:

[0046] An initial path determination module, which is used to determine the initial movement path of the robot;

[0047] A lidar module, which is used to scan the environment in real time to obtain obstacle information on the path. The obstacle information includes the position information and movement information of the obstacle;

[0048] A path analysis module, which is used to analyze according to the obtained obstacle information, so as to judge whether the planned path is abnormal. When it is judged that the path is abnormal, a control instruction is generated;

[0049] An execution module, which is used to control the robot to move along the initial movement path. At the same time, when a control instruction is generated, the execution module drives the robot to move correspondingly according to the control instruction;

[0050] An early warning module, which is used to give an early warning about the robot's condition according to the robot's movement on the planned path.

[0051] Through the above technical solution, in this application, the initial path determination module plans the most suitable movement path for the robot according to the overall image of the robot's working area, which can reduce the influence on the robot during movement; at the same time, when the robot is moving, the movement speed of the robot can be correspondingly controlled according to the moving obstacle information on the planned path to avoid collisions between the robot and obstacles and reduce accidents; at the same time, in this application, the early warning module also gives an early warning about the robot's condition according to the robot's movement on the planned path when the robot is moving normally, to judge whether there is an abnormal risk in the robot's operation status, so as to perform timely maintenance on the robot with abnormal risk to ensure the normal operation of the robot.

[0052] The working method of the initial path determination module is as follows: obtain the overall image of the robot's working area, and obtain the obstacle-free contour information in the overall image through image processing technology, and determine it as the moving area;

[0053] Divide the moving area into multiple sub-moving areas, and equally divide the sub-moving areas into grids to obtain a set composed of multiple grid intersection points;

[0054] According to the magnitude of the fitness coefficient of each grid intersection point in the set, select the grid intersection point with the largest fitness coefficient in the set and determine it as the path connection point;

[0055] Connect the path connection points of each sub-moving area in sequence, and connect the head and tail to the target starting point and ending point respectively to obtain a complete route, and thus determine this route as the initial movement path;

[0056] The method for obtaining the fitness coefficient is as follows: Based on the maximum movement width L of the robot, with the grid intersection point as the center, Determine the moving area of each grid intersection point with as the radius, obtain the number of obstacles N in each moving area, and the volume size V of each obstacle i ;

[0057] Through the formula Obtain appropriate coefficients;

[0058] Among them, sl is the connection distance between the current grid intersection point and the path connection point in the previous sub-movement area, α and β are preset coefficients, maxV is the maximum volume, a1 and a2 are weight coefficients, and i ∈ [1, N].

[0059] Through the above technical solution, the above solution provides a specific method for the initial path determination module to determine the initial movement path of the robot. First, obtain the overall image of the robot's working area, and obtain the obstacle-free contour information in the overall image through image processing technology, and determine it as the movement area. In this way, according to the positions of the starting point and the ending point, the corresponding obstacle-free area can be determined as the movement area of the robot; in order to more accurately determine the best driving path of the robot, the movement area is evenly divided into multiple sub-movement areas, and the sub-movement areas are equally grid-divided to obtain a set composed of multiple grid intersection points. Based on the maximum movement width L of the robot, with the grid intersection point as the center, Determine the movement area of each grid intersection point with a radius, obtain the number of obstacles N in each movement area, and the volume size V of each obstacle i , and through the formula Obtain appropriate coefficients. Generally speaking, the fewer the number of obstacles and the smaller the volume of the obstacles in the movement area of the grid intersection point, the more suitable it is for the robot to move. And the closer the distance between two points within the appropriate range, the more beneficial it is for the robot to move. Therefore, obtain the connection distance sl between the current grid intersection point and the path connection point in the previous sub-movement area, and comprehensively analyze to obtain the appropriate coefficients of each grid intersection point. It can be seen that the larger its value, the more suitable the movement area of this point is for the robot to move; therefore, according to the size of the appropriate coefficients of each grid intersection point in the set, select the grid intersection point with the largest appropriate coefficient in the set as the path connection point, connect the path connection points of each sub-movement area in sequence, and connect the head and tail to the target starting point and ending point respectively to obtain a complete route, and thus determine this route as the initial movement path. Through this method, the best movement path of the robot can be planned according to the number and volume of obstacles in the movement area combined with the movement distance of the robot, so as to reduce the interference during the movement of the robot.

[0060] It should be noted that in the above solution, the maximum movement width L of the robot can be determined independently according to the specifications of the robot, and the preset coefficients α and β, and the weight coefficients a1 and a2 can be determined according to empirical data and relevant historical data, which will not be elaborated here.

[0061] The method for the path analysis module to determine whether the planned path is abnormal is as follows: Based on the position information and movement information of the obstacle, obtain the relative distance P between the obstacle and the robot L and the relative speed P V ;

[0062] Through the formula obtain the collision time P T , and compare it with the preset safe collision time BP T for comparison:

[0063] When P T <BP T , it is determined that the planned path is abnormal, and a control instruction is generated;

[0064] The working method of the execution module is as follows: Drive the robot to move on the initial path at the set moving speed vx;

[0065] When a control instruction is generated, and B1P T <P T <BP T , at this time, through the formula adjust the moving speed of the robot to vu;

[0066] When a control instruction is generated, and B1P T ≥P T , at this time, set the moving speed of the robot to zero;

[0067] Among them, B1P T is another safe collision time set by the system, ε is the conversion coefficient, R is the road condition complexity, R0 is the set road condition complexity comparison value, W is the weight of the goods carried by the robot, and W0 is the set carried goods weight comparison value.

[0068] Through the above technical solution, the above solution provides a method for determining whether there is an abnormality on the planned path and a method for controlling the robot when an abnormality occurs. First, when an obstacle is detected, based on the position information and movement information of the obstacle, obtain the relative distance P between the obstacle and the robot L and the relative speed P V , and then through the formula obtain the collision time P T , and compare it with the preset safe collision time BP T for comparison. When P T <BP TWhen it indicates that there may be a collision between the moving obstacle and the robot, it is determined that the planned path is abnormal, and a control instruction is generated to control the moving speed of the robot. When no control instruction is generated, the moving robot moves on the initial path at the set moving speed vx. When a control instruction is generated and B1P T <P T <BP T At this time, it indicates that the possibility of a collision is relatively high. To avoid the robot from colliding with the obstacle, the moving speed of the robot is reduced. At this time, through the formula the moving speed of the robot is adjusted to vu. It can be seen from the formula that when BP T -P T the larger the value, the more the speed needs to be reduced. Similarly, the reduction of the moving speed of the robot is related to the road condition complexity R and the weight W of the goods carried by the robot. Generally speaking, the more goods are carried and the greater the road condition complexity, the more the moving speed needs to be reduced. Therefore, through the formula a comprehensive analysis is carried out to adjust the final moving speed of the robot to vu to ensure the safe driving of the robot; when B1P T ≥P T it indicates that a collision will definitely occur at the initial moving speed at this time. To ensure safety, the robot is emergently braked, and the moving speed of the robot is set to zero at this time. In this way, when the robot is moving, the moving speed of the robot can be correspondingly controlled according to the moving obstacle information on the planned path to avoid collisions between the robot and the obstacle and reduce the occurrence of safety accidents.

[0069] It should be noted that the road condition complexity can be determined artificially in advance according to the composition of the working area of the robot and the surrounding environment, and the safe collision time BP T 、B1P T can be determined according to empirical data. The conversion coefficient ε, the set road condition complexity comparison value R0, and the set carried goods weight comparison value W0 can all be determined according to historical data combined with empirical data, and will not be elaborated here.

[0070] The system also includes a warning module. The warning module is used to warn the robot status according to the moving situation of the robot on the planned path. The warning method is: in the case of the robot completing a job once, obtain the positions of the robot at the path connection points of each sub-moving area, and according to the distance between the current position and the center point of the path connection point, the position deviation value fl of each sub-moving area can be obtained j ;

[0071] Through the formula the deviation coefficient τ of the robot in one complete job is obtained, where n is the number of sub-moving areas, and j ∈ [1, n];

[0072] Simultaneously obtain the deviation coefficient of the robot under m times of task completion, and thus formulate the curve function τ(x) of the deviation coefficient varying with the number of times;

[0073] Through the formula Obtain the early warning risk value Q;

[0074] When Q > Q0, it is determined that there is an abnormal risk in the operation status of the robot;

[0075] Among them, Q0 is the preset abnormal risk threshold of the system, x1 is the first task completion, x2 is the last abnormal task completion, and τ0(x) is the curve function of the standard deviation coefficient varying with the number of times set by the system.

[0076] Through the above technical solution, the above solution provides a method for the early warning module to warn the robot status according to the movement of the robot on the planned path. First, when the robot is working normally and in the case of the robot completing a task once, obtain the positions of the robot at the path connection points of each sub-movement area. According to the distance between the current position and the center point of the path connection point, thus obtain the position deviation value fl of each sub-movement area j , and thus through the formula Obtain the deviation coefficient τ of the robot under one-time task completion. It can be seen that the larger the deviation coefficient, the greater the fluctuation change of the robot during movement, and the greater the possibility of abnormal operation of the robot. In order to obtain abnormal conditions more accurately, continuously collect the task conditions m times, obtain the deviation coefficient of the robot under m times of task completion, and thus formulate the curve function τ(x) of the deviation coefficient varying with the number of times. Then through the formula Obtain the early warning risk value Q, where τ0(x) is the curve function of the standard deviation coefficient varying with the number of times set by the system. Compare the formulated curve function of the deviation coefficient varying with the number of times with the standard curve function. It can be seen that the larger the value of the early warning risk value, the greater the possibility of abnormal risk in the operation status of the robot. Therefore, compare the early warning risk value with the preset abnormal risk threshold Q0. When Q > Q0, it is determined that there is an abnormal risk in the operation status of the robot. In this way, when the robot is moving normally, the robot status can be warned according to the movement of the robot on the planned path, so as to judge whether there is an abnormal risk in the operation status of the robot, and thus timely repair and maintain the robot with abnormal risk to ensure the normal operation of the robot.

[0077] It should be noted that Q0 is the preset abnormal risk threshold Q0 of the system, and the curve function τ0(x) of the standard deviation coefficient varying with the number of times set by the system can be determined according to the historical operation data of the robot, and will not be described in detail here.

[0078] A robot path planning control method based on laser navigation, and this control method is implemented through the above-mentioned robot path planning control system based on laser navigation.

[0079] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by this claim book, they shall fall within the protection scope of the present invention.

Claims

1. A robot path planning control system based on laser navigation, characterized in that, The system includes: An initial path determination module, which is used to determine the initial movement path of the robot; A lidar module, which is used to scan the environment in real time to obtain obstacle information on the path; A path analysis module, which is used to analyze according to the obtained obstacle information to judge whether the planned path is abnormal. When it is judged that the path is abnormal, a control instruction is generated; An execution module, which is used to control the robot to move along the initial movement path. At the same time, when a control instruction is generated, the execution module drives the robot to perform corresponding movement according to the control instruction.

2. The robot path planning control system based on laser navigation according to claim 1, wherein, The working method of the initial path determination module is as follows: Obtain the overall image of the robot working area, and obtain the obstacle-free contour information in the overall image through image processing technology, and determine it as the movement area; Divide the movement area into multiple sub-movement areas, and equally divide the sub-movement areas to obtain a set composed of multiple grid intersection points; According to the appropriate coefficient sizes of each grid intersection point in the set, select the grid intersection point with the largest appropriate coefficient in the set and determine it as the path connection point; Connect the path connection points of each sub-movement area in sequence, and connect the head and tail to the target starting point and ending point respectively to obtain a complete route, and thus determine this route as the initial movement path.

3. The robot path planning control system based on laser navigation according to claim 2, characterized in that, The method for obtaining the appropriate coefficient is as follows: Based on the maximum moving width L of the robot, with the grid intersection as the center, determine the moving area region of each grid intersection with the radius, obtain the number N of obstacles in each moving area region, and the volume size V of each obstacle i ; Obtain appropriate coefficients through the formula to obtain appropriate coefficients; Wherein, sl is the connection distance between the current grid intersection point and the path connection point in the previous sub-movement area, α and β are preset coefficients, maxV is the maximum volume, a1 and a2 are weight coefficients, and i ∈ [1, N].

4. A robot path planning control system based on laser navigation according to claim 1, characterized in that, The obstacle information includes the position information and movement information of the obstacle.

5. The robot path planning control system based on laser navigation according to claim 4, wherein, The method for the path analysis module to judge whether the planned path is abnormal is as follows: Based on the position information and movement information of the obstacle, the relative distance P between the obstacle and the robot is obtained L and the relative speed P V ; Through the formula the collision time P is obtained T , and it is compared with the preset safe collision time BP T as follows: When P T <BP T If so, it is determined that the planned path is abnormal, and a control instruction is generated.

6. The robot path planning control system based on laser navigation according to claim 5, characterized in that, The working method of the execution module is as follows: Drive the robot to move on the initial path at the set moving speed vx; When generating a control instruction and B1P T <P T <BP T At this time, the moving speed of the robot is adjusted to vu through the formula vu = vx ; When generating a control command and B1P T ≥P T then, set the moving speed of the robot to zero at this time; Among them, B1P T is another safety collision time set for the system, ε is the conversion coefficient, R is the road condition complexity, R0 is the set comparison value of road condition complexity, W is the weight of the goods carried by the robot, and W0 is the set comparison value of the weight of the goods carried.

7. A robot path planning control system based on laser navigation according to claim 6, characterized in that, The system further includes an early warning module, which is used to give an early warning of the robot status according to the movement situation of the robot on the planned path. The early warning method is as follows: In the case where the robot completes the operation once, obtain the positions of the robot at the path connection points of each sub-movement area, and based on the distance between the current position and the center point of the path connection point, thereby obtain the position deviation value fl of each sub-movement area j ; Through the formula the deviation coefficient τ of the robot under one-time task completion is obtained, where n is the number of sub-movement areas, and j ∈ [1, n]; At the same time, obtain the deviation coefficient of the robot under m times of completing the operation, so as to formulate the curve function τ(x) of the deviation coefficient changing with the number of times; Through the formula the early warning risk value Q is obtained; When Q > Q0, it is judged that there is an abnormal risk in the operation status of the robot; Wherein, Q0 is the abnormal risk threshold preset by the system, x1 is the first time to complete the operation, x2 is the last abnormal completion of the operation, and τ0(x) is the standard deviation coefficient curve function set by the system changing with the number of times.

8. A robot path planning and control method based on laser navigation, characterized in that, The control method is implemented through the robot path planning control system based on laser navigation described in any one of claims 1-7.

Images