CN114128695A - Crawler-type autonomous accurate variable air-assisted spraying robot structure and path planning and variable spraying method thereof - Google Patents

Crawler-type autonomous accurate variable air-assisted spraying robot structure and path planning and variable spraying method thereof Download PDF

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CN114128695A
CN114128695A CN202111330957.9A CN202111330957A CN114128695A CN 114128695 A CN114128695 A CN 114128695A CN 202111330957 A CN202111330957 A CN 202111330957A CN 114128695 A CN114128695 A CN 114128695A
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spraying
variable
laser radar
crawler
distance
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CN114128695B (en
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刘慧�
杜志鹏
段云鹏
沈跃
张世义
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Jiangsu University
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
    • A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
    • A01M7/005—Special arrangements or adaptations of the spraying or distributing parts, e.g. adaptations or mounting of the spray booms, mounting of the nozzles, protection shields
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
    • A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
    • A01M7/0025—Mechanical sprayers
    • A01M7/0032—Pressure sprayers
    • A01M7/0042—Field sprayers, e.g. self-propelled, drawn or tractor-mounted
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
    • A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
    • A01M7/0089—Regulating or controlling systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D55/00—Endless track vehicles
    • B62D55/06—Endless track vehicles with tracks without ground wheels

Abstract

The invention discloses a crawler-type autonomous accurate variable air supply spraying robot structure and a path planning and variable spraying method thereof. A bracket 2 is fixed on the crawler-type mobile chassis 1, and a battery 3, a central processing unit 4, a water pump 5 and a water tank 7 are fixed on the bracket 2; a variable spraying system 6 is arranged on the bracket 2; the variable spraying system consists of a fan 61, a duct 62 and a spray head 63. The data acquisition module 8 is composed of a navigation module 81 and a laser radar 82. The central processing unit 4 is used for processing the data of the data acquisition module 8 and controlling the advancing speed and the advancing direction of the crawler moving chassis 1, the spraying amount and the spraying time of the variable spraying system 6 and the air volume and the air speed of the fan. The invention realizes accurate variable spray with variable flow and variable air volume. The penetrability of the spray and the spraying efficiency are improved.

Description

Crawler-type autonomous accurate variable air-assisted spraying robot structure and path planning and variable spraying method thereof
Technical Field
The invention relates to a spraying robot, in particular to a crawler-type autonomous precise variable air-assisted spraying robot structure, a path planning method and a variable spraying method thereof.
Background
The pesticide spraying operation is an important production link in agricultural production, and in order to ensure the product quality, the pesticide needs to be sprayed for many times in the growth period of crops; most of the automatic pesticide spraying devices applied at present can only realize quantitative pesticide spraying but cannot realize precise variable pesticide spraying, and the phenomena of pollution waste such as heavy spraying, missed spraying, mistaken spraying and the like exist. Aiming at the problem, an independent spraying robot capable of realizing precise variable spraying is designed.
Disclosure of Invention
The invention aims to design a crawler-type autonomous accurate variable air-assisted spraying robot, which is suitable for spraying operation and can realize the detection of the characteristics of existence, size, shape, density and the like of a plant target with high precision, so that the proper, uniform and accurate target-ground variable spraying is carried out, the pesticide use efficiency is improved, the pesticide cost is saved, and the pollution to the environment and the potential safety hazard of an operator are reduced.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a crawler-type automatic accurate variable air-assisted spraying robot structure comprises a crawler moving chassis (1), a system support (2), a variable spraying system (6), a battery (3), a water pump (5), a water tank (7) and a data acquisition module (8); a system support (2) is fixed on the mobile chassis (1) of the tracked vehicle, and a battery (3), a central processing unit (4), a water pump (5), a variable spraying system (6), a water tank (7) and a data acquisition module (8) are fixed on the system support (2); the variable spraying system (6) consists of a fan (61), a duct (62) and a nozzle (63) of a spray rod arranged in the vertical direction, the fan (61) and the nozzle (63) are arranged in the duct (62), and the fan (61) is used for adjusting the air speed and the air volume in real time to improve the penetrability and the spraying range of liquid medicine spraying; the data acquisition module (8) consists of a navigation module (81) and a laser radar (82); the liquid medicine is stored in the water tank (7), is sequentially connected with the water pump (5) and the nozzle (63) and is sprayed to the plant canopy, and the battery (3) is used for supplying power to the water pump (5), the central processing unit (4) and the fan (61); the central processing unit (4) is electrically connected with the data acquisition module (8) and is used for processing the data acquired by the data acquisition module (8), processing the data, planning a path and providing calculation power for a target plant volume detection algorithm.
Furthermore, tracked vehicle chassis (1) is used for turning to and gesture, the position adjustment of spraying robot structure to carry on the platform for operating system provides.
Further, the system support (2) is divided into an upper layer and a lower layer, the lower layer is fixed on the mobile chassis (1) of the crawler through bolts and nuts, and a variable spraying system (3), a battery (4) and a water tank (7) are mounted on the lower layer; the upper layer and the lower layer are separated by a flat plate to prevent water, and the upper layer is provided with a variable spraying system (6) and a data acquisition module (8); the system support (2) is formed by connecting aluminum profiles and aluminum plates through metal foot pieces, and the weight of the platform is reduced while the functions of supporting and bearing the whole system are provided.
Furthermore, the water tank (7) is fixed on the system support (2), the water pumps (5) are fixed on the lower layer of the system support through bolts and nuts, the number of the water pumps (5) is 5, wherein 1 large water pump is provided with 4 small water pumps, the large water pump is used for supplying water to four small water pumps, and the small water pumps are used for controlling the flow of two nozzles (63) which are respectively connected and are arranged in the same duct; the nozzle (63) is fixed on a hollow rod in the duct (62) through a pipe clamp; the air-conditioning system is characterized in that the number of the nozzles (63) is 8, 2 are arranged in each of 4 ducts (62), 4 fans (61) are arranged, the number of the ducts (62) is 2, the outside of the ducts is fixed on the system support (2) through carbon tubes (66) penetrating through the 2 ducts and is equidistantly arranged in the vertical direction, 1 fan (61) is fixed in each duct (62), and the control of the air speed and the air volume is realized by controlling the rotating speed of the fan (61).
Furthermore, battery (3) are the lithium cell, are fixed in system support (2) lower floor with it, provide electric power for water pump (5), central processing unit (4), fan (61) and data acquisition module (8).
Further, the data acquisition module (8) consists of 3 navigation modules (81) and a laser radar (82); the laser radar (82) is divided into a front laser radar (821), an upper laser radar (822) and a rear laser radar (823), the front laser radar (821) and the upper laser radar (822) are fixed at the front end of the system support (2) through a front fixing plate, and the rear laser radar (823) is fixed at the rear end of the system support (2) through a rear fixing plate;
the front laser radar (821) is horizontally placed upside down and used for detecting an obstacle in front of the robot; the upper laser radar (822) is vertically arranged and used for scanning the volume of plant canopies on two sides of the robot; and the rear laser radar (823) is horizontally arranged and used for detecting obstacles behind the robot.
Furthermore, the central processing unit (4) is fixed on the lower layer of the system bracket (2); the central processing unit (4) is 8-corearmalarmv 8.264-bitcpu, and is electrically connected with the fan (61) and the water pump (5) through the driving module.
A path planning method for a crawler-type autonomous precise variable air-assisted spraying robot structure comprises the following steps:
step 1, a navigation module (81) is used for obtaining position information of longitude and latitude and height of a spraying robot and posture information of the spraying robot, a pure tracking algorithm is used for tracking a path planned according to actual conditions of an orchard according to the pure tracking algorithm, and if g is a path point on the path, ldThe distance from the point C of the central point to the point g, namely the forward looking distance is represented; the wheel track is l, the turning radius is R, and the instantaneous linear speed of the crawler is VcInstantaneous linear velocity V of the left wheellInstantaneous linear velocity V of the right wheelrInstantaneous angular velocity of ωcAlpha represents the included angle between the current vehicle body posture and the target point g;
step 2, the sine formula can be used for obtaining:
Figure BDA0003348823430000031
step 3, according to the curvature formula, the curvature can be expressed as:
Figure BDA0003348823430000032
and 4, obtaining a turning radius R according to the crawler motion model:
Figure BDA0003348823430000033
the two formulas are combined:
Figure BDA0003348823430000034
step 5, controlling VcAnd ωcTo control the track of movement of the tracked vehicle, k being a coefficient representing the forward-looking distance as a linear function of the longitudinal speed of the vehicle, i.e.
ld＝kVc
Step 6, constraining the forward looking distance by using the maximum and minimum forward looking distances, wherein the larger forward looking distance means that the track is more smoothly tracked, and the smaller forward looking distance enables the tracking to be more accurate, but simultaneously brings oscillation; in order to better determine the forward viewing distance, a PSO algorithm (particle swarm optimization) is introduced, a variable theta is introduced as a heading error and represents an included angle between the pose and the track of the vehicle body, and a transverse error e is adopted in the PSO algorithmlThe sum of the root mean square and the heading error theta is used as a main reference for particle swarm optimization, and the transverse error elThe evaluation parameters of (a) were:
Figure BDA0003348823430000035
the evaluation parameters of the course error theta are as follows:
Figure BDA0003348823430000036
designing fitness function based on the two formulas
F＝λF1+(1-λ)F2
N is working duration, M is sampling time, lambda is weight, the designed fitness function, F can adjust the decision weight of the transverse error and the course error according to the transverse error, and the method has good adaptability, and in a pure tracking model based on a PSO algorithm, when the transverse error e islWhen larger, the forward looking distance ldMainly by transverse error elMaking a decision; when the lateral error ldSmaller, forward looking distance ldThe decision is mainly made through a course error theta, so the design weight is as follows:
λ＝loga((el+1)/U)(U≥(elmax+1)/a)
where α is the base of the weighting function, U is the tuning parameter, elmaxIs the maximum lateral error.
A variable spraying method of a crawler-type autonomous accurate variable air-assisted spraying robot structure comprises the following steps:
step A, scanning a plant canopy by using a laser radar, performing variable spray control by processing canopy data, and performing a target plant volume detection method:
go up laser radar (822) upward scanning, carry out cluster processing with the point cloud data of gathering, the plant trunk is fitted, and distance data processing according to laser radar returns obtains the distance l of plant trunk to laser radar, and wherein laser radar's angular resolution is 0.225, and there is following data relation between two adjacent point cloud data:
Figure BDA0003348823430000041
wherein beta isiAnd betai+1Angles corresponding to adjacent point clouds,/iAnd li+1Distance values, d, collected for two adjacent point cloudsiAnd di+1The distance between the outer side of the crown and the trunk is h, and the distance between two adjacent point clouds in the vertical direction is h;
step B, calculating the area which is approximately trapezoidal and covers between two adjacent point clouds and the trunk:
Figure BDA0003348823430000042
and according to the scanning frequency of the two-dimensional laser radar of 25Hz, the volume V of the plant canopy between two adjacent clouds can be calculatedi′：
Figure BDA0003348823430000043
Wherein, Δ t is the time required for scanning a frame, W is the width of scanning of a frame of laser radar, and v is the advancing speed of the robot;
step C, controlling the flow and the air volume in the variable spraying system (6) according to the volume of the plant canopy, and considering the distance between the laser radar (822) and the spray head, so that the variable spraying system (6) needs to be controlled to carry out delay spraying on the spraying control information obtained by processing, and the delay time t can be obtained:
Figure BDA0003348823430000044
wherein the distance between the upper laser radar (822) and the nozzle (63) in the advancing direction of the robot is x, and the advancing speed of the robot is v.
The traditional manual pesticide spraying has the defects of poor pesticide spraying uniformity, heavy spraying, missed spraying, mistaken spraying and the like in the operation process, and has low operation efficiency and low pesticide utilization rate, and operating personnel are easy to be poisoned in the operation environment. The traditional mechanical operation is easy to damage plants, the failure rate is high, and the problems that the pesticide dosage is insufficient in areas with serious plant diseases and insect pests and the like are easily caused. Aiming at the problems, the scheme has the characteristics of accuracy, high efficiency, green and environmental protection. The convenient remote monitoring and control interaction interface of the mobile phone end can really realize unmanned operation; the environmental adaptability of the robot is improved by the chassis of the tracked vehicle; the variable spraying system realizes accurate variable air supply and variable spraying, and improves the efficiency of spraying the medicine, the utilization rate of the medicine and the penetrability of the medicine.
The scheme adopted by the invention is the autonomous accurate variable air-assisted spraying robot taking the crawler chassis as the moving platform, is mainly applied to orchards and is suitable for most roads.
Drawings
FIG. 1 is a front left view of the present invention;
FIG. 2 is a rear left view of the present invention;
FIG. 3 is a schematic diagram of a variable spray system according to the present invention;
FIG. 4 is a schematic diagram of a front-side two-dimensional lidar structure;
FIG. 5 is a schematic diagram of a rear-side two-dimensional lidar structure;
FIG. 6 is a schematic diagram of path planning;
FIG. 7 is a schematic diagram of plant target volume calculation;
wherein: 1-a crawler moving chassis; 2-a system support; 3-a battery; 4-a central processing unit; 5, a water pump; 6-variable spray system; 61-a fan; 62-duct; 63-a spray head; 64-a pipe clamp; 65-a fixing member; 66-carbon tubes; 7-a water tank; 8-a data acquisition module; 81-a navigation module; 82-laser radar; 821-front lidar; 822-upper laser radar; 823-rear lidar; 841-front fixed plate; 842-rear fixed plate.
Detailed Description
The invention relates to an autonomous precise variable air-assisted spraying robot, which is specifically described below by combining a schematic diagram.
The invention discloses a path planning method for a crawler-type autonomous precise variable air-assisted spraying robot structure, which comprises the following steps:
step 1, a navigation module (81) is used for obtaining position information of longitude and latitude and height of a spraying robot and posture information of the spraying robot, a pure tracking algorithm is used for tracking a path planned according to actual conditions of an orchard according to the pure tracking algorithm, and if g is a path point on the path, ldThe distance from the point C of the central point to the point g, namely the forward looking distance is represented; the wheel track is l, the turning radius is R, and the instantaneous linear speed of the crawler is VcInstantaneous linear velocity V of the left wheellInstantaneous linear velocity V of the right wheelrInstantaneous angular velocity of ωcAlpha represents the included angle between the current vehicle body posture and the target point g;
step 2, the sine formula can be used for obtaining:
Figure BDA0003348823430000051
step 3, according to the curvature formula, the curvature can be expressed as:
Figure BDA0003348823430000061
and 4, obtaining a turning radius R according to the crawler motion model:
Figure BDA0003348823430000062
the two formulas are combined:
Figure BDA0003348823430000063
step 5, controlling VcAnd ωcTo control the track of movement of the tracked vehicle, k being a coefficient representing the forward-looking distance as a linear function of the longitudinal speed of the vehicle, i.e.
ld＝kVc
Step 6, constraining the forward looking distance by using the maximum and minimum forward looking distances, wherein the larger forward looking distance means that the track is more smoothly tracked, and the smaller forward looking distance enables the tracking to be more accurate, but simultaneously brings oscillation; in order to better determine the forward viewing distance, a PSO algorithm (particle swarm optimization) is introduced, a variable theta is introduced as a heading error and represents an included angle between the pose and the track of the vehicle body, and a transverse error e is adopted in the PSO algorithmlThe sum of the root mean square and the heading error theta is used as a main reference for particle swarm optimization, and the transverse error elThe evaluation parameters of (a) were:
Figure BDA0003348823430000064
the evaluation parameters of the course error theta are as follows:
Figure BDA0003348823430000065
designing fitness function based on the two formulas
F＝λF1+(1-λ)F2
Wherein N is the length of the working time,m is sampling time, lambda is weight, the designed fitness function, F can adjust the decision weight of the transverse error and the course error according to the transverse error, the method has good adaptability, and when the transverse error e is in a pure tracking model based on a PSO algorithm, the method has good adaptabilitylWhen larger, the forward looking distance ldMainly by transverse error elMaking a decision; when the lateral error ldSmaller, forward looking distance ldThe decision is mainly made through a course error theta, so the design weight is as follows:
λ＝loga((el+1)/U)(U≥(elmax+1)/a)
where α is the base of the weighting function, U is the tuning parameter, elmaxIs the maximum lateral error.
The invention discloses a variable spraying method of a crawler-type autonomous accurate variable air-assisted spraying robot structure, which comprises the following steps:
step A, scanning a plant canopy by using a laser radar, performing variable spray control by processing canopy data, and performing a target plant volume detection method:
go up laser radar (822) upward scanning, carry out cluster processing with the point cloud data of gathering, the plant trunk is fitted, and distance data processing according to laser radar returns obtains the distance l of plant trunk to laser radar, and wherein laser radar's angular resolution is 0.225, and there is following data relation between two adjacent point cloud data:
Figure BDA0003348823430000071
wherein beta isiAnd betai+1Angles corresponding to adjacent point clouds,/iAnd li+1Distance values, d, collected for two adjacent point cloudsiAnd di+1The distance between the outer side of the crown and the trunk is h, and the distance between two adjacent point clouds in the vertical direction is h;
step B, calculating the area which is approximately trapezoidal and covers between two adjacent point clouds and the trunk:
Figure BDA0003348823430000072
and according to the scanning frequency of the two-dimensional laser radar of 25Hz, the volume V of the plant canopy between two adjacent clouds can be calculatedi′：
Figure BDA0003348823430000073
Wherein, Δ t is the time required for scanning a frame, W is the width of scanning of a frame of laser radar, and v is the advancing speed of the robot;
step C, controlling the flow and the air volume in the variable spraying system (6) according to the volume of the plant canopy, and considering the distance between the laser radar (822) and the spray head, so that the variable spraying system (6) needs to be controlled to carry out delay spraying on the spraying control information obtained by processing, and the delay time t can be obtained:
Figure BDA0003348823430000074
wherein the distance between the upper laser radar (822) and the nozzle (63) in the advancing direction of the robot is x, and the advancing speed of the robot is v.
As shown in fig. 1 and 2, an autonomous precise variable air-assisted spraying robot has a left front and a left rear structural view, and is mainly characterized in that an integral frame is composed of a tracked vehicle moving chassis (1), a system bracket (2), a battery (3), a central processing unit (4), a water pump (5), a variable spraying system (6), a water tank (7) and a data acquisition module (8), and comprises the following parts: 81-a navigation module; 82-laser radar; 821-front lidar; 822-upper laser radar; 823-rear lidar.
The tracked vehicle moving chassis (1) adopts PID control to control the movement track, and the rotation speed of two tracks of the tracked vehicle moving chassis is controlled to realize steering and speed control, so that the movement track of the tracked vehicle moving chassis is further controlled. The crawler has strong trafficability and can turn on the spot, and can work in the environment with poor road conditions and tight space. The system bracket (2) is formed by connecting aluminum profiles and aluminum plates through metal foot pieces, and the weight of the robot is greatly reduced on the premise of ensuring the strength of the bracket. And the central processing unit (4) processes the point cloud data obtained by real-time scanning of the front laser radar (821) and the rear laser radar (823), so as to realize real-time obstacle avoidance and local path planning. Meanwhile, the two laser radars (821 and 823) are also used for positioning, obstacle avoidance and attitude adjustment of the robot; a vertically positioned lidar (822) assists variable spray. The variable spraying system (6) realizes accurate variable spraying with variable flow and variable air volume according to the volume and the distance of the canopy of the plant in front of the nozzle.
As shown in fig. 3, the structure of the variable spraying system is schematically shown, the inside of the duct (62) is fixed with a motor through a carbon tube (66) penetrating the duct and a tube clamp on the rod, and the duct is fixed on the system bracket (2) through a tube clamp (64) on the carbon tube (66) and a fixing piece (65) fixed on the aluminum profile; the fan (61) controls the air speed and the air volume through electric regulation. The nozzle (63) is fixed to the rod that runs through the duct by its own pipe clamp. The flow rate of the nozzle (63) is controlled by controlling the pumping amount of the water pump (5). The variable spraying systems (6) comprise 4 groups, and the left side and the right side of the robot comprise 2 groups respectively.
As shown in fig. 4 and 5, the front two-dimensional lidar schematic diagram and the rear two-dimensional lidar schematic diagram are respectively a front two-dimensional lidar (821) which scans forwards and an upper lidar (822) which scans upwards, and the rear two-dimensional lidar (823) which scans backwards is fixed on the laser plate through bolts and nuts and then fixed at the front end and the rear end of the system bracket (2) through nuts. The Avena inflata 8-corearmalmearmv 8.264-bitcpu processor is used as a central processing unit (4), and the computing power and storage requirements required by the point cloud computing and navigation planning algorithm of three two-dimensional laser radars can be completely met. The robot takes a mainstream robot software framework ros (robot Operation system) as a current development environment. The method has the advantages of a crown layer point cloud volume processing algorithm developed based on the ROS and an aviation programming set ROS system, and has the characteristic of modularization. The scheme fully utilizes the front laser and the rear laser (821 and 823) and is simultaneously used for positioning, pose estimation and local track planning. The spraying robot adjusts a driving route and self posture in real time according to data of the front and the rear two-dimensional laser radars (821 and 823), so that the spraying robot keeps a certain safe distance and spraying distance when meeting plants, and meanwhile, the obstacle avoidance function in the operation process can be realized.
As shown in FIG. 6, g is a path point on the path, ldThe distance from the center point C to the point g, i.e., the forward looking distance, is shown. The turning radius is R. The instantaneous linear speed of the tracked vehicle is Vc，VlAt a left wheel linear velocity, VrIs the right wheel linear velocity and the instantaneous angular velocity is omegac. Alpha represents the angle between the current body attitude and the target point g.
As shown in FIG. 7, l is the distance from the plant trunk to the laser radar, βiAnd betai+1Angles corresponding to adjacent point clouds,/iAnd li+1Distance values, d, collected for two adjacent point cloudsiAnd di+1Is the distance between the outer side of the crown and the trunk, h is the distance between two adjacent point clouds in the vertical direction, SiThe area of the covering between two adjacent point clouds and the trunk is similar to a trapezoid.

Claims (9)

1. A crawler-type automatic accurate variable air-assisted spraying robot structure is characterized by comprising a crawler moving chassis (1), a system bracket (2), a variable spraying system (6), a battery (3), a water pump (5), a water tank (7) and a data acquisition module (8); a system support (2) is fixed on the mobile chassis (1) of the tracked vehicle, and a battery (3), a central processing unit (4), a water pump (5), a variable spraying system (6), a water tank (7) and a data acquisition module (8) are fixed on the system support (2); the variable spraying system (6) consists of a fan (61), a duct (62) and a nozzle (63) of a spray rod arranged in the vertical direction, the fan (61) and the nozzle (63) are arranged in the duct (62), and the fan (61) is used for adjusting the air speed and the air volume in real time to improve the penetrability and the spraying range of liquid medicine spraying; the data acquisition module (8) consists of a navigation module (81) and a laser radar (82); the liquid medicine is stored in the water tank (7), is sequentially connected with the water pump (5) and the nozzle (63) and is sprayed to the plant canopy, and the battery (3) is used for supplying power to the water pump (5), the central processing unit (4) and the fan (61); the central processing unit (4) is electrically connected with the data acquisition module (8) and is used for processing the data acquired by the data acquisition module (8), processing the data, planning a path and providing calculation power for a target plant volume detection algorithm.
2. The crawler-type automatic accurate variable air-assisted spraying robot structure as claimed in claim 1, wherein the crawler chassis (1) is used for steering, attitude and position adjustment of the spraying robot structure and provides a carrying platform for a working system.
3. The structure of the crawler-type automatic accurate variable air-assisted spraying robot according to claim 1, wherein the system bracket (2) is divided into an upper layer and a lower layer, the lower layer is fixed on a moving chassis (1) of the crawler vehicle by bolts and nuts, and is provided with a variable spraying system (3), a battery (4) and a water tank (7); the upper layer and the lower layer are separated by a flat plate to prevent water, and the upper layer is provided with a variable spraying system (6) and a data acquisition module (8); the system support (2) is formed by connecting aluminum profiles and aluminum plates through metal foot pieces, and the weight of the platform is reduced while the functions of supporting and bearing the whole system are provided.
4. The crawler-type automatic accurate variable air-assisted spraying robot structure according to claim 1, characterized in that the water tank (7) is fixed on the system support (2), the water pumps (5) are fixed on the lower layer of the system support by bolts and nuts, the number of the water pumps (5) is 5, wherein, 1 large water pump is provided with 4 small water pumps, the large water pump is used for supplying water to four small water pumps, and the small water pumps are used for controlling the flow of two nozzles (63) which are respectively connected and arranged in the same duct; the nozzle (63) is fixed on a hollow rod in the duct (62) through a pipe clamp; the air-conditioning system is characterized in that the number of the nozzles (63) is 8, 2 are arranged in each of 4 ducts (62), 4 fans (61) are arranged, the number of the ducts (62) is 2, the outside of the ducts is fixed on the system support (2) through carbon tubes (66) penetrating through the 2 ducts and is equidistantly arranged in the vertical direction, 1 fan (61) is fixed in each duct (62), and the control of the air speed and the air volume is realized by controlling the rotating speed of the fan (61).
5. The structure of the crawler-type automatic accurate variable air supply spraying robot according to claim 1, wherein the battery (3) is a lithium battery, is fixed on the lower layer of the system bracket (2), and provides power for the water pump (5), the central processing unit (4), the fan (61) and the data acquisition module (8).
6. A tracked autonomous precise variable air-assisted spray robot structure according to claim 1, characterized in that the data acquisition module (8) consists of 3 navigation modules (81) and a laser radar (82); the laser radar (82) is divided into a front laser radar (821), an upper laser radar (822) and a rear laser radar (823), the front laser radar (821) and the upper laser radar (822) are fixed at the front end of the system support (2) through a front fixing plate, and the rear laser radar (823) is fixed at the rear end of the system support (2) through a rear fixing plate;
the front laser radar (821) is horizontally placed upside down and used for detecting an obstacle in front of the robot; the upper laser radar (822) is vertically arranged and used for scanning the volume of plant canopies on two sides of the robot; and the rear laser radar (823) is horizontally arranged and used for detecting obstacles behind the robot.
7. The structure of the crawler-type automatic accurate variable air-assisted spraying robot as claimed in claim 1, wherein the central processing unit (4) is fixed at the lower layer of the system bracket (2); the central processing unit (4) is 8-corearmalarmv 8.264-bitcpu, and is electrically connected with the fan (61) and the water pump (5) through the driving module.
8. The path planning method for the crawler-type autonomous precise variable air-assisted spray robot structure according to claim 1, characterized by comprising the following steps:
step 1, a navigation module (81) is used for obtaining position information of longitude and latitude and height of a spraying robot and posture information of the spraying robot, a pure tracking algorithm is used for tracking a path planned according to actual conditions of an orchard according to the pure tracking algorithm, and if g is a path point on the path, ldIndicating the distance from the center point C to the g point, i.e.A forward looking distance; the wheel track is l, the turning radius is R, and the instantaneous linear speed of the crawler is VcInstantaneous linear velocity V of the left wheellInstantaneous linear velocity V of the right wheelrInstantaneous angular velocity of ωcAlpha represents the included angle between the current vehicle body posture and the target point g;
step 2, the sine formula can be used for obtaining:
Figure FDA0003348823420000021
step 3, according to the curvature formula, the curvature can be expressed as:
Figure FDA0003348823420000022
and 4, obtaining a turning radius R according to the crawler motion model:
Figure FDA0003348823420000023
the two formulas are combined:
Figure FDA0003348823420000024
step 5, controlling VcAnd ωcTo control the track of movement of the tracked vehicle, k being a coefficient representing the forward-looking distance as a linear function of the longitudinal speed of the vehicle, i.e.
ld＝kVc
Step 6, constraining the forward looking distance by using the maximum and minimum forward looking distances, wherein the larger forward looking distance means that the track is more smoothly tracked, and the smaller forward looking distance enables the tracking to be more accurate, but simultaneously brings oscillation; in order to better determine the forward viewing distance, a PSO algorithm (particle swarm optimization) is introduced, wherein a variable theta is introduced as a heading error and represents the position and the track of the vehicle bodyAngle, here using the transverse error elThe sum of the root mean square and the heading error theta is used as a main reference for particle swarm optimization, and the transverse error elThe evaluation parameters of (a) were:
Figure FDA0003348823420000031
the evaluation parameters of the course error theta are as follows:
Figure FDA0003348823420000032
designing a fitness function based on the two formulas:
F＝λF1+(1-λ)F2
n is working duration, M is sampling time, lambda is weight, the designed fitness function, F can adjust the decision weight of the transverse error and the course error according to the transverse error, and the method has good adaptability, and in a pure tracking model based on a PSO algorithm, when the transverse error e islWhen larger, the forward looking distance ldMainly by transverse error elMaking a decision; when the lateral error ldSmaller, forward looking distance ldThe decision is mainly made through a course error theta, so the design weight is as follows:
λ＝loga((el+1)/U) (U≥(elmax+1)/a)
where α is the base of the weighting function, U is the tuning parameter, elmaxIs the maximum lateral error.
9. The variable spraying method of the crawler-type automatic accurate variable air-assisted spraying robot structure according to claim 1, characterized by comprising the following steps:
step A, scanning a plant canopy by using a laser radar, performing variable spray control by processing canopy data, and performing a target plant volume detection method:
go up laser radar (822) upward scanning, carry out cluster processing with the point cloud data of gathering, the plant trunk is fitted, and distance data processing according to laser radar returns obtains the distance l of plant trunk to laser radar, and wherein laser radar's angular resolution is 0.225, and there is following data relation between two adjacent point cloud data:
Figure FDA0003348823420000041
wherein beta isiAnd betai+1Angles corresponding to adjacent point clouds,/iAnd li+1Distance values, d, collected for two adjacent point cloudsiAnd di+1The distance between the outer side of the crown and the trunk is h, and the distance between two adjacent point clouds in the vertical direction is h;
step B, calculating the area which is approximately trapezoidal and covers between two adjacent point clouds and the trunk:
Figure FDA0003348823420000042
and according to the scanning frequency of the two-dimensional laser radar of 25Hz, the volume V of the plant canopy between two adjacent clouds can be calculatedi′：
Figure FDA0003348823420000043
Wherein, Δ t is the time required for scanning a frame, W is the width of scanning of a frame of laser radar, and v is the advancing speed of the robot;
step C, controlling the flow and the air volume in the variable spraying system (6) according to the volume of the plant canopy, and considering the distance between the laser radar (822) and the spray head, so that the variable spraying system (6) needs to be controlled to carry out delay spraying on the spraying control information obtained by processing, and the delay time t can be obtained:
Figure FDA0003348823420000044
wherein the distance between the upper laser radar (822) and the nozzle (63) in the advancing direction of the robot is x, and the advancing speed of the robot is v.
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