Agricultural machinery automatic driving control method and system based on intelligent cockpit

Abstract

The present invention discloses an automatic driving control method and system for agricultural machinery based on an intelligent cockpit, and the method includes: generating a grid map of a working area, global path planning, updating a grid map, local path planning and automatic driving control. The present invention belongs to the field of automatic driving technology, and specifically refers to an automatic driving control method and system for agricultural machinery based on an intelligent cockpit. The scheme combines trigonometric functions with random numbers to dynamically generate particle positions, calculates fitness values ​​according to obstacle avoidance penalty factors and path length penalty factors, updates particle positions according to deviation coefficients, and determines the optimal global path; formulates a value set based on four restrictions, and then samples to obtain candidate speed combinations, simulates local paths under different candidate speed combinations, and comprehensively evaluates local paths from five aspects, selects the optimal local path with the maximum comprehensive evaluation value, and determines the optimal path planning scheme to ensure the stability and reliability of automatic driving of agricultural machinery.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving, and specifically refers to an agricultural machinery autonomous driving control method and system based on an intelligent cockpit. Background Art

[0002] The automatic driving control method of agricultural machinery is to use advanced sensor technology, machine learning algorithms and data analysis to perceive the working area environment in real time, and control the agricultural machinery to perform automatic driving through the intelligent cockpit to improve the work efficiency and accuracy. However, the existing automatic driving control method of agricultural machinery has the problem that the working area of ​​agricultural machinery is irregular in shape and the terrain is complex. The existing path planning algorithm cannot fully and effectively utilize the working area for global path search and cannot correctly select the optimal global path, resulting in the problem that the agricultural machinery cannot complete the work efficiently and safely, reducing work efficiency and increasing operation costs; the existing automatic driving control method of agricultural machinery has the problem that when the agricultural machinery encounters moving obstacles in the working area, it cannot effectively find the optimal local path, the agricultural machinery collides with the obstacle and deviates excessively from the global path due to avoiding the obstacle, resulting in damage to the agricultural machinery and increased energy consumption, reduced power efficiency and increased operation costs. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an agricultural machinery automatic driving control method and system based on an intelligent cockpit. In view of the irregular shape of the agricultural machinery working area and the complex terrain in the existing agricultural machinery automatic driving control method, the existing path planning algorithm cannot fully and effectively utilize the working area for global path search, and cannot correctly select the optimal global path, resulting in the inability of agricultural machinery to complete operations efficiently and safely, reducing work efficiency and increasing operating costs. This scheme uses a grid map as the search space, combines trigonometric functions and random numbers to dynamically generate particle positions, and enhances the scope and breadth of the global path search; calculates the fitness value based on the obstacle avoidance penalty factor and the path length penalty factor to reduce the collision risk and improve work efficiency; updates the particle position based on the deviation coefficient to determine the optimal global path, thereby improving the diversity and flexibility of particles in the search space, increasing the chance of finding the optimal global path, and ensuring the stability and reliability of the agricultural machinery automatic driving. ; In view of the problems in the existing automatic driving control methods for agricultural machinery that when the agricultural machinery encounters moving obstacles in the working area, it cannot effectively find the optimal local path, the agricultural machinery collides with the obstacles and deviates excessively from the global path due to avoiding obstacles, resulting in damage to the agricultural machinery and increased energy consumption, reduced power efficiency, and increased operating costs, this solution formulates a value set based on the value range of linear velocity, angular velocity, minimum turning radius, acceleration and deceleration limits, and braking distance limits, and then samples to obtain candidate speed combinations to avoid the agricultural machinery from losing control or being unable to travel along the planned path due to unreasonable speed; simulate the local paths under different candidate speed combinations, comprehensively evaluate the local paths from five aspects: safety, power efficiency, target proximity, path consistency, and direction consistency, and select the optimal local path with the maximum comprehensive evaluation value, so that the agricultural machinery can avoid moving obstacles in the best way and minimize the adverse effects on the operating efficiency, safety, and overall driving trajectory of the agricultural machinery.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides an agricultural machinery automatic driving control method based on a smart cockpit, the method comprising the following steps:

[0005] Step S1: Generate a grid map of the working area;

[0006] Step S2: global path planning;

[0007] Step S3: Update the grid map;

[0008] Step S4: local path planning;

[0009] Step S5: Automatic driving control.

[0010] Furthermore, in step S1, generating a grid map of the working area specifically includes the following steps:

[0011] Step S11: collecting images of the working area; collecting images of the working area through sensors installed on the agricultural machinery, and transmitting the collected images of the working area to the intelligent cockpit;

[0012] Step S12: image preprocessing: in the smart cockpit, the collected working area image is preprocessed, the contrast of the working area image is enhanced by using a histogram equalization algorithm, and the noise in the working area image is removed by using a Gaussian filtering algorithm;

[0013] Step S13: Image recognition: In the smart cockpit, the boundary data of the working area is identified from the working area image based on the Canny edge detection algorithm, and the obstacle data in the working area image is identified using the YOLO model;

[0014] Step S14: constructing a grid map; constructing a grid map of the working area in the smart cockpit based on the boundary data and obstacle data identified from the working area image, and calibrating the starting point and the target point.

[0015] Furthermore, in step S2, the global path planning specifically includes the following steps:

[0016] Step S21: Initial global path: In the smart cockpit, the grid map of the working area is used as the search space of the global path, and M initial particle positions are set between the starting point and the target point, each particle position represents a different candidate global path; the initial particle position is determined by combining the periodic perturbation of the trigonometric function with random numbers, and introducing an adjustment factor to dynamically adjust the mapping method in different intervals, generating M mapping values, and then determining the M initial particle positions in the search space. The formula used is as follows:

[0017] ;

[0018] ;

[0019] In the formula, is the initial position of the mth particle, q m+1 and q m are the mapping values ​​corresponding to the positions of the m+1th and mth initial particles, respectively, S lb and S ub are the lower and upper limits of the search space, α is an adjustment factor in the range of (0, 1), mod(·), sin(·) and cos(·) are the modulus function, sine function and cosine function, respectively, and r1 and r2 are random numbers;

[0020] Step S22: Calculate the fitness value and pre-set the safety distance , calculate the obstacle avoidance penalty factor and path length penalty factor of the candidate global path corresponding to each particle position, and then perform weighted processing according to the weight coefficient to obtain the fitness value of each particle position;

[0021] Step S23: Design the deviation coefficient, the formula used is as follows:

[0022] ;

[0023] In the formula, is the deviation coefficient of the position of the mth particle in the uth search, and are the minimum fitness value and the average fitness value at the u-th search, β is the disturbance amplitude, and u max is the maximum number of searches, is the fitness value of the mth particle position during the uth search;

[0024] Step S24: Particle position update: The particle position is updated according to the deviation coefficient, and the formula used is as follows:

[0025] ;

[0026] In the formula, and are the positions of the mth particle in the u+1th and uth searches respectively, r3, r4 and r5 are random numbers, is a position randomly selected from the M particle positions in the u-th search, δ is the attraction coefficient, is the global optimal position at the u-th search. The global optimal position is the particle position with the minimum fitness value. k1 and k2 are the golden section coefficients.

[0027] Step S25: Determine the optimal global path; pre-set the fitness threshold, update the fitness value of the particle position, if the fitness value of the global optimal position is lower than the fitness threshold, then the candidate global path represented by the global optimal position is used as the optimal global path, and the intelligent cockpit controls the agricultural machinery to start from the starting point and perform automatic driving according to the optimal global path; otherwise, if the maximum number of searches is reached, return to step S21 to re-initialize the global path; otherwise, return to step S23 to continue searching.

[0028] Furthermore, in step S3, the updating of the grid map is carried out in the process of the agricultural machinery performing automatic driving along the optimal global path. An observation area V is delineated with the agricultural machinery as the center and 3 times the safety distance as the radius. The agricultural machinery monitors the environment in the observation area V in real time through sensors and updates the grid map. If a moving obstacle is detected in the observation area V, local path planning is performed; otherwise, the agricultural machinery will continue to perform automatic driving along the optimal global path.

[0029] Furthermore, in step S4, the local path planning specifically includes the following steps:

[0030] Step S41: generating a candidate speed combination, including the following steps:

[0031] Step S411: speed limit; based on the minimum and maximum values ​​of the linear speed v and angular speed ω of the agricultural machine, formulate a first value set of speed combinations ; where v min and v max are the minimum and maximum values ​​of the linear velocity, ω min and ω max are the minimum and maximum angular velocity respectively;

[0032] Step S412: Minimum turning radius limit: Based on the minimum turning radius of the agricultural machine, formulate a second value set of speed combinations ;in, is the minimum turning radius;

[0033] Step S413: Acceleration and deceleration restrictions: Since the torque of the drive motor is limited, the agricultural machine is constrained when accelerating and decelerating, and a third value set of speed combinations is formulated. ; where v t and ω t are the linear velocity and angular velocity of the agricultural machinery at the current time t, and are the maximum deceleration and acceleration values ​​of the linear velocity, and are the maximum deceleration and acceleration of the angular velocity, is the time interval;

[0034] Step S414: Braking distance limit; Under the condition of maximum deceleration, a fourth value set of speed combinations is formulated for the agricultural machine to stop before colliding with an obstacle. ;in, is the distance between the trajectory obtained by the agricultural machine traveling at the speed combination (v, ω) and the nearest obstacle;

[0035] Step S415: Sampling; obtaining the final value set of the agricultural machinery speed combination ,right The linear velocity and angular velocity in the step size are and Take samples and get candidate linear velocity values ​​and candidate angular velocity values, pair the candidate linear velocity values ​​with the candidate angular velocity values, and obtain N v ×N ωcandidate speed combinations (v, ω);

[0036] Step S42: simulate the local path; when the agricultural machine detects a moving obstacle in the observation area V, collect the current position (x, y) and direction angle θ of the agricultural machine, and simulate the local path of the agricultural machine according to each candidate speed combination (v, ω) to obtain N v ×N ω The location of the end of the local path J(v,ω) and direction angle ;

[0037] Step S43: local path evaluation, including the following steps:

[0038] Step S431: Safety assessment: The safety value of J(v, ω) is obtained by calculating the distance between the end of the local path J(v, ω) and each obstacle in the observation area V. ; The formula used is as follows:

[0039] ;

[0040] ;

[0041] Where E is the number of obstacles detected in the observation area V, γ e is the safety factor of the e-th obstacle, is the distance between the end of the local path J(v,ω) and the e-th obstacle, σ max is the maximum distance error value;

[0042] Step S432: Power efficiency evaluation: by considering the acceleration at the end of the local path J(v, ω) and the maximum acceleration capability of the agricultural machine, the power efficiency value of J(v, ω) is obtained. ; The formula used is as follows:

[0043] ;

[0044] In the formula, and are the linear acceleration and angular acceleration at the end of the local path J(v, ω), respectively;

[0045] Step S433: Target proximity evaluation: by considering the distance between the end of the local path J(v, ω) and the target point , get the target proximity value of J(v,ω) ;

[0046] Step S434: Path consistency evaluation; by considering the distance between the end of the local path J(v, ω) and the nearest path point in the global path , and obtain the path consistency value of J(v,ω) ;

[0047] Step S435: Direction consistency evaluation; by considering the angle difference between the direction angle at the end of the local path J(v, ω) and the direction angle from the agricultural machine to the target point , we get the direction-consistent value of J(v,ω) ;

[0048] Step S436: Overall evaluation: normalize the safety value, power efficiency value, target proximity value, path consistency value and direction consistency value respectively, and then perform weighted combination to obtain the overall evaluation value of the local path. ;

[0049] Step S44: Determine the optimal local path; from all simulated local paths, select the local path with the largest comprehensive evaluation value as the optimal local path, and the intelligent cockpit controls the agricultural machinery to perform automatic driving according to the optimal local path to avoid moving obstacles.

[0050] Furthermore, in step S5, the automatic driving control is that after the intelligent cockpit controls the agricultural machinery to reach the end of the optimal local path, the grid map is updated in real time. If a moving obstacle is detected again in the observation area V, the local path planning is re-performed; otherwise, the agricultural machinery continues to perform automatic driving along the optimal global path until the agricultural machinery reaches the target point, and the automatic driving is ended.

[0051] The agricultural machinery automatic driving control system based on the intelligent cockpit provided by the present invention includes a grid map module for generating a working area, a global path planning module, an updating grid map module, a local path planning module and an automatic driving control module;

[0052] The grid map generating module of the working area collects the working area image and performs preprocessing, edge detection and obstacle recognition in the smart cockpit, constructs the grid map and calibrates the starting point and the target point, and sends the data to the global path planning module;

[0053] The global path planning module uses the grid map as the search space, combines trigonometric functions with random numbers to dynamically generate particle positions, calculates the fitness value according to the obstacle avoidance penalty factor and the path length penalty factor, updates the particle position according to the deviation coefficient, determines the optimal global path, and the intelligent cockpit controls the agricultural machinery to perform automatic driving according to the optimal global path, and sends the data to the update grid map module;

[0054] The grid map update module is a module in which the agricultural machine monitors the observation area in real time and updates the grid map, performs local path planning when a moving obstacle is detected, otherwise continues to travel along the optimal global path, and sends data to the local path planning module;

[0055] The local path planning module formulates a value set based on the value range of linear speed, angular speed, minimum turning radius, acceleration and deceleration limits, and braking distance limits, and then samples to obtain candidate speed combinations, simulates local paths under different candidate speed combinations, and comprehensively evaluates the local paths from five aspects: safety, power efficiency, target proximity, path consistency, and direction consistency, selects the optimal local path with the maximum comprehensive evaluation value, and sends the data to the automatic driving control module;

[0056] The automatic driving control module is an intelligent cockpit that controls the agricultural machinery to travel along the optimal local path and updates the grid map in real time. If a moving obstacle is detected, the local path is replanned, otherwise it continues to travel along the optimal global path until it reaches the target point.

[0057] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0058] (1) In view of the problems in the existing agricultural machinery automatic driving control methods, such as the irregular shape of the agricultural machinery working area and the complex terrain, the existing path planning algorithm cannot fully and effectively utilize the working area for global path search and cannot correctly select the optimal global path, resulting in the inability of agricultural machinery to complete operations efficiently and safely, reducing work efficiency and increasing operation costs. This scheme uses the grid map as the search space, combines trigonometric functions and random numbers to dynamically generate particle positions, increases the diversity of candidate global paths, and enhances the scope and breadth of global path search; calculates the fitness value based on the obstacle avoidance penalty factor and the path length penalty factor to ensure that the selected path is safe and efficient, reduce the risk of collision, and improve work efficiency; updates the particle position based on the deviation coefficient to determine the optimal global path, improves the diversity and flexibility of particles in the search space, increases the chance of finding the optimal global path, ensures the stability and reliability of agricultural machinery automatic driving, reduces the safety risks caused by global path planning errors, and improves work quality.

[0059] (2) In view of the problem that the existing automatic driving control methods for agricultural machinery cannot effectively find the optimal local path when the agricultural machinery encounters moving obstacles in the working area, the agricultural machinery collides with the obstacles and deviates excessively from the global path due to avoiding obstacles, resulting in damage to the agricultural machinery and increased energy consumption, reduced power efficiency, and increased operating costs, this scheme formulates a value set based on the value range of linear speed, angular velocity, minimum turning radius, acceleration and deceleration limits, and braking distance limits, and then samples to obtain candidate speed combinations to avoid the agricultural machinery from losing control or being unable to travel along the planned path due to unreasonable speed; simulates the local paths under different candidate speed combinations, and comprehensively evaluates the local paths from five aspects: safety, power efficiency, target proximity, path consistency, and direction consistency. The optimal local path with the maximum comprehensive evaluation value is selected, so that the agricultural machinery can avoid moving obstacles in the best way and minimize the adverse effects on the operating efficiency, safety, and overall driving trajectory of the agricultural machinery. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of the flow of the automatic driving control method for agricultural machinery based on the intelligent cockpit provided by the present invention;

[0061] Figure 2 A schematic diagram of an agricultural machinery automatic driving control system based on a smart cockpit provided by the present invention;

[0062] Figure 3 is a schematic flow chart of step S2;

[0063] Figure 4 It is a schematic diagram of the process of step S4.

[0064] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0066] In the description of the present invention, it is necessary to understand that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention.

[0067] Example 1, see Figure 1 The present invention provides an agricultural machinery automatic driving control method based on a smart cockpit, the method comprising the following steps:

[0068] Step S1: Generate a grid map of the working area; collect images of the working area and perform preprocessing, edge detection and obstacle recognition in the smart cockpit, build a grid map and calibrate the starting point and target point;

[0069] Step S2: Global path planning: Using the grid map as the search space, the particle positions are dynamically generated by combining trigonometric functions and random numbers, the fitness value is calculated according to the obstacle avoidance penalty factor and the path length penalty factor, the particle position is updated according to the deviation coefficient, and the optimal global path is determined. The intelligent cockpit controls the agricultural machinery to perform automatic driving according to the optimal global path;

[0070] Step S3: updating the grid map; the agricultural machine monitors the observation area in real time and updates the grid map, and performs local path planning when a moving obstacle is detected, otherwise it continues to travel along the optimal global path;

[0071] Step S4: local path planning: formulate a value set based on the range of linear speed, angular speed, minimum turning radius, acceleration and deceleration limits, and braking distance limits, and then sample to obtain candidate speed combinations, simulate local paths under different candidate speed combinations, and comprehensively evaluate the local paths from five aspects: safety, power efficiency, target proximity, path consistency, and direction consistency, and select the optimal local path with the maximum comprehensive evaluation value;

[0072] Step S5: Automatic driving control; the intelligent cockpit controls the agricultural machinery to travel along the optimal local path and updates the grid map in real time. If a moving obstacle is detected, the local path is replanned; otherwise, it continues to travel along the optimal global path until it reaches the target point.

[0073] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, generating a grid map of the working area specifically includes the following steps:

[0074] Step S11: collecting images of the working area; collecting images of the working area through sensors installed on the agricultural machinery, and transmitting the collected images of the working area to the intelligent cockpit;

[0075] Step S12: image preprocessing: in the smart cockpit, the collected working area image is preprocessed, the contrast of the working area image is enhanced by using a histogram equalization algorithm, and the noise in the working area image is removed by using a Gaussian filtering algorithm;

[0076] Step S13: Image recognition: In the smart cockpit, the boundary data of the working area is identified from the working area image based on the Canny edge detection algorithm, and the obstacle data in the working area image is identified using the YOLO model;

[0077] Step S14: constructing a grid map; constructing a grid map of the working area in the smart cockpit based on the boundary data and obstacle data identified from the working area image, and calibrating the starting point and the target point.

[0078] Example 3, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S2, the global path planning specifically includes the following steps:

[0079] Step S21: Initial global path: In the smart cockpit, the grid map of the working area is used as the search space of the global path, and M initial particle positions are set between the starting point and the target point, each particle position represents a different candidate global path; the initial particle position is determined by combining the periodic perturbation of the trigonometric function with random numbers, and introducing an adjustment factor to dynamically adjust the mapping method in different intervals, generating M mapping values, and then determining the M initial particle positions in the search space. The formula used is as follows:

[0080] ;

[0081] ;

[0082] In the formula, is the initial position of the mth particle, q m+1 and q m are the mapping values ​​corresponding to the positions of the m+1th and mth initial particles, respectively, S lb and S ub are the lower and upper limits of the search space, α is an adjustment factor in the range of (0, 1), mod(·), sin(·) and cos(·) are the modulus function, sine function and cosine function respectively, r1 and r2 are random numbers in the range of (0, 1);

[0083] Step S22: Calculate the fitness value and pre-set the safety distance , calculate the obstacle avoidance penalty factor and path length penalty factor of the candidate global path corresponding to each particle position, and then perform weighted processing according to the weight coefficient to obtain the fitness value of each particle position. The formula used is as follows:

[0084] ;

[0085] ;

[0086] ;

[0087] In the formula, is the fitness value of the mth particle position during the uth search, and are the obstacle avoidance penalty factor and path length penalty factor of the mth particle position during the uth search, and are the weight coefficients of obstacle avoidance penalty factor and path length penalty factor, respectively, and , is the distance between the position of the mth particle and the nearest static obstacle during the uth search, is the length of the global path represented by the position of the mth particle during the uth search, is the maximum length of the global path represented by the positions of M particles during the u-th search;

[0088] Step S23: Design the deviation coefficient, the formula used is as follows:

[0089] ;

[0090] In the formula, is the deviation coefficient of the position of the mth particle in the uth search, and are the minimum fitness value and the average fitness value at the u-th search, β is the disturbance amplitude, and u max is the maximum number of searches;

[0091] Step S24: Particle position update: The particle position is updated according to the deviation coefficient, and the formula used is as follows:

[0092] ;

[0093] In the formula, and are the positions of the mth particle in the u+1th and uth searches respectively, r3, r4 and r5 are random numbers in the range of (0, 1), is a position randomly selected from the M particle positions in the u-th search, δ is the attraction coefficient, which is used to control right The attraction strength, is the global optimal position at the u-th search. The global optimal position is the particle position with the minimum fitness value. k1 and k2 are the golden section coefficients.

[0094] Step S25: Determine the optimal global path; pre-set the fitness threshold, update the fitness value of the particle position, if the fitness value of the global optimal position is lower than the fitness threshold, then the candidate global path represented by the global optimal position is used as the optimal global path, and the intelligent cockpit controls the agricultural machinery to start from the starting point and perform automatic driving according to the optimal global path; otherwise, if the maximum number of searches is reached, return to step S21 to re-initialize the global path; otherwise, return to step S23 to continue searching.

[0095] By performing the above operations, in view of the fact that the existing agricultural machinery automatic driving control method has the problem that the working area of ​​the agricultural machinery is irregular in shape and the terrain is complex, the existing path planning algorithm cannot fully and effectively utilize the working area for global path search, and cannot correctly select the optimal global path, resulting in the inability of agricultural machinery to complete operations efficiently and safely, reducing work efficiency and increasing operation costs. This scheme uses the grid map as the search space, combines trigonometric functions and random numbers to dynamically generate particle positions, increases the diversity of candidate global paths, and enhances the scope and breadth of the global path search; calculates the fitness value according to the obstacle avoidance penalty factor and the path length penalty factor to ensure that the selected path is safe and efficient, reduce the risk of collision, and improve work efficiency; updates the particle position according to the deviation coefficient, determines the optimal global path, improves the diversity and flexibility of particles in the search space, increases the chance of finding the optimal global path, ensures the stability and reliability of agricultural machinery automatic driving, reduces the safety risks caused by global path planning errors, and improves the work quality.

[0096] Example 4, see Figure 1 This embodiment is based on the above embodiment. In step S3, the grid map is updated when the agricultural machine is automatically driving along the optimal global path. An observation area V is defined with the agricultural machine as the center and 3 times the safety distance as the radius. The agricultural machine monitors the environment in the observation area V in real time through sensors and updates the grid map. If a moving obstacle is detected in the observation area V, local path planning is performed. Otherwise, the agricultural machine will continue to automatically drive along the optimal global path.

[0097] Example 5, see Figure 1 and Figure 4 This embodiment is based on the above embodiment. In step S4, the local path planning specifically includes the following steps:

[0098] Step S41: generating a candidate speed combination, including the following steps:

[0099] Step S411: speed limit; based on the minimum and maximum values ​​of the linear speed v and angular speed ω of the agricultural machine, formulate a first value set of speed combinations ; where v min and v max are the minimum and maximum values ​​of the linear velocity, ω min and ω max are the minimum and maximum angular velocity respectively;

[0100] Step S412: Minimum turning radius limit: Based on the minimum turning radius of the agricultural machine, formulate a second value set of speed combinations ;in, is the minimum turning radius;

[0101] Step S413: Acceleration and deceleration restrictions: Since the torque of the drive motor is limited, the agricultural machine is constrained when accelerating and decelerating, and a third value set of speed combinations is formulated. ; where v t and ω t are the linear velocity and angular velocity of the agricultural machinery at the current time t, and are the maximum deceleration and acceleration values ​​of the linear velocity, and are the maximum deceleration and acceleration of the angular velocity, is the time interval;

[0102] Step S414: Braking distance limit; Under the condition of maximum deceleration, a fourth value set of speed combinations is formulated for the agricultural machine to stop before colliding with an obstacle. ;in, is the distance between the trajectory obtained by the agricultural machine traveling at the speed combination (v, ω) and the nearest obstacle;

[0103] Step S415: Sampling; obtaining the final value set of the agricultural machinery speed combination ,right The linear velocity and angular velocity in the step size are and Take samples and get candidate linear velocity values ​​and candidate angular velocity values, pair the candidate linear velocity values ​​with the candidate angular velocity values, and obtain N v ×N ω candidate speed combinations (v, ω);

[0104] Step S42: simulate the local path; when the agricultural machine detects a moving obstacle in the observation area V, collect the current position (x, y) and direction angle θ of the agricultural machine, and simulate the local path of the agricultural machine according to each candidate speed combination (v, ω) to obtain N v ×N ω The location of the end of the local path J(v,ω) and direction angle ; The formula used is as follows:

[0105] ;

[0106] ;

[0107] ;

[0108] In the formula, , and are the horizontal coordinate position, vertical coordinate position and direction angle of the simulated local path at the end, x, y and They are the horizontal coordinate position, vertical coordinate position and direction angle of the current agricultural machinery;

[0109] Step S43: local path evaluation, including the following steps:

[0110] Step S431: Safety assessment: The safety value of J(v, ω) is obtained by calculating the distance between the end of the local path J(v, ω) and each obstacle in the observation area V. ; The formula used is as follows:

[0111] ;

[0112] ;

[0113] Where E is the number of obstacles detected in the observation area V, γ e is the safety factor of the e-th obstacle, is the distance between the end of the local path J(v,ω) and the e-th obstacle, σ max is the maximum distance error value;

[0114] Step S432: Power efficiency evaluation: by considering the acceleration at the end of the local path J(v, ω) and the maximum acceleration capability of the agricultural machine, the power efficiency value of J(v, ω) is obtained. ; The formula used is as follows:

[0115] ;

[0116] In the formula, and are the linear acceleration and angular acceleration at the end of the local path J(v, ω), respectively;

[0117] Step S433: Target proximity evaluation: by considering the distance between the end of the local path J(v, ω) and the target point , get the target proximity value of J(v,ω) ;

[0118] Step S434: Path consistency evaluation; by considering the distance between the end of the local path J(v, ω) and the nearest path point in the global path , and obtain the path consistency value of J(v,ω) ;

[0119] Step S435: Direction consistency evaluation; by considering the angle difference between the direction angle at the end of the local path J(v, ω) and the direction angle from the agricultural machine to the target point , we get the direction-consistent value of J(v,ω) ;

[0120] Step S436: Overall evaluation: normalize the safety value, power efficiency value, target proximity value, path consistency value and direction consistency value respectively, and then perform weighted combination to obtain the overall evaluation value of the local path. ; The formula used is as follows:

[0121] ;

[0122] Where z1, z2, z3, z4 and z5 are the weight coefficients of safety value, power efficiency value, target proximity value, path consistency value and direction consistency value respectively. , , , , and They are , , , and Normalized value;

[0123] Step S44: Determine the optimal local path; from all simulated local paths, select the local path with the largest comprehensive evaluation value as the optimal local path, and the intelligent cockpit controls the agricultural machinery to perform automatic driving according to the optimal local path to avoid moving obstacles.

[0124] By performing the above operations, in view of the problem that the existing automatic driving control method for agricultural machinery cannot effectively find the optimal local path when the agricultural machinery encounters a moving obstacle in the working area, the agricultural machinery collides with the obstacle and deviates excessively from the global path due to avoiding the obstacle, resulting in damage to the agricultural machinery and increased energy consumption, reduced power efficiency, and increased operating costs, this solution formulates a value set based on the value range of linear velocity, angular velocity, minimum turning radius, acceleration and deceleration limits, and braking distance limits, and then samples to obtain a candidate speed combination to avoid the agricultural machinery from losing control or being unable to travel along the planned path due to unreasonable speed; simulates the local paths under different candidate speed combinations, comprehensively evaluates the local paths from five aspects: safety, power efficiency, target proximity, path consistency, and direction consistency, and selects the optimal local path with the maximum comprehensive evaluation value, so that the agricultural machinery can avoid moving obstacles in the best way and minimize the adverse effects on the operating efficiency, safety, and overall driving trajectory of the agricultural machinery.

[0125] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the automatic driving control is that after the intelligent cockpit controls the agricultural machinery to reach the end of the optimal local path, the grid map is updated in real time. If a moving obstacle is detected again in the observation area V, the local path planning is re-performed; otherwise, the agricultural machinery continues to perform automatic driving along the optimal global path until the agricultural machinery reaches the target point, and the automatic driving ends.

[0126] Embodiment 7, see Figure 2 , this embodiment is based on the above embodiment, and the agricultural machinery automatic driving control system based on the intelligent cockpit provided by the present invention includes a grid map module for generating a working area, a global path planning module, an updating grid map module, a local path planning module and an automatic driving control module;

[0127] The grid map generating module of the working area collects the working area image and performs preprocessing, edge detection and obstacle recognition in the smart cockpit, constructs the grid map and calibrates the starting point and the target point, and sends the data to the global path planning module;

[0128] The global path planning module uses the grid map as the search space, combines trigonometric functions with random numbers to dynamically generate particle positions, calculates the fitness value according to the obstacle avoidance penalty factor and the path length penalty factor, updates the particle position according to the deviation coefficient, determines the optimal global path, and the intelligent cockpit controls the agricultural machinery to perform automatic driving according to the optimal global path, and sends the data to the update grid map module;

[0129] The grid map update module is a module in which the agricultural machine monitors the observation area in real time and updates the grid map, performs local path planning when a moving obstacle is detected, otherwise continues to travel along the optimal global path, and sends data to the local path planning module;

[0130] The local path planning module formulates a value set based on the value range of linear speed, angular speed, minimum turning radius, acceleration and deceleration limits, and braking distance limits, and then samples to obtain candidate speed combinations, simulates local paths under different candidate speed combinations, and comprehensively evaluates the local paths from five aspects: safety, power efficiency, target proximity, path consistency, and direction consistency, selects the optimal local path with the maximum comprehensive evaluation value, and sends the data to the automatic driving control module;

[0131] The automatic driving control module is an intelligent cockpit that controls the agricultural machinery to travel along the optimal local path and updates the grid map in real time. If a moving obstacle is detected, the local path is replanned, otherwise it continues to travel along the optimal global path until it reaches the target point.

[0132] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0133] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0134] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. The automatic driving control method of agricultural machinery based on the intelligent cockpit is characterized by: The method comprises the following steps: Step S1: Generate a grid map of the working area; collect images of the working area and perform preprocessing, edge detection and obstacle recognition in the smart cockpit, build a grid map and calibrate the starting point and target point; Step S2: global path planning; Step S3: updating the grid map; the agricultural machine monitors the observation area in real time and updates the grid map, and performs local path planning when a moving obstacle is detected, otherwise it continues to travel along the optimal global path; Step S4: local path planning; Step S5: Automatic driving control; the intelligent cockpit controls the agricultural machine to drive along the optimal local path and updates the grid map in real time. If a moving obstacle is detected, the local path is replanned, otherwise it continues to drive along the optimal global path until it reaches the target point; Step S2 includes step S24: particle position update; the particle position is updated according to the deviation coefficient, and the formula used is as follows: ； In the formula, and are the positions of the mth particle in the u+1th and uth searches respectively, r3, r4 and r5 are random numbers, is a position randomly selected from the M particle positions in the u-th search, δ is the attraction coefficient, is the global optimal position at the u-th search. The global optimal position is the particle position with the minimum fitness value. k1 and k2 are the golden section coefficients. is the deviation coefficient of the position of the mth particle during the uth search.

2. The method for controlling the automatic driving of agricultural machinery based on the intelligent cockpit according to claim 1 is characterized in that: In step S2, the global path planning specifically includes the following steps: Step S21: Initial global path: In the smart cockpit, the grid map of the working area is used as the search space of the global path, and M initial particle positions are set between the starting point and the target point, each particle position represents a different candidate global path; the initial particle position is determined by combining the periodic perturbation of the trigonometric function with random numbers, and introducing an adjustment factor to dynamically adjust the mapping method in different intervals, generating M mapping values, and then determining the M initial particle positions in the search space. The formula used is as follows: ； ； In the formula, is the initial position of the mth particle, q m+1 and q m are the mapping values ​​corresponding to the positions of the m+1th and mth initial particles, respectively, S lb and S ub are the lower and upper limits of the search space, α is the adjustment factor, mod(·), sin(·) and cos(·) are the modulus function, sine function and cosine function, respectively, r1 and r2 are random numbers; Step S22: Calculate the fitness value and pre-set the safety distance , calculate the obstacle avoidance penalty factor and path length penalty factor of the candidate global path corresponding to each particle position, and then perform weighted processing according to the weight coefficient to obtain the fitness value of each particle position; Step S23: Design the deviation coefficient, the formula used is as follows: ； In the formula, is the deviation coefficient of the position of the mth particle in the uth search, and are the minimum fitness value and the average fitness value at the u-th search, β is the disturbance amplitude, and u max is the maximum number of searches, is the fitness value of the mth particle position during the uth search; Step S24: particle position update; Step S25: Determine the optimal global path; pre-set the fitness threshold, update the fitness value of the particle position, if the fitness value of the global optimal position is lower than the fitness threshold, then the candidate global path represented by the global optimal position is used as the optimal global path, and the intelligent cockpit controls the agricultural machinery to start from the starting point and perform automatic driving according to the optimal global path; otherwise, if the maximum number of searches is reached, return to step S21 to re-initialize the global path; otherwise, return to step S23 to continue searching.

3. The method for controlling the automatic driving of agricultural machinery based on the intelligent cockpit according to claim 1 is characterized in that: In step S4, the local path planning specifically includes the following steps: Step S41: generating a candidate speed combination, including the following steps: Step S411: speed limit; based on the minimum and maximum values ​​of the linear speed v and angular speed ω of the agricultural machine, formulate a first value set of speed combinations ; where v min and v max are the minimum and maximum values ​​of the linear velocity, ω min and ω max are the minimum and maximum angular velocity respectively; Step S412: Minimum turning radius limit: Based on the minimum turning radius of the agricultural machine, formulate a second value set of speed combinations ;in, is the minimum turning radius; Step S413: Acceleration and deceleration restrictions: Since the torque of the drive motor is limited, the agricultural machine is constrained when accelerating and decelerating, and a third value set of speed combinations is formulated. ; where v t and ω t are the linear velocity and angular velocity of the agricultural machinery at the current time t, and are the maximum deceleration and acceleration values ​​of the linear velocity, and are the maximum deceleration and acceleration of the angular velocity, is the time interval; Step S414: Braking distance limit; Under the condition of maximum deceleration, a fourth value set of speed combinations is formulated for the agricultural machine to stop before colliding with an obstacle. ;in, is the distance between the trajectory obtained by the agricultural machine traveling at the speed combination (v, ω) and the nearest obstacle; Step S415: Sampling; obtaining the final value set of the agricultural machinery speed combination ,right The linear velocity and angular velocity in the step size are and Take samples and get candidate linear velocity values ​​and candidate angular velocity values, pair the candidate linear velocity values ​​with the candidate angular velocity values, and obtain N v ×N ω candidate speed combinations (v, ω); Step S42: simulate the local path; when the agricultural machine detects a moving obstacle in the observation area V, collect the current position (x, y) and direction angle θ of the agricultural machine, and simulate the local path of the agricultural machine according to each candidate speed combination (v, ω) to obtain N v ×N ω The location of the end of the local path J(v,ω) and direction angle ; Step S43: local path evaluation; Step S44: Determine the optimal local path; from all simulated local paths, select the local path with the largest comprehensive evaluation value as the optimal local path, and the intelligent cockpit controls the agricultural machinery to perform automatic driving according to the optimal local path to avoid moving obstacles.

4. The method for controlling the automatic driving of agricultural machinery based on the intelligent cockpit according to claim 3 is characterized in that: In step S43, the local path evaluation specifically includes the following steps: Step S431: Safety assessment: The safety value of J(v, ω) is obtained by calculating the distance between the end of the local path J(v, ω) and each obstacle in the observation area V. ; The formula used is as follows: ； ； Where E is the number of obstacles detected in the observation area V, γ e is the safety factor of the e-th obstacle, is the distance between the end of the local path J(v,ω) and the e-th obstacle, σ max is the maximum distance error value; Step S432: Power efficiency evaluation: by considering the acceleration at the end of the local path J(v, ω) and the maximum acceleration capability of the agricultural machine, the power efficiency value of J(v, ω) is obtained. ; The formula used is as follows: ； In the formula, and are the linear acceleration and angular acceleration at the end of the local path J(v, ω), respectively; Step S433: Target proximity evaluation: by considering the distance between the end of the local path J(v, ω) and the target point , get the target proximity value of J(v,ω) ; Step S434: Path consistency evaluation; by considering the distance between the end of the local path J(v, ω) and the nearest path point in the global path , and obtain the path consistency value of J(v,ω) ; Step S435: Direction consistency evaluation; by considering the angle difference between the direction angle at the end of the local path J(v, ω) and the direction angle from the agricultural machine to the target point , we get the direction-consistent value of J(v,ω) ; Step S436: Overall evaluation: normalize the safety value, power efficiency value, target proximity value, path consistency value and direction consistency value respectively, and then perform weighted combination to obtain the overall evaluation value of the local path. .

5. The method for controlling the automatic driving of agricultural machinery based on the intelligent cockpit according to claim 1 is characterized in that: In step S1, generating a grid map of the working area specifically includes the following steps: Step S11: collecting images of the working area; collecting images of the working area through sensors installed on the agricultural machinery, and transmitting the collected images of the working area to the intelligent cockpit; Step S12: image preprocessing: in the smart cockpit, the collected working area image is preprocessed, the contrast of the working area image is enhanced by using a histogram equalization algorithm, and the noise in the working area image is removed by using a Gaussian filtering algorithm; Step S13: Image recognition: In the smart cockpit, the boundary data of the working area is identified from the working area image based on the Canny edge detection algorithm, and the obstacle data in the working area image is identified using the YOLO model; Step S14: constructing a grid map; constructing a grid map of the working area in the smart cockpit based on the boundary data and obstacle data identified from the working area image, and calibrating the starting point and the target point.

6. The method for controlling the automatic driving of agricultural machinery based on the intelligent cockpit according to claim 1 is characterized in that: In step S4, the updating of the grid map is to define an observation area V with the agricultural machine as the center and 3 times the safety distance as the radius during the process of the agricultural machine performing automatic driving along the optimal global path. The agricultural machine monitors the environment in the observation area V in real time through sensors and updates the grid map. If a moving obstacle is detected in the observation area V, local path planning is performed; otherwise, the agricultural machine will continue to drive automatically along the optimal global path.

7. The method for controlling the automatic driving of agricultural machinery based on the intelligent cockpit according to claim 1, characterized in that: In step S1, the automatic driving control is that after the intelligent cockpit controls the agricultural machinery to reach the end of the optimal local path, the grid map is updated in real time. If a moving obstacle is detected again in the observation area V, the local path planning is re-performed; otherwise, the agricultural machinery continues to perform automatic driving along the optimal global path until the agricultural machinery reaches the target point, and the automatic driving ends.

8. An agricultural machinery automatic driving control system based on a smart cockpit, used to implement an agricultural machinery automatic driving control method based on a smart cockpit as claimed in any one of claims 1 to 7, characterized in that: It includes a grid map module for generating a working area, a global path planning module, an update grid map module, a local path planning module and an automatic driving control module; The grid map generating module of the working area collects the working area image and performs preprocessing, edge detection and obstacle recognition in the smart cockpit, constructs the grid map and calibrates the starting point and the target point, and sends the data to the global path planning module; The global path planning module uses the grid map as the search space, combines trigonometric functions with random numbers to dynamically generate particle positions, calculates the fitness value according to the obstacle avoidance penalty factor and the path length penalty factor, updates the particle position according to the deviation coefficient, determines the optimal global path, and the intelligent cockpit controls the agricultural machinery to perform automatic driving according to the optimal global path, and sends the data to the update grid map module; The grid map update module is a module in which the agricultural machine monitors the observation area in real time and updates the grid map, performs local path planning when a moving obstacle is detected, otherwise continues to travel along the optimal global path, and sends data to the local path planning module; The local path planning module formulates a value set based on the value range of linear speed, angular speed, minimum turning radius, acceleration and deceleration limits, and braking distance limits, and then samples to obtain candidate speed combinations, simulates local paths under different candidate speed combinations, and comprehensively evaluates the local paths from five aspects: safety, power efficiency, target proximity, path consistency, and direction consistency, selects the optimal local path with the maximum comprehensive evaluation value, and sends the data to the automatic driving control module; The automatic driving control module is an intelligent cockpit that controls the agricultural machinery to travel along the optimal local path and updates the grid map in real time. If a moving obstacle is detected, the local path is replanned, otherwise it continues to travel along the optimal global path until it reaches the target point.

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