CN115034466A - Mushroom reciprocating pushing nondestructive picking mode and path planning method thereof - Google Patents

Abstract

The invention discloses a method for reciprocating nondestructive picking of mushrooms and a path planning method thereof, comprising the following steps: A. designing a reciprocating picking method for separating mushrooms from culture medium; B. determining an optimal driving direction for nondestructive picking of mature mushrooms; C. A dual-objective optimal mature mushroom picking path planning algorithm considering the non-destructive picking driving direction and optimal path. The method of the invention optimizes the mushroom picking path problem. The invention realizes the optimization of the multi-objective problem after the mushrooms reciprocatingly promote picking and the optimal path. Through the determination of the non-destructive picking direction and the picking path planning, it is very good to avoid the current mushrooms to be picked and the surrounding mushrooms when picking mushrooms. damage, thereby effectively improving the success rate of non-destructive mushroom picking and improving the picking efficiency. The invention is not limited to the picking of edible fungi such as mushrooms and straw mushrooms, and is also applicable to the directional picking of other spherical cluster fruits and their trajectory planning.

Description

A kind of mushroom reciprocating promoting non-destructive picking method and its path planning method

technical field

The invention relates to the technical field of picking machinery, in particular to the field of mushroom picking, and in particular relates to a non-destructive picking method for reciprocating mushrooms and a path planning method thereof.

Background technique

With the continuous development of mushroom cultivation technology, mushroom cultivation has formed factory production. Mushroom picking is the longest and most laborious link in the entire production chain due to the long cycle of mushroom picking and the need to invest a lot of labor for a long time to ensure timely harvesting. Manual work is currently the main method of picking. Mushroom picking is labor-intensive and the environment is humid. Long-term picking will consume a lot of physical strength of the pickers and have a certain impact on their health. At the same time, due to the shortage of young and young labor in rural areas, the traditional production method of manual mushroom picking has become an important bottleneck restricting the development of the mushroom planting industry. Therefore, mushroom picking urgently needs to be automated as soon as possible.

In the existing automatic mushroom picking method, the mushroom picking is mainly accomplished by the end effector sucking or holding the mushroom cap and then rotating to complete the separation of the fruit body and the culture medium. However, field experiments have shown that, on the one hand, this method of rotary separation and picking is prone to separation between the stipe and the fungus cap (ie, root damage) when the fruiting body has a strong binding force to the medium. The growth is anisotropic and the density is not equal, and there may be multiple mushrooms attached and adhered around a mushroom. In this case, picking by grabbing or rotating is difficult to adapt to the mushrooms that grow together, and it is easy to cause damage to the surrounding mushrooms, which seriously affects the picking quality.

Therefore, the intelligent and non-destructive picking of mushrooms has become a key technical problem to be solved urgently by fruit and vegetable picking robots. It is necessary to propose a non-destructive picking method for mushroom reciprocation and its path planning method.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve the problem that the fruiting body picking damage rate is high in the above-mentioned existing mushroom automatic intelligent picking technology, and provide a kind of mushroom reciprocating nondestructive picking method and its path planning method, which effectively improves mature mushrooms, especially The success rate of gathering mushrooms to achieve non-destructive picking also optimizes the length of the picking path and improves the picking efficiency, so as to obtain the optimal picking path while realizing the non-destructive picking of mushrooms.

In order to achieve the above object, the technical scheme of the present invention is as follows:

A mushroom reciprocating nondestructive picking method and a path planning method thereof, mainly comprising:

A: Design the method of reciprocating and promoting the separation of mushrooms and culture medium;

B: The method for determining the optimal driving direction for non-destructive picking of mature mushrooms;

C: A dual-objective optimized mature mushroom picking path planning algorithm considering the non-destructive picking driving direction and optimal path.

Preferably, a mushroom reciprocating non-destructive picking method and a path planning method thereof specifically include:

A: Design the method of reciprocating and promoting the separation of mushrooms and culture medium;

Step A1: Use the end effector to clamp or adsorb the mushroom;

Step A2: push the mushroom back and forth along the optimal pushing direction of the mushroom to realize the separation of the mushroom and the culture medium;

B: The method for determining the optimal driving direction for non-destructive picking of mature mushrooms;

Step B1: Obtain the mushroom center coordinates, mushroom cap radius and mushroom height data in the mushroom distribution image, and save the above data as mushroom data features;

Step B2: Use the mushroom data features to search out the neighborhood mushrooms and the sub-neighborhood mushrooms around the target mushroom;

Step B3: Calculate the neighborhood mushroom solution set and the sub-neighborhood mushroom solution set and determine the optimal pushing direction of the mushroom;

C: A dual-objective optimized mature mushroom picking path planning algorithm considering the driving direction and optimal path of non-destructive picking;

Step C1: Build a multi-objective optimization model for mushroom picking paths:

Step C1.1: Construct the mushroom reciprocatingly push the picking failure rate function, and calculate the picking failure rate of the picking sequence;

Step C1.1.1: Judgment of the reciprocating push of the target mushroom to pick;

Step C1.1.2: Calculate the picking failure rate of the mushroom picking sequence;

Step C1.2: Build a mushroom path length calculation function to calculate the path length of the picking sequence;

Step C1.3: establish a multi-objective optimization model according to the established mushroom reciprocating push picking failure rate function and path length function;

Step C2: After the model construction of Step C1 is completed, use the improved NSGA-II algorithm to solve the model to obtain the Pareto non-dominated set;

Step C2.1: Chromosome coding of population individuals;

Step C2.2: Initialize the population;

Step C2.3: Inject the extreme value of the single-target picking path length into the initial population and calculate the p _failure and dist _sum function values of all individuals in the population;

Step C2.4: The elite strategy selects the parent population;

Step C2.5: Selection, crossover, mutation operations:

Step C2.6: merge and deduplicate the parent-child population;

Step C2.7: Select the offspring population by the elite strategy of adding the cyclic crowding sorting algorithm;

Step C2.7.1: Set the crowding degree of the two boundary individuals 1 _d and n _d in the non-dominated hierarchy as inf, sort the individuals in the non-dominated hierarchy according to the crowding degree from high to low, and delete the individual with the smallest crowding degree;

Step C2.7.2: Recalculate the crowding degree of the remaining individuals in the hierarchy, and delete the individual with the smallest crowding degree again;

Step C2.7.3: Iterate sequentially until the specified number of solutions remain.

Step C2.8: Repeat the steps C2.5 to C2.8 to the target genetic algebra and then jump out of the current loop;

Step C2.9: According to the weight coefficient, select the optimal solution from the non-dominated solution set and output it.

Preferably, the step A specifically includes:

Step A1: Use the end effector to clamp or adsorb the mushroom;

Step A2: Push the mushroom back and forth along the optimal pushing direction of the mushroom to separate the mushroom from the culture medium.

Preferably, the optimal pushing direction of the mature mushroom in the step A2 is determined based on the method for determining the optimal pushing direction of the nondestructive picking of the mature mushroom in the step B, and the method specifically includes:

Step B1: Obtain mushroom center coordinates, mushroom cap radius and mushroom height data in the mushroom distribution image, and save the above data as mushroom data features;

Step B2: Use the mushroom data features to search for the neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom; the mushrooms that intersect or are tangent to the target mushroom are called neighborhood mushrooms, which are far away from the target mushroom but still reciprocate the target mushroom. Mushrooms that push space to cause interference are called subneighborhood mushrooms;

The evaluation index for judging suspicious mushrooms as neighborhood mushrooms is as follows:

0<a _oi ≤r _oi

where a _oi represents the center distance between the target mushroom and the ith suspicious mushroom; ro _oi represents the sum of the cap radius of the target and the ith suspicious mushroom; i∈[1,…,n-1], n is the total number of mushrooms ;

The evaluation index for judging suspicious mushrooms as sub-neighborhood mushrooms is as follows:

r _oi <a _oi ≤d _o +r _i

where _ri is the radius of the mushroom cap of the suspicious mushroom, and do is the reciprocating push range of the target _mushroom . The formula is as follows:

In the formula, h _o and r _o represent the height of the target mushroom and the radius of the mushroom cap respectively, k is the coefficient of pushing the picking range, k∈(0,1];

其中，i∈[1,2,3,…,m]，m为邻域蘑菇总数；选择其中任一条向量如选

作为基向量，剩余其他向量称为邻域向量，邻域向量与基向量依次做叉乘与点乘运算，具体公式如下：Step B3: Calculate the neighborhood mushroom solution set, the sub-neighborhood mushroom solution set, and determine the optimal pushing direction of the mushroom; whether the target mushroom is surrounded by its neighborhood mushrooms is analogous to the relationship between points and polygons, and the improved cross-product discrimination method is used to determine Judging the relationship between points and polygons to determine whether the target mushroom is surrounded by its neighbor mushrooms; assuming that the number of neighbor mushrooms is m, make a vector between the target mushroom particle p _o and each neighbor mushroom particle p _i

Among them, i∈[1,2,3,…,m], m is the total number of mushrooms in the neighborhood; choose any one of the vectors if you choose

As a basis vector, the remaining other vectors are called neighborhood vectors, and the neighborhood vectors and basis vectors perform cross product and dot product operations in turn. The specific formula is as follows:

Where f is the direction coefficient, θ _{2, i} is the vector angle with direction;

The results obtained after all the neighborhood vectors and the basis vectors are cross-multiplied and dot-multiplied in turn are arranged in ascending order, and the maximum value θ _max and the minimum value θ _min in the results are selected; the specific formula for judging the relationship between the point and the polygon is as follows:

When the point is on and outside the polygon, it is considered that the target mushroom is not surrounded by its neighbor mushrooms; the neighborhood mushroom solution set A is recorded, and the specific expression is as follows:

A=360°-(|θ _max |+|θ _min |)

Using the inner common tangent theorem to calculate the inner common tangent angle between the target mushroom and a certain sub-neighborhood mushroom is as follows:

where n is the number of mushrooms in the sub-neighborhood, α _oi is the angle formed by the common tangent between the target mushroom and the sub-neighborhood mushroom, α _oi is the center distance between the target mushroom and the sub-neighborhood mushroom, r _o , _ri are the target mushroom, respectively Mushroom cap radius and sub-neighborhood mushroom cap radius;

The supplementary angle β _oi of the internal common tangent angle within the range of 0-360° is called the sub-neighborhood mushroom individual solution set; the calculation formula of the supplementary angle β _oi is as follows:

β _oi =360°-α _oi , i∈[1,...,n]

The existence of the neighborhood mushrooms and the sub-neighborhood mushrooms around the picked target mushroom is mainly divided into three situations:

1) There are no neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom;

2) There are both neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom;

3) There is only one of the neighborhood mushrooms and the sub-neighborhood mushrooms around the target mushroom;

的情况，则视为目标蘑菇往复推动采摘失败，退出可摘算法判断；若所有取交集操作完成后交集不为空集，则保留该交集并将该交集用于确定可行推动方向；对于第三种情况，当目标蘑菇周围仅存在邻域蘑菇时，将目标蘑菇与邻域蘑菇关系判断中获得的邻域蘑菇解集用于确定可行推动方向；当目标蘑菇周围仅存在次邻域蘑菇时，所有次邻域蘑菇个体解集β_oi的交集便为该目标蘑菇被所有次邻域蘑菇干涉后的最终可行推动方向解集称为次邻域蘑菇解集B，具体表达形式如下：For the first case, in order to avoid invalid calculation, the default pushing direction of the target mushroom picking in this case is 180°; for the second case, the solution set A of the neighborhood mushrooms should be solved in turn with the individual solution set of the sub-neighborhood mushrooms β _oi performs the intersection operation, if the intersection appears to be an empty set

In the case of , it is considered that the target mushroom has failed to reciprocately push the picking, and the judgment of the picking algorithm is exited; if the intersection is not an empty set after all the intersection operations are completed, the intersection is retained and used to determine the feasible pushing direction; for the third In this case, when there are only neighboring mushrooms around the target mushroom, the neighborhood mushroom solution set obtained in the judgment of the relationship between the target mushroom and the neighboring mushrooms is used to determine the feasible driving direction; when there are only sub-neighborhood mushrooms around the target mushroom, The intersection of the individual solution sets β _oi of all the sub-neighborhood mushrooms is the final feasible pushing direction solution set after the target mushroom is interfered by all the sub-neighborhood mushrooms, which is called the sub-neighborhood mushroom solution set B, and the specific expression is as follows:

When the sub-neighborhood mushroom solution set B is an empty set, the target mushroom does not reciprocately promote the picking, which is regarded as a failure to pick. success;

Select the solution set with the largest angle range from the obtained feasible pushing direction solution set as the optimal pushing direction solution set; the optimal pushing direction solution formula:

where θ _down and θ _up are the lower and upper bounds of the solution set, respectively.

Preferably, especially for the actual situation of the growth of mushrooms, in order to achieve the non-destructive picking of mature mushrooms, in conjunction with step B, step C specifically includes the following steps:

Step C1: Construct a multi-objective optimization model of mushrooms reciprocatingly push the picking path:

Step C2: After the model construction of Step C1 is completed, use the improved NSGA-II algorithm to solve the model to obtain the Pareto non-dominated set;

Step C3: According to the result obtained in Step C2, the obtained Pareto non-dominated set is sorted based on the weights.

Preferably, the step C1 specifically includes the following steps:

Step C1.1: Construct the mushroom reciprocatingly push the picking failure rate function, and calculate the picking failure rate of the picking sequence;

Use step B to search and record the total number of failed mushrooms in a complete picking sequence; calculate the picking failure rate of the mushroom picking sequence, and its determination form is as follows:

Among them, n _failure represents the total number of mature mushrooms that fail to reciprocate in the picking path, and n _sum is the total number of mature mushrooms;

Step C1.2: Build a mushroom path length calculation function to calculate the path length of the picking sequence; the specific form of the path length calculation function is as follows:

Among them, n is the total number of mature mushrooms, and x and y are the horizontal and vertical coordinates of the mushrooms, respectively;

Step C1.3: establish a multi-objective optimization model according to the established mushroom reciprocating push-picking failure rate function and the path length function; the optimization objectives of the multi-objective optimization model are:

The multi-objective optimization model sets two optimization objectives, the first objective is to minimize the picking failure rate, and the second objective is to minimize the picking path length.

Preferably, in the step C2, the process of solving the multi-objective optimization model based on the NSGA-II method specifically includes the following steps:

Step C2.1: Chromosome coding of population individuals;

Step C2.2: Initialize the population and calculate the p _failure and dist _sum function values of individuals in the population;

Step C2.3: Inject the extreme value of the single-target picking path length into the initial population;

Step C2.4: The elite strategy selects the parent population;

Step C2.5: selection, crossover and mutation operations;

Step C2.6: merge and deduplicate the parent-child population;

Step C2.7: Select the offspring population by the elite strategy of adding the cyclic crowding sorting algorithm;

Step C2.8: repeating the steps C2.5 to C2.7 to the target genetic algebra and then jumping out of the current loop;

Step C2.9: According to the weight coefficient, select the optimal solution from the non-dominated solution set and output it.

Preferably, in the step C2.7, the main steps of the cyclic congestion sorting algorithm are as follows:

Step C2.7.1: Set the crowding degree of the two boundary individuals 1 _d and n _d in the non-dominated hierarchy as inf, sort the individuals in the non-dominated hierarchy according to the crowding degree from high to low, and delete the individual with the smallest crowding degree;

Step C2.7.2: Recalculate the crowding degree of the remaining individuals in the hierarchy, and delete the individual with the smallest crowding degree again;

Step C2.7.3: Iterate sequentially until the specified number of solutions remain.

A further technical solution, step A2 in the step A: push the mushroom back and forth along the optimal pushing direction of the mushroom to realize the optimal pushing direction in the separation of the mushroom and the culture medium is to use step B: the determination of the optimal pushing direction for the nondestructive picking of mature mushrooms method to determine.

A further technical scheme, the step C1.1.1 under the step C1.1: the reciprocating push of the target mushroom is based on the following judgment:

Use the method for determining the optimal pushing direction of B. mature mushrooms to determine whether the target mushrooms can be picked by reciprocating push.

A further technical scheme, the specific manner of the chromosome coding of the individual population in step C2.1 under the step C2 is as follows:

The encoding adopts the integer permutation encoding method. If a total of M mature mushrooms are screened out, the chromosomes are divided into M segments, and each segment is the number corresponding to a mature mushroom. When 10 mature mushrooms {1,2,3,4,5,6,7,8,9 ,10}, then {10,5,3,4,7,6,9,8,2,1} is a legal chromosome.

In a further technical solution, the specific description of the crossover mode of the particle swarm algorithm in step C2.3 under step C2 is as follows:

The crossover method of particle swarm optimization adopts sequential crossover (OX), that is, the starting and ending positions are randomly selected in the two parent chromosomes, and the genes in this region of parent chromosome 1 are copied to the same position of child 1, and then placed in the same position of child 1. The genes missing in offspring 1 are filled in sequence on parent chromosome 2. Another progeny was obtained in a similar manner.

In a further technical solution, the elite strategy in step C2.4 under step C2 selects the parent population mainly on the basis of non-dominant level and crowding degree. Individuals with higher priority are selected in order, and the crowding degree is used to select among the same-level individuals. The formula for calculating crowding degree is as follows:

分别为当前层级中的第m个目标函数的最大与最小值；where n is the number of individuals in the current level, f _m (i) represents the m-th objective function value of the i-th chromosome in the current level,

are the maximum and minimum values of the mth objective function in the current level, respectively;

In a further technical solution, the specific formula for adjusting the crossover probability by stages in step C2.6 under step C2 is as follows:

Among them, T is the maximum evolutionary generation, T ₁ =αT, T ₂ =(1-α)T, usually α is 0.382 or 0.258; β is the adjustment coefficient of the crossover probability in the later stage of evolution, β∈(0,1], the coefficient The setting of can guarantee that the crossover probability asymptotically approaches a value other than 0 in the later stages of evolution.

A further technical solution, the detailed description of the repeated individual control strategy in step C2.7 under step C2 is as follows:

In the NSGA-II algorithm, the new population S(t) is generated by using the elite strategy for the population after the parent population P(t) and the child population R(t) are merged. In the process of selection, crossover and mutation of the parent population P(t), when the probability of crossover and mutation is not 1, there are always some individuals without crossover and mutation. Therefore, after the parent and child populations merge, there will be duplicate individuals. When the new population S(t) is subsequently non-dominated, these duplicate individuals will not dominate each other, and some duplicate individuals will be selected into the new parent population P(t+1). After so many generations, the new parent generation is occupied by more and more repeated solutions, resulting in very few non-dominated solutions in the final Pareto solution set.

The specific steps for repeating the individual control strategy are as follows:

(1) Delete the duplicate individuals in the population S(t) after the parent generation P(t) and the child generation R(t) are merged;

(2) Judging whether the number of the population after deduplication is less than the number of the population P(t), if it is less than, continue to perform championship selection, crossover, mutation, and merge the population, and return to (1), otherwise the population will be quickly non-dominated Sort and select N individuals as the new parent population P(t+1).

Compared with the prior art, the technical solution of the present invention has outstanding substantive features and significant advantages:

1. In order to realize the non-destructive picking of mushrooms, the present invention proposes a method for reciprocating non-destructive picking, especially for dense mushrooms, a trajectory planning method for determining the optimal driving direction and picking sequence suitable for mushroom reciprocating and non-destructive picking is proposed. ;

2. The present invention uses the relationship between points and polygons in graph theory to analogize the relationship between the picked mushrooms and their surrounding mushrooms in the actual growth environment, and uses the results calculated by the improved cross-product discrimination method and the inner common tangent theorem to determine the picked mushrooms Whether reciprocating push picking is possible and the optimal pushing direction when picking is possible, and then optimize the non-destructive picking success rate and path length of the mature mushroom picking path through the improved NSGA-II algorithm;

3. In order to improve the optimization efficiency of the algorithm, the present invention adopts methods such as performing a population quality improvement operation on the initial population and injecting a single target extremum into the initial population, which effectively improves the convergence of the algorithm and the distribution of the solution set, so that the algorithm can be guaranteed to converge to the true value. At the same time as the Pareto frontier, the approximate Pareto optimal solution set with more uniform distribution and wider coverage is obtained;

4. The present invention realizes the optimization of the multi-objective problem after the combination of the mushroom reciprocating and picking and the optimal path, overcomes the phenomenon of separation of the mushroom cap and the mushroom stem in the existing picking technology, and also determines the non-destructive picking direction and the picking path. Strong planning can avoid damage to other mushrooms when picking mushrooms, thus effectively improving the success rate of mushroom picking without damage;

5. The method of the present invention is not limited to the picking of edible fungi such as mushrooms and straw mushrooms, but is also applicable to the directional picking of other globular-like cluster fruits and their trajectory planning.

Description of drawings

FIG. 1 is a flow chart of a preferred embodiment of the method of the present invention.

FIG. 2 is a schematic diagram of the reciprocating push picking according to the second embodiment of the present invention.

FIG. 3 is a flow chart of judging that the ripe mushroom can be picked according to the second embodiment of the present invention.

FIG. 4 is a flowchart of a mushroom picking path planning algorithm according to Embodiment 3 of the present invention.

FIG. 5 is an image of the actual distribution of mushrooms in the third embodiment.

Fig. 6 is the optimal picking path diagram of mature mushrooms in the third embodiment.

FIG. 7 is a schematic diagram of the pushing direction of picking mature mushrooms in Embodiment 3. FIG.

Detailed ways

For better understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and examples.

Example 1

In the present embodiment, referring to FIG. 1 , a non-destructive picking method of mushroom reciprocating push and a path planning method thereof, specifically include:

A: Design the method of reciprocating and promoting the separation of mushrooms and culture medium;

Step A1: Use the end effector to clamp or adsorb the mushroom;

Step A2: push the mushroom back and forth along the optimal pushing direction of the mushroom to realize the separation of the mushroom and the culture medium;

B: The method for determining the optimal driving direction for non-destructive picking of mature mushrooms;

Step B1: Obtain the mushroom center coordinates, mushroom cap radius and mushroom height data in the mushroom distribution image, and save the above data as mushroom data features;

Step B2: Use the mushroom data features to search out the neighborhood mushrooms and the sub-neighborhood mushrooms around the target mushroom;

Step B3: Calculate the neighborhood mushroom solution set and the sub-neighborhood mushroom solution set and determine the optimal pushing direction of the mushroom;

C: A dual-objective optimized mature mushroom picking path planning algorithm considering the driving direction and optimal path of non-destructive picking;

Step C1: Build a multi-objective optimization model for mushroom picking paths:

Step C1.1: Construct the mushroom reciprocatingly push the picking failure rate function, and calculate the picking failure rate of the picking sequence;

Step C1.1.1: Judgment of the reciprocating push of the target mushroom to pick;

Step C1.1.2: Calculate the picking failure rate of the mushroom picking sequence;

Step C1.2: Build a mushroom path length calculation function to calculate the path length of the picking sequence;

Step C1.3: establish a multi-objective optimization model according to the established mushroom reciprocating push picking failure rate function and path length function;

Step C2: After the model construction of Step C1 is completed, use the improved NSGA-II algorithm to solve the model to obtain the Pareto non-dominated set;

Step C2.1: Chromosome coding of population individuals;

Step C2.2: Initialize the population;

Step C2.3: Inject the extreme value of the single-target picking path length into the initial population and calculate the p _failure and dist _sum function values of all individuals in the population;

Step C2.4: The elite strategy selects the parent population;

Step C2.5: Selection, crossover, mutation operations:

Step C2.6: merge and deduplicate the parent-child population;

Step C2.7: Select the offspring population by the elite strategy of adding the cyclic crowding sorting algorithm;

Step C2.7.1: Set the crowding degree of the two boundary individuals 1 _d and n _d in the non-dominated hierarchy as inf, sort the individuals in the non-dominated hierarchy according to the crowding degree from high to low, and delete the individual with the smallest crowding degree;

Step C2.7.2: Recalculate the crowding degree of the remaining individuals in the hierarchy, and delete the individual with the smallest crowding degree again;

Step C2.7.3: Iterate sequentially until the specified number of solutions remain.

Step C2.8: Repeat the steps C2.5 to C2.8 to the target genetic algebra and then jump out of the current loop;

Step C2.9: According to the weight coefficient, select the optimal solution from the non-dominated solution set and output it.

The non-destructive mushroom picking method and its path planning method in this embodiment not only effectively improve the success rate of non-destructive picking of mature mushrooms, especially aggregated mushrooms, but also optimize the length of the picking path and improve the picking efficiency, so as to realize the non-destructive mushroom picking. At the same time, the optimal path for picking is obtained.

Embodiment 2

This embodiment is basically the same as the above-mentioned embodiment, and the special features are:

In this embodiment, as shown in Figures 1 to 3, the optimal pushing direction of the mature mushrooms in the step A2 is determined based on the method for determining the optimal pushing direction of the nondestructive picking of the mature mushrooms in the step B, and the method specifically includes:

Step B1: Obtain mushroom center coordinates, mushroom cap radius and mushroom height data in the mushroom distribution image, and save the above data as mushroom data features;

Step B2: Use the mushroom data features to search for the neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom; the mushrooms that intersect or are tangent to the target mushroom are called neighborhood mushrooms, which are far away from the target mushroom but still reciprocate the target mushroom. Mushrooms that push space to cause interference are called subneighborhood mushrooms;

The evaluation index for judging suspicious mushrooms as neighborhood mushrooms is as follows:

0<a _oi ≤r _oi

where a _oi represents the center distance between the target mushroom and the ith suspicious mushroom; ro _oi represents the sum of the cap radius of the target and the ith suspicious mushroom; i∈[1,…,n-1], n is the total number of mushrooms ;

The evaluation index for judging suspicious mushrooms as sub-neighborhood mushrooms is as follows:

r _oi <a _oi ≤d _o +r _i

where _ri is the radius of the mushroom cap of the suspicious mushroom, and do is the reciprocating push range of the target _mushroom . The formula is as follows:

In the formula, h _o and r _o represent the height of the target mushroom and the radius of the mushroom cap respectively, k is the coefficient of pushing the picking range, k∈(0,1];

其中，i∈[1,2,3,…,m]，m为邻域蘑菇总数；选择其中任一条向量如选

作为基向量，剩余其他向量称为邻域向量，邻域向量与基向量依次做叉乘与点乘运算，具体公式如下：Step B3: Calculate the neighborhood mushroom solution set, the sub-neighborhood mushroom solution set, and determine the optimal pushing direction of the mushroom; whether the target mushroom is surrounded by its neighborhood mushrooms is analogous to the relationship between points and polygons, and the improved cross-product discrimination method is used to determine Judging the relationship between points and polygons to determine whether the target mushroom is surrounded by its neighbor mushrooms; assuming that the number of neighbor mushrooms is m, make a vector between the target mushroom particle p _o and each neighbor mushroom particle p _i

Among them, i∈[1,2,3,…,m], m is the total number of mushrooms in the neighborhood; choose any one of the vectors if you choose

As a basis vector, the remaining other vectors are called neighborhood vectors, and the neighborhood vectors and basis vectors perform cross product and dot product operations in turn. The specific formula is as follows:

Where f is the direction coefficient, θ _{2, i} is the vector angle with direction;

The results obtained after all the neighborhood vectors and the basis vectors are cross-multiplied and dot-multiplied in turn are arranged in ascending order, and the maximum value θ _max and the minimum value θ _min in the results are selected; the specific formula for judging the relationship between the point and the polygon is as follows:

When the point is on and outside the polygon, it is considered that the target mushroom is not surrounded by its neighbor mushrooms; the neighborhood mushroom solution set A is recorded, and the specific expression is as follows:

A=360°-(|θ _max |+|θ _min |)

Using the inner common tangent theorem to calculate the inner common tangent angle between the target mushroom and a certain sub-neighborhood mushroom is as follows:

where n is the number of mushrooms in the sub-neighborhood, α _oi is the inner common tangent angle formed by the target mushroom and the sub-neighborhood mushroom, a _oi is the center distance between the target mushroom and the sub-neighborhood mushroom, r _o , _ri are the target mushroom, respectively Mushroom cap radius and sub-neighborhood mushroom cap radius;

The supplementary angle β _oi of the internal common tangent angle within the range of 0-360° is called the sub-neighborhood mushroom individual solution set; the calculation formula of the supplementary angle β _oi is as follows:

β _oi =360°-α _oi , i∈[1,...,n]

The existence of the neighborhood mushrooms and the sub-neighborhood mushrooms around the picked target mushroom is mainly divided into three situations:

1) There are no neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom;

2) There are both neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom;

3) There is only one of the neighborhood mushrooms and the sub-neighborhood mushrooms around the target mushroom;

的情况，则视为目标蘑菇往复推动采摘失败，退出可摘算法判断；若所有取交集操作完成后交集不为空集，则保留该交集并将该交集用于确定可行推动方向；对于第三种情况，当目标蘑菇周围仅存在邻域蘑菇时，将目标蘑菇与邻域蘑菇关系判断中获得的邻域蘑菇解集用于确定可行推动方向；当目标蘑菇周围仅存在次邻域蘑菇时，所有次邻域蘑菇个体解集β_oi的交集便为该目标蘑菇被所有次邻域蘑菇干涉后的最终可行推动方向解集称为次邻域蘑菇解集B，具体表达形式如下：For the first case, in order to avoid invalid calculation, the default pushing direction of the target mushroom picking in this case is 180°; for the second case, the solution set A of the neighborhood mushrooms should be solved in turn with the individual solution set of the sub-neighborhood mushrooms β _oi performs the intersection operation, if the intersection appears to be an empty set

In the case of , it is considered that the target mushroom has failed to reciprocately push the picking, and the judgment of the picking algorithm is exited; if the intersection is not an empty set after all the intersection operations are completed, the intersection is retained and used to determine the feasible pushing direction; for the third In this case, when there are only neighboring mushrooms around the target mushroom, the neighborhood mushroom solution set obtained in the judgment of the relationship between the target mushroom and the neighboring mushrooms is used to determine the feasible driving direction; when there are only sub-neighborhood mushrooms around the target mushroom, The intersection of the individual solution sets β _oi of all the sub-neighborhood mushrooms is the final feasible pushing direction solution set after the target mushroom is interfered by all the sub-neighborhood mushrooms, which is called the sub-neighborhood mushroom solution set B, and the specific expression is as follows:

When the sub-neighborhood mushroom solution set B is an empty set, the target mushroom does not reciprocately promote the picking, which is regarded as a failure to pick. success;

Select the solution set with the largest angle range from the obtained feasible pushing direction solution set as the optimal pushing direction solution set; the optimal pushing direction solution formula:

where θ _down and θ _up are the lower and upper bounds of the solution set, respectively.

In order to realize the non-destructive picking of mushrooms, this embodiment uses the reciprocating non-destructive picking method, uses the relationship between points and polygons in graph theory to compare the relationship between the picked mushrooms and their surrounding mushrooms in the actual growth environment, and uses the improved cross-product discrimination method and The result calculated by the internal common tangent theorem is used to determine whether the picked mushrooms can be reciprocatingly pushed for picking and the optimal pushing direction when they can be picked. The damage to the current mushroom to be picked and its surrounding mushrooms can effectively improve the success rate of non-destructive mushroom picking and improve picking efficiency.

Embodiment 3

This embodiment is basically the same as the above-mentioned embodiment, and the special features are:

In this embodiment, as shown in FIGS. 4 to 7 , in the step C, considering the non-destructive picking driving direction and the optimal path, a dual-objective optimized mature mushroom picking path planning algorithm is adopted.

In order to realize the non-destructive picking of mature mushrooms, in conjunction with step B, step C specifically includes the following steps:

Step C1: Build a multi-objective optimization model for mushroom picking paths:

Step C1.1: Construct the mushroom reciprocatingly push the picking failure rate function, and calculate the picking failure rate of the picking sequence;

Here, step C1.1 specifically includes the following steps:

Step C1.1.1: Judgment of the reciprocating push of the target mushroom to pick

According to the mushroom data features obtained from the mushroom distribution map, the neighborhood mushrooms and the sub-neighborhood mushrooms around the target mushroom are searched.

The evaluation index for judging suspicious mushrooms as neighborhood mushrooms is as follows:

0＜a _0i ≤r _oi

where a _oi represents the center distance between the target mushroom and the ith mushroom; ro _oi represents the sum of the cap radius of the target and the ith mushroom; i∈(1,...,n-1), n is the total number of mushrooms .

The evaluation index for judging suspicious mushrooms as sub-neighborhood mushrooms is as follows:

ro _oi <a _0i ≤d _o +r _i

where _ri is the radius of the mushroom cap of the suspicious mushroom, and do is the pushing range of the target _mushroom . The formula is as follows:

In the formula, h _o and ro _o represent the height of the target mushroom and the radius of the mushroom cap respectively, k is the coefficient of pushing the picking range, k∈(0,1].

m为邻域蘑菇总数)，选择其中任一条向量如选

作为基向量，剩余其他向量称为邻域向量，邻域向量与基向量依次做叉乘与点乘运算，具体公式如下：Use the relationship between points and polygons to determine whether the target mushroom is surrounded by its neighbor mushrooms, simplify the target mushroom and its neighbor mushrooms into mass points, and the center coordinate of the mushroom is the position of the mass point. Except for the target mushroom, the neighboring mushrooms are connected in turn clockwise two by two, forming a closed polygon. Therefore, the problem of whether the target mushroom is surrounded by the neighboring mushrooms is abstracted into the problem of the relationship between points and polygons in graph theory. The relationship between points and polygons is judged by the improved cross-product discrimination method. Make a vector between the discriminant point p _o and each vertex p _i of the polygon

m is the total number of mushrooms in the neighborhood), choose any one of the vectors if you choose

As a basis vector, the remaining other vectors are called neighborhood vectors, and the neighborhood vectors and basis vectors perform cross product and dot product operations in turn. The specific formula is as follows:

Where f is the direction coefficient, θ _{2, i} is the vector angle with direction.

All the neighborhood vectors and the basis vectors are cross-multiplied and dot-multiplied in sequence and the results obtained are arranged in ascending order, and the maximum value θ _max and the minimum value θ _min are selected. If the absolute value of the maximum value θ _max and the absolute value of the minimum value θ _min are added, the value is greater than 180°, it indicates that the point is inside the polygon, when it is less than 180°, the point is outside the polygon, and when it is equal to 180°, the point is within the polygon on the polygon. Its specific form is as follows:

When the point is on and outside the polygon, it is considered that the target mushroom is not surrounded by its neighbor mushrooms. Record the neighborhood mushroom solution set A, the specific expression is as follows:

A=360°-(|θ _max |+|θ _min |)

Using the inner common tangent theorem to calculate the inner common tangent angle between the target mushroom and the sub-neighborhood mushroom. Assuming that the number of mushrooms in the sub-neighborhood is n, the internal common tangent theorem calculates the internal common tangent angle between the target mushroom and the sub-neighborhood mushroom as follows:

where α _oi is the inner common tangent angle formed by the target mushroom and the sub-neighborhood mushroom, a _oi is the center distance between the target mushroom and the sub-neighborhood mushroom, r _o , _ri are the target mushroom cap radius and the sub-neighborhood mushroom, respectively cap radius.

The supplementary angle β _oi of the internal common chamfering angle in the range of 0-360° is called the sub-neighborhood mushroom individual solution set. The formula for calculating the supplementary angle β _oi is as follows:

β _oi =360°-α _oi , i∈[1,...,n]

The existence of the neighborhood mushrooms and the sub-neighborhood mushrooms around the picked target mushroom is mainly divided into three situations:

1) There are no neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom;

2) There are both neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom;

3) There is only one of the neighborhood mushrooms and the sub-neighborhood mushrooms around the target mushroom.

For the first case, in order to avoid invalid calculations, the default push direction of the target mushroom picking in this case is 180°.

的情况，则视为目标蘑菇往复推动采摘失败，退出可摘算法判断。若所有取交集操作完成后交集不为空集，则保留该交集并将该交集用于确定可行推动方向。For the second case, the neighborhood mushroom solution set A should be intersected with the sub-neighborhood mushroom individual solution set β _oi in turn, if the intersection is an empty set

In the case of , it is considered that the target mushroom has failed to reciprocate the picking, and the judgment of the picking algorithm is withdrawn. If the intersection is not an empty set after all the intersection operations are completed, the intersection is retained and used to determine the feasible pushing direction.

For the third case, when there are only neighborhood mushrooms around the target mushroom, the neighborhood mushroom solution set obtained in the judgment of the relationship between the target mushroom and the neighborhood mushrooms is used to determine the feasible pushing direction; when there are only sub-neighborhoods around the target mushroom In the case of mushrooms, the intersection of the individual solution sets β _oi of all sub-neighborhood mushrooms is the final feasible pushing direction solution set after the target mushroom is interfered by all the sub-neighborhood mushrooms, which is called the sub-neighborhood mushroom solution set B, and the specific expression is as follows:

When the sub-neighborhood mushroom solution set B is an empty set, the target mushroom cannot be picked by reciprocating push, which is regarded as a failure to pick. Picking was successful.

Select the solution set with the largest angle range as the solution set of the optimal pushing direction from the obtained feasible pushing direction solution set. The optimal driving direction solution formula:

where θ _down and θ _up are the lower and upper bounds of the solution set, respectively.

Record the direction of mushroom pushing and the number of mushrooms that fail to be picked back and forth in a complete picking sequence.

Step C1.1.2: Calculate the picking failure rate of the mushroom picking sequence, and its determination form is as follows:

Among them, n _failure represents the total number of mushrooms that fail to be picked up and down in this path, and n _sum is the total number of mature mushrooms.

Step C1.2: Build a mushroom path length calculation function to calculate the path length of the picking sequence; the path length calculation function, its specific form is as follows:

Among them, n is the total number of mature mushrooms, and x and y are the horizontal and vertical coordinates of the mushrooms, respectively.

Step C1.3: establish a multi-objective optimization model according to the established mushroom reciprocating push picking failure rate function and path length function;

The multi-objective optimization model sets two optimization objectives, the first optimization objective is to minimize the picking failure rate, and the second optimization objective is to minimize the length of the picking path; the optimization objectives of the multi-objective optimization model are:

Step C2: After the model construction of Step C1 is completed, use the improved NSGA-II algorithm to solve the model, and obtain the Pareto non-dominated set after evolving the specified algebra;

Step C2.1: Chromosome coding of individual population; use a fixed-length non-repetitive integer coding method to encode each individual in the population, each chromosomal gene represents a mature mushroom at the corresponding position, and the chromosome length M is 28;

Step C2.2 Initialize the population and calculate the p _failure and dist _sum function values of each individual in the population, and use a random method to generate an initial population with a scale of 2N-1, where N=100;

Before the algorithm starts to iterate, initialize the initial population of twice the population size in a random way, and calculate the p _failure and dist _sum objective function values of each individual in the population. The purpose of this is to optimize the initial population with the size of the population through the subsequent fast non-dominated sorting and the elite strategy, so as to improve the quality of individuals in the initial population and speed up the convergence speed of the algorithm.

Step C2.3 injects the extreme value of the single-target picking path length into the initial population, uses the particle swarm algorithm to search for the individual picking sequence with the optimal path length, and injects the individual into the step C2.2 with a scale of 2N-1 (199 ) in the initial population. In the initialization population of multi-objective optimization, injecting the extreme value of each objective can speed up the convergence speed of the algorithm. Because in the optimization problem of mushroom picking path, it is easier to reduce the picking failure rate than optimizing the picking path length. For multi-objective optimization problems, only the extremum of the mushroom picking path length is injected to speed up the convergence of the algorithm.

Step C2.4: The elite strategy selects the parent population; for the initial population with a size of 2N(200) generated in step C2.3, calculate the p _failure and dist _sum objective function values of all individuals, and perform fast non-dominated sorting. Calculate the crowding degree of the individuals in each non-dominated layer; according to the elite strategy, select the better individuals to form a parent population with a size of N(100);

Step C2.5: selection, crossover and mutation operations; according to the initial population generated in step C2.4, the parent generation for reproduction is selected through the championship; 4 individuals are taken out from the initial population in a random way with replacement, according to the non-dominant method. The level and the crowding degree of individuals are selected from 4 individuals as the individual to be reproduced, and the other individual to be reproduced is also selected in this way. In order to prevent the same individual from being selected as the individual to be bred, it is required that the two individuals to be bred should be different individuals. Carry out genetic operations such as crossover and mutation according to the probability of crossover (p _c ) and mutation (p _m ) to generate offspring populations. The mutation mode of the population adopts the method of single-point mutation; The crossover probability is adaptively adjusted in stages, and the specific formula for adjusting the crossover probability in stages is as follows:

Among them, T is the maximum evolutionary algebra, R ₁ =αT, T ₂ =(1-α)T; β is the adjustment coefficient of the crossover probability in the later stage of evolution, β∈(0,1], the setting of this coefficient can ensure that the crossover probability is within Late evolution is asymptotic to a value other than 0. In this example, α is 0.382, and β is 0.4.

Step C2.6: Merge and deduplicate the parent and child populations to generate a population with a scale of 2N (200) chromosomes, and adopt the duplicate individual control strategy to remove duplicate individuals from the merged population to improve the diversity of population individuals. The specific steps for repeating the individual control strategy are as follows:

(1) Delete the duplicate individuals in the population S(t) after the parent generation P(t) and the child generation R(t) are merged;

(2) Determine whether the population size after deduplication is smaller than the population size N(100) of the population P(t), if it is smaller, continue to perform championship selection, crossover, mutation, and merge populations, and return to (1), otherwise, after deduplication Individuals in the merged population calculate p _failure and dist _sum objective function values, and perform fast non-dominated sorting and crowding degree calculation on the deduplicated merged population according to the objective function values.

Step C2.7: Adopt the elite strategy of adding the cyclic crowding sorting algorithm to select the sub-generation population, and divide the individuals in the merged population after deduplication into the non-dominated sets F ₁ , F ₂ ,...,F according to the Pareto level from high to low. _m is put into the new parent population P(t+1), when the size of the new parent population exceeds N(100), the cyclic crowding sorting algorithm is used to eliminate the individuals in F _m until the number of remaining individuals in F _m is equal to Stop when the sum of the number of individuals that have been deposited into the new parent population is equal to N;

Here, step C2.7 specifically includes the following steps:

Step C2.7.1: Set the crowding degree of the two boundary individuals 1 _d and n _d in the non-dominated hierarchy as inf, and the calculation formula of the crowding degree is as follows:

分别为当前层级中的第m个目标函数的最大与最小值；where n is the number of individuals in the current level, f _m (i) represents the m-th objective function value of the i-th chromosome in the current level,

are the maximum and minimum values of the mth objective function in the current level, respectively;

Sort the individuals in the non-dominated hierarchy in descending order of crowding degree, and delete the individual with the least crowding degree;

Step C2.7.2: Recalculate the crowding degree of the remaining individuals in the hierarchy, and delete the individual with the smallest crowding degree again;

Step C2.7.3: Iterate sequentially until the specified number of solutions remain.

Step C2.8: Repeat steps C2.5 to C2.7, and jump out of the current loop if the current genetic algebra reaches the specified target genetic algebra.

Step C2.9: According to the weight coefficient, select the optimal solution from the non-dominated solution set and output it.

In order to realize the nondestructive picking of mushrooms, the method for reciprocating nondestructive picking in this embodiment, especially for dense mushrooms, uses a trajectory planning method suitable for determining the optimal driving direction and picking sequence for mushroom reciprocating nondestructive picking. In order to improve the optimization efficiency of the algorithm, methods such as improving the population quality of the initial population and injecting the single-objective extremum into the initial population are adopted, which effectively improves the convergence of the algorithm and the distribution of the solution set, so that the algorithm can converge to the real Pareto frontier at the same time. , to obtain the approximate Pareto optimal solution set with more uniform distribution and wider coverage. The method is not limited to the picking of edible fungi such as mushrooms and straw mushrooms, but is also applicable to the directional picking and trajectory planning of other globular-like cluster fruits.

In the above-mentioned embodiment of the present invention, the method for reciprocating nondestructive picking of mushrooms and the path planning method thereof, firstly utilize the reciprocating propulsion picking method in which the mushrooms are separated from the culture medium; then implement the method for determining the optimal driving direction for nondestructive picking of mature mushrooms; Optimal path, using dual-objective optimization of mature mushroom picking path planning algorithm; the above embodiment adopts the specific form of reciprocating pushing picking, and the above embodiment determines the optimal pushing direction for the reciprocating pushing picking, this kind of reciprocating with the optimal pushing direction Pushing to pick can greatly increase the success rate of mushroom picking without damage. And the mushroom picking path problem is optimized by the above embodiment method. The above-mentioned embodiments of the present invention realize the optimization of the multi-objective problem after the combination of the mushroom reciprocating picking and the optimal path, and through the determination of the non-destructive picking direction and the picking path planning, it is very good to avoid the current mushrooms to be picked and its characteristics when picking mushrooms. The damage of the surrounding mushrooms can effectively improve the success rate of non-destructive mushroom picking and improve the picking efficiency.

The above-mentioned embodiments are only intended to illustrate the technical concept and characteristics of the present invention, and the purpose thereof is to enable those who are familiar with the art to understand the content of the present invention and implement them accordingly, and cannot limit the protection scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be included within the protection scope of the present invention.

Claims (7)

1. a mushroom reciprocatingly promotes a lossless picking method and a path planning method thereof, is characterized in that, comprises the steps: A. Design the method of reciprocating and promoting the separation of mushrooms and culture medium; B. The method for determining the optimal driving direction for non-destructive picking of mature mushrooms; C. A dual-objective optimal mature mushroom picking path planning algorithm considering the non-destructive picking driving direction and optimal path. 2. a kind of mushroom reciprocating non-destructive picking method and path planning method thereof according to claim 1, is characterized in that, described step A specifically comprises: Step A1: Use the end effector to clamp or adsorb the mushroom; Step A2: Push the mushroom back and forth along the optimal pushing direction of the mushroom to separate the mushroom from the culture medium. 3. a kind of mushroom reciprocating promoting non-destructive picking method and path planning method thereof according to claim 1, is characterized in that, in described step A2, the optimal driving direction of mature mushroom is based on the optimal driving direction of step B mature mushroom non-destructive picking. Determined by a method, the method specifically includes: Step B1: Obtain mushroom center coordinates, mushroom cap radius and mushroom height data in the mushroom distribution image, and save the above data as mushroom data features; Step B2: Use the mushroom data features to search for the neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom; the mushrooms that intersect or are tangent to the target mushroom are called neighborhood mushrooms, which are far away from the target mushroom but still reciprocate the target mushroom. Mushrooms that push space to cause interference are called subneighborhood mushrooms; The evaluation index for judging suspicious mushrooms as neighborhood mushrooms is as follows: 0＜a _oi ≤r _oi where a _oi represents the center distance between the target mushroom and the ith suspicious mushroom; ro _oi represents the sum of the cap radius of the target and the ith suspicious mushroom; i∈[1,...,n-1], n is total number of mushrooms; The evaluation index for judging suspicious mushrooms as sub-neighborhood mushrooms is as follows: r _oi <a _oi ≤d _o +r _i where _ri is the radius of the mushroom cap of the suspicious mushroom, and do is the reciprocating push range of the target _mushroom . The formula is as follows:

In the formula, h _o and r _o represent the height of the target mushroom and the radius of the mushroom cap respectively, k is the coefficient of pushing the picking range, k ∈ (0, 1]; Step B3: Calculate the neighborhood mushroom solution set, the sub-neighborhood mushroom solution set, and determine the optimal pushing direction of the mushroom; whether the target mushroom is surrounded by its neighborhood mushrooms is analogous to the relationship between points and polygons, and the improved cross-product discrimination method is used to determine Judging the relationship between points and polygons to determine whether the target mushroom is surrounded by its neighbor mushrooms; assuming that the number of neighbor mushrooms is m, make a vector between the target mushroom particle p _o and each neighbor mushroom particle p _i

Among them, i∈[1, 2, 3, ..., m], m is the total number of mushrooms in the neighborhood; choose any one of the vectors if you choose

As a basis vector, the remaining other vectors are called neighborhood vectors, and the neighborhood vectors and basis vectors perform cross product and dot product operations in turn. The specific formula is as follows:

Where f is the direction coefficient, θ _{2, i} is the vector angle with direction; The results obtained after all the neighborhood vectors and the basis vectors are cross-multiplied and dot-multiplied in turn are arranged in ascending order, and the maximum value θ _max and the minimum value θ _min in the results are selected; the specific formula for judging the relationship between the point and the polygon is as follows:

When the point is on and outside the polygon, it is considered that the target mushroom is not surrounded by its neighbor mushrooms; the neighborhood mushroom solution set A is recorded, and the specific expression is as follows: A=360°-(|θ _max |+|θ _min |) Using the inner common tangent theorem to calculate the inner common tangent angle between the target mushroom and a certain sub-neighborhood mushroom is as follows:

where n is the number of mushrooms in the sub-neighborhood, α _oi is the inner common tangent angle formed by the target mushroom and the sub-neighborhood mushroom, a _oi is the center distance between the target mushroom and the sub-neighborhood mushroom, r _o , _ri are the target mushroom, respectively Mushroom cap radius and sub-neighborhood mushroom cap radius; The supplementary angle β _oi of the internal common tangent angle within the range of 0-360° is called the sub-neighborhood mushroom individual solution set; the calculation formula of the supplementary angle β _oi is as follows: β _oi =360°-α _oi , i∈[1,...,n] The existence of the neighborhood mushrooms and the sub-neighborhood mushrooms around the picked target mushroom is mainly divided into three situations: 1) There are no neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom; 2) There are both neighborhood mushrooms and sub-neighborhood mushrooms around the target mushroom; 3) There is only one of the neighborhood mushrooms and the sub-neighborhood mushrooms around the target mushroom; For the first case, in order to avoid invalid calculation, the default pushing direction of the target mushroom picking in this case is 180°; for the second case, the solution set A of the neighborhood mushrooms should be solved in turn with the individual solution set of the sub-neighborhood mushrooms β _oi performs the intersection operation, if the intersection appears to be an empty set

In the case of , it is considered that the target mushroom has failed to reciprocately push the picking, and the judgment of the picking algorithm is exited; if the intersection is not an empty set after all the intersection operations are completed, the intersection is retained and used to determine the feasible pushing direction; for the third In this case, when there are only neighboring mushrooms around the target mushroom, the neighborhood mushroom solution set obtained in the judgment of the relationship between the target mushroom and the neighboring mushrooms is used to determine the feasible pushing direction; when there are only sub-neighborhood mushrooms around the target mushroom, The intersection of all sub-neighborhood mushroom individual solution sets β _oi is the final feasible pushing direction solution set after the target mushroom is interfered by all sub-neighborhood mushrooms, which is called sub-neighborhood mushroom solution set B, and the specific expression is as follows:

When the sub-neighborhood mushroom solution set B is an empty set, the target mushroom does not reciprocately promote the picking, which is regarded as a failure to pick. success; Select the solution set with the largest angle range from the obtained feasible pushing direction solution set as the optimal pushing direction solution set; the optimal pushing direction solution formula:

where θ _down and θ _up are the lower and upper bounds of the solution set, respectively. 4. a kind of mushroom reciprocating non-destructive picking method and path planning method thereof according to claim 1, is characterized in that, especially for the actual situation of mushroom gathering growth, in order to realize the non-destructive picking of mature mushrooms, in conjunction with step B, step C specifically includes the following steps: Step C1: Construct a multi-objective optimization model of mushrooms reciprocatingly push the picking path: Step C2: After the model construction of Step C1 is completed, use the improved NSGA-II algorithm to solve the model to obtain the Pareto non-dominated set; Step C3: According to the result obtained in Step C2, the obtained Pareto non-dominated set is sorted based on the weights. 5. A kind of mushroom reciprocating non-destructive picking method and path planning method thereof according to claim 4, is characterized in that, described step C1 specifically comprises the following steps: Step C1.1: Construct the mushroom reciprocatingly push the picking failure rate function, and calculate the picking failure rate of the picking sequence; Use step B to search and record the total number of failed mushrooms in a complete picking sequence; calculate the picking failure rate of the mushroom picking sequence, and its determination form is as follows:

Among them, n _failure represents the total number of mature mushrooms that fail to reciprocate in the picking path, and n _sum is the total number of mature mushrooms; Step C1.2: Build a mushroom path length calculation function to calculate the path length of the picking sequence; the specific form of the path length calculation function is as follows:

Among them, n is the total number of mature mushrooms, and x and y are the horizontal and vertical coordinates of the mushrooms, respectively; Step C1.3: establish a multi-objective optimization model according to the established mushroom reciprocating push-picking failure rate function and the path length function; the optimization objectives of the multi-objective optimization model are:

The multi-objective optimization model sets two optimization objectives, the first objective is to minimize the picking failure rate, and the second objective is to minimize the picking path length. 6. a kind of mushroom reciprocatingly pushes the non-destructive picking method and path planning method thereof according to claim 4, it is characterized in that, in described step C2, specifically comprises the following steps to multi-objective optimization model solving process based on NSGA-II method: Step C2.1: Chromosome coding of population individuals; Step C2.2: Initialize the population and calculate the P _failure and dist _sum function values of individuals in the population; Step C2.3: Inject the extreme value of the single-target picking path length into the initial population; Step C2.4: The elite strategy selects the parent population; Step C2.5: selection, crossover and mutation operations; Step C2.6: merge and deduplicate the parent-child population; Step C2.7: Select the offspring population by the elite strategy of adding the cyclic crowding sorting algorithm; Step C2.8: repeating the steps C2.5 to C2.7 to the target genetic algebra and then jumping out of the current loop; Step C2.9: According to the weight coefficient, select the optimal solution from the non-dominated solution set and output it. 7. a kind of mushroom reciprocating promoting non-destructive picking method and path planning method thereof according to claim 6, is characterized in that, in described step C2.7, the main steps of cyclic crowding sorting algorithm are as follows: Step C2.7.1: Set the crowding degree of the two boundary individuals 1 _d and n _d in the non-dominated hierarchy as inf, sort the individuals in the non-dominated hierarchy according to the crowding degree from high to low, and delete the individual with the smallest crowding degree; Step C2.7.2: Recalculate the crowding degree of the remaining individuals in the hierarchy, and delete the individual with the smallest crowding degree again; Step C2.7.3: Iterate sequentially until the specified number of solutions remain.

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