CN117908451A - Farmland environment detection and management system based on clustered robots - Google Patents
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Abstract
The invention discloses a farmland environment detection and management system based on clustered robots, which comprises a farmland mobile robot cluster and an upper computer control system, wherein the farmland mobile robot cluster comprises farmland mobile robots distributed at different places in a farmland, and each farmland mobile robot detects soil, air and environment, identifies weed positions and plans an optimal travelling path; the upper computer control system detects, analyzes and pre-warns farmland soil, air and environment, distributes a moving starting point and a target point for each farmland moving robot and issues control instructions for weeding, fertilizing and moving. The invention solves the problems that a large amount of equipment and sensors are needed and detection points are fixed in the traditional detection system, realizes automatic fine management of farmlands, and greatly improves the production efficiency of agricultural production.
Description
Technical Field
The invention relates to a farmland environment detection and management system based on a clustered robot, and belongs to the technical field of farmland environment management.
Background
The agricultural robot is a novel production mode in agricultural production, and plays an important role in improving agricultural productivity, changing agricultural production modes, solving labor shortage and the like. In agricultural production, the content of nitrogen, phosphorus and potassium in soil, the temperature and humidity, the pH value of soil, the water content, inorganic salts, weeds and other environmental factors are all of great importance, and all of the environmental factors directly influence the growth quality of agricultural products. The traditional farmland environment detection system is composed of a power supply type acquisition instrument and various sensors, a large number of equipment and sensors are needed, and the use cost is high; in addition, the traditional method only helps farmers to know some key indexes of farmlands in real time, and cannot timely take corresponding measures for managing farm crops. In addition, in the traditional agricultural robot, the robot has a large structure generally, and is difficult to meet the environmental requirements in the environment of a few farmland soil moist soft beds or small crop gaps.
With the continuous development of technologies such as the Internet of things, artificial intelligence, machine vision, navigation and the like, the management of farmland environments is also developed towards a high degree of automation, and a plurality of robots based on the cluster technology can provide accurate crop growth environment information for farmers through real-time detection of various sensors carried by the robots; the device system carried by the device system can also realize automatic fine management of crops.
Disclosure of Invention
The invention aims to provide a farmland environment detection and management system based on clustered robots, which is characterized in that robot clusters are arranged in a farmland, each robot detects farmland environment at a place, an upper computer control system uniformly detects and analyzes farmland soil, air and environment, distributes a movement starting point and a movement target point of each robot, and issues control instructions for weeding, fertilizing and moving, so that efficient management of the farmland is realized.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides a farmland environment detection and management system based on a clustered robot, which comprises:
the farmland mobile robot cluster comprises farmland mobile robots distributed at different places in the farmland, wherein each farmland mobile robot is used for shooting the landform of the place, identifying the weed position and sending the landform, the weed position information and the self position information to the upper computer control system; detecting soil, air and environment data of a place and sending the data to an upper computer control system; determining an optimal travelling path based on a moving starting point and a target point issued by the upper computer control system, and performing weeding operation along the optimal travelling path; performing fertilization operation and movement based on an upper computer control instruction;
And, a step of, in the first embodiment,
The upper computer control system is used for detecting, analyzing and early warning farmland soil, air and environment; constructing a farmland global map based on the topography and the landform sent by the farmland mobile robot clusters; distributing a moving starting point and a target point for each farmland mobile robot based on the farmland global map, the weed position information and the position information of each farmland mobile robot; and issuing control instructions for weeding, fertilizing and moving.
Further, the farmland mobile robot comprises a control module, a positioning navigation module, a wireless transmission module, a data acquisition module, a driving and moving system, a weeding system and a fertilization system;
the positioning navigation module is used for collecting environment image information of a place where the mobile robot is located and position information of the mobile robot and transmitting the environment image information and the position information to the singlechip control board;
The data acquisition module is used for detecting soil, air and environment information of a place where the mobile robot is located and sending the soil, air and environment information to the singlechip control board;
The driving and moving system is used for driving the mobile robot to move along a preset path;
the weeding system is arranged right in front of the mobile robot, and the fertilization system is arranged right behind the mobile robot;
the control module comprises a main control board and a singlechip control board;
The single chip microcomputer control board is used for transmitting the detection data of the data acquisition module, the position information of the mobile robot and the environmental image information acquired by the positioning navigation module to the main control board, controlling the mobile robot to move along an optimal path according to the instruction of the main control board, and controlling the action of the fertilization system and the weeding system according to the instruction of the main control board;
The main control board is used for identifying weeds in a farmland based on the environmental image information acquired by the positioning navigation module and obtaining weed position information, transmitting the acquired environmental image information, weed position information and mobile robot position information to the upper computer control system, determining an optimal travelling path of the mobile robot based on a moving starting point and a target point issued by the upper computer control system and issuing the optimal travelling path to the single chip microcomputer control board, and issuing weeding, fertilizing and controller robot moving instructions issued by the upper computer control system to the single chip microcomputer control board;
and the positioning navigation module, the driving and moving system, the data acquisition module, the weeding system and the fertilization system are electrically connected with the singlechip control board.
Furthermore, the singlechip control board is also used for,
The soil, air and weather detection data sent by the data detection module are converted into self-usable electric signals, when the current value of the electric signals exceeds a preset range, an active buzzer connected with the electric signals is controlled to alarm, and the value of the electric signals is transmitted to an upper computer control system through the wireless transmission module.
Further, the main control board configures a target recognition module and an optimal path search module,
The target identification module is used for identifying the positions of weeds in the farmland based on the environmental image information acquired by the positioning navigation module;
the optimal path searching module is used for determining an optimal travelling path of the mobile robot based on the starting point and the target point of the mobile robot.
Further, the object recognition module is specifically configured to,
Weed positions in the farmland are identified based on YOLOV S target identification algorithm.
Further, the optimal path searching module is specifically configured to,
The optimal travelling path of the mobile robot is obtained by adopting an improved artificial fish swarm algorithm, and the specific implementation mode is as follows:
Setting the mobile environment of the mobile robot as a two-dimensional space, and acquiring the initial position coordinate and the target position coordinate of the mobile robot from an upper computer control system; assuming that the mobile robot is an artificial fish in the artificial fish swarm, the state of each artificial fish is the position of the artificial fish, namely the position of the mobile robot, the target is food in a 2-dimensional space, and the quantity is set to be dimension 1, so that the whole optimizing process has only one optimal value, namely only one optimal path;
In the improved artificial fish swarm algorithm,
The adaptive field of view function is updated as:
Visual(t+1)＝Visual(t)*α+Visualmin；
The updating of the self-adaptive step length is as follows:
Step(t+1)＝Step(t)*α+Stepmin；
Wherein Visual (t+1) represents the adaptive field of view of the t+1st iteration, visual (t) represents the adaptive field of view of the t iteration, visual min is a preset minimum field of view, step (t+1) represents the adaptive Step size of the t+1st iteration, step (t) represents the adaptive Step size of the t iteration, step min is a preset minimum Step size, alpha is a parameter,
Alpha is set as follows: wherein, max Iter is the preset maximum iteration number;
in the course of the said optimizing process,
The location update mode of the grouping behavior is as follows:
Wherein, X i (t+1) represents the position of the t+1st iteration, X i (t) represents the position of the t iteration, X c (t) represents the center of the field of view of X i (t), and rand (0, 1) represents a random number between 0 and 1;
the position updating mode of the rear-end collision behavior is as follows:
Wherein X s (t) represents the artificial fish position with the highest food concentration in the field of view of the artificial fish X i (t);
the position updating mode of the foraging behavior is as follows:
wherein, Representing the optimal position of the potential position within the step size range of the artificial fish X i (t), X j (t) represents a random position state of the artificial fish X i (t) within the visual field range, and m is a probability weight factor following Bernoulli distribution.
Further, the positioning navigation module comprises a Beidou positioning system, a laser range finder and a camera;
The Beidou positioning system, the laser range finder and the camera are electrically connected with the singlechip control board;
The Beidou positioning system comprises a BIRF civil satellite navigation system radio frequency chip and a Beidou vehicle-mounted navigation system;
The laser range finder and the Beidou positioning system are used for positioning the position of the mobile robot and collecting the front information of the mobile robot;
the camera is used for collecting environment image information of the place where the mobile robot is located.
Further, the data acquisition module comprises a soil and air detection module and a meteorological monitoring module;
the soil and air detection module comprises a measuring rod, a lifting mechanism and information acquisition sensors;
The lifting mechanism is electrically connected with the singlechip control board;
The measuring rod is divided into an upper half part and a lower half part, wherein the upper half part is fixed on a mobile robot body and is provided with a PM2.5 content measuring sensor, a PM10 content measuring sensor, a NO 2 content measuring sensor, an SO 2 content measuring sensor, a CO 2 content measuring sensor, an O 2 content measuring sensor, an air temperature sensor and an air pressure sensor; the lower half part is controlled to be inserted into the soil through the lifting mechanism, and is provided with a soil nitrogen, phosphorus and potassium content measuring sensor, a soil PH content measuring sensor and a soil temperature and humidity measuring sensor; each information acquisition sensor is electrically connected with the singlechip control board;
The weather monitoring module comprises a radiation sensor and a wind speed sensor;
The radiation sensor and the wind speed sensor are arranged at the top of the mobile robot and are electrically connected with the singlechip control board.
Further, the upper computer control system controls the farmland mobile robot cluster in a centralized control mode;
The upper computer control system is provided with:
the environment model building module is used for obtaining the topography and the landform of each place sent by the farmland mobile robot cluster and fusing the topography and the landform to form a global grid map of the whole farmland environment;
The travelling target distribution module is used for marking the self position information and the weed position information sent by the farmland mobile robot cluster in the global grid map by taking the self position of the mobile robot as a starting point and the weed position as a target point and issuing the self position information and the weed position information to the corresponding mobile robot.
Further, the upper computer control system is also used for,
Numbering the priorities of all the mobile robots, and displaying the positions of the mobile robots in real time in a global grid map;
When the distance between the two mobile robots is smaller than the preset safety distance, triggering the obstacle avoidance function of the mobile robots, at the moment, issuing a control instruction to enable the mobile robot with low priority to wait for a step length in situ before reaching a conflict point, and continuing to pass according to the original path after the mobile robot with high priority passes;
When the mobile robot encounters a mobile obstacle, if the distance between the two is smaller than a preset safety distance, triggering the obstacle avoidance function of the mobile robot, and at the moment, issuing a control instruction to enable the mobile robot to wait for a step length in situ before reaching a conflict point, and continuing to pass through according to an original path after waiting for the mobile obstacle to pass through.
The beneficial effects of the invention are as follows:
Aiming at the defects of lack of effective farmland environment detection and management means, low production efficiency and the like in the traditional agricultural production, the invention provides a farmland environment detection and management system based on clustered robots, which solves the problems that the traditional detection system needs a large amount of equipment and sensors and has fixed detection points by arranging robot clusters in a farmland, detecting and analyzing farmland soil, air and environment uniformly by an upper computer control system, distributing the movement starting points and target points of the robots and issuing control instructions for weeding, fertilizing and moving. The invention adopts the cluster robot, and solves the problems of the application range and efficiency of the agricultural robot.
The built-in path planning and target recognition algorithm of the mobile robot solves the problems of autonomy and working accuracy of the agricultural robot, realizes automatic fine management of farmlands, and greatly improves the production efficiency of agricultural production.
Drawings
Fig. 1 is a front view of a mobile robot provided by an embodiment of the present invention;
FIG. 2 is an oblique view of a mobile robot provided by one embodiment of the present invention;
fig. 3 is an internal structural view of a mobile robot according to an embodiment of the present invention;
FIG. 4 is a block diagram of a measuring stick in a mobile robot according to an embodiment of the present invention;
FIG. 5 is a block diagram of a weeding system in a mobile robot according to one embodiment of the present invention;
Fig. 6 is a schematic diagram of a centralized control framework of a mobile robot cluster by using a host computer control system according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of the control principle of each functional module of the mobile robot according to one embodiment of the present invention;
FIG. 8 is a communication system diagram of a farmland environment detection and management system according to an embodiment of the present invention;
FIG. 9 is a flow chart of data acquisition for a mobile robot provided by one embodiment of the present invention;
FIG. 10 is a flow chart of fertilization control provided by one embodiment of the present invention;
In the figure: 1-a radiation sensor; 2-a wind speed sensor; 3-a camera; 4-laser rangefinder; 5-weeding steering engine; 6-a Beidou positioning system; 7, a fertilization tube; 8-auger; 9, a fertilizer outlet; 10-a fertilizer hopper; 11-a display screen; 12-radar antenna; 13-a connecting rod; 14-a wireless transmission module; 15-driving wheels; 16-caterpillar tracks; 17-4G network connector cards; 18-a lifting mechanism; 19-a main control board; 20-a singlechip control board; 21-an air pressure sensor; 22-an air temperature sensor; 23-PM2.5 content measurement sensor; 24-PM10 content measurement sensor; 25-NO 2 content measuring sensor; 26-SO 2 content measuring sensor; 27-CO 2 content measuring sensor; 28-O 2 content measuring sensor; 29-an air temperature sensor; 30-a soil nitrogen, phosphorus and potassium content measuring sensor; 31-a soil PH content measuring sensor; 32-a soil temperature and humidity measuring sensor; 33-rotating the wheel set; 34-weeding blade.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
In the present invention, unless explicitly stated or limited otherwise, the terms "connected," "mounted," and the like should be construed broadly, and may be, for example, fixedly connected, directly connected, or connected via an intermediate medium. The above terms are understood in the specific meaning of the present invention according to circumstances, for those of ordinary skill in the art.
Further, reference to "one embodiment" or "an embodiment" of the present invention means that a particular feature, structure, or characteristic may be included in at least one implementation of the present invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The following describes the embodiments of the present invention in further detail with reference to the accompanying drawings. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
The embodiment of the invention provides a farmland environment detection and management system based on a clustered robot, which comprises the following components:
the farmland mobile robot cluster comprises farmland mobile robots distributed at different places in the farmland, wherein each farmland mobile robot is used for shooting the landform of the place, identifying the weed position and sending the landform, the weed position information and the self position information to the upper computer control system; detecting soil, air and environment data of a place and sending the data to an upper computer control system; determining an optimal travelling path based on a moving starting point and a target point issued by the upper computer control system, and performing weeding operation along the optimal travelling path; performing fertilization operation and movement based on an upper computer control instruction;
The upper computer control system is used for detecting, analyzing and early warning farmland soil, air and environment; constructing a farmland global map based on the topography and the landform sent by the farmland mobile robot clusters; distributing a moving starting point and a target point for each farmland mobile robot based on the farmland global map, the weed position information and the position information of each farmland mobile robot; and issuing control instructions for weeding, fertilizing and moving.
In this embodiment, a centralized control framework is adopted, referring to fig. 6, the upper computer control system centrally grasps global information in the farmland environment and state information of all mobile robots, performs centralized task processing and resource allocation, and then allocates a task planning result to each mobile robot for execution. Each mobile robot need only perform assigned tasks and need not interact with other robots. The upper computer control system and the farmland mobile robot cluster are communicated through a wireless transmission module, and a specific communication architecture is shown in fig. 8.
In this embodiment, weeding and fertilizing are performed in different places by distributing different target points of each mobile robot, so that automatic management of the whole farmland is completed.
The farmland mobile robot cluster in the farmland environment detection and management system will be specifically described below.
The farmland mobile robot cluster comprises farmland mobile robots distributed at different places in a farmland, and each farmland mobile robot has the same structure and function, and is specifically as follows:
referring to fig. 1, the farmland mobile robot includes a control module, a positioning navigation module, a wireless transmission module 14, a data acquisition module, a driving and moving system, a weeding system and a fertilizing system.
Specifically, referring to fig. 3 and 7, the control module is composed of a main control board 19 and a single-chip microcomputer control board 20, wherein the main control board 19 and the single-chip microcomputer control board 20 are configured on the farmland mobile robot.
The singlechip control board 20 is used for transmitting the detection data of the data acquisition module, the position information of the mobile robot and the environmental image information acquired by the positioning navigation module to the main control board 19, and controlling the mobile robot to move along the optimal path according to the instruction of the main control board; and controlling the action of the fertilization system and the weeding system according to the instruction of the main control panel.
The main control board 19 is configured to identify weeds in a farmland based on the environmental image information collected by the positioning navigation module and obtain weed position information, send the collected environmental image information, weed position information and mobile robot position information to the upper computer control system, determine an optimal travelling path of the mobile robot based on a moving starting point and a target point issued by the upper computer control system, issue the optimal travelling path to the single chip microcomputer control board 20, and issue a command issued by the upper computer control system for weeding, fertilizing and moving the controller robot to the single chip microcomputer control board 20.
In one embodiment of the present invention, the single-chip microcomputer control board is further configured to convert the soil, air and weather detection data sent by the data detection module into an electrical signal usable by the single-chip microcomputer control board 20, and when the current value of the electrical signal exceeds the preset range, alarm through an active buzzer connected to the single-chip microcomputer control board 20, and transmit the value at this time to the upper computer control system through the wireless transmission module 14, so that a user can view the data in real time.
In one embodiment of the present invention, the object recognition module and the optimal path search module are configured on the main control board 19,
The target identification module is used for identifying the positions of weeds in the farmland based on the environmental image information acquired by the positioning navigation module;
and the optimal path searching module is used for determining an optimal travelling path of the mobile robot based on the starting point and the target point of the mobile robot.
Specifically, the target recognition module recognizes the weed position in the farmland based on YOLOV S target recognition algorithm, and the specific implementation process is as follows:
constructing YOLOV S target-recognition network, comprising: the input end is used for preprocessing the input environment image information; a backbone network for extracting input image features; the neck network is used for carrying out feature fusion on the extracted image features; the output layer outputs the input pictures into three characteristic diagrams with different sizes, namely a large picture, a middle picture, a small picture and a medium picture respectively to predict targets with different sizes and categories;
Acquiring a training set, taking an image shot by a mobile robot camera as input, inputting a YOLOV S target recognition network constructed by the mobile robot camera, taking a weed position as output, taking a weed position marked in the input image as output true value, and training the YOLOV S target recognition network to obtain an optimal model, namely a weed recognition model;
and identifying images shot by the mobile robot camera by adopting the trained weed identification model, acquiring weed positions, and uploading the weed positions to an upper computer control system.
In YOLOV S target recognition network, the preprocessing of the input environmental image information by the input terminal includes:
the input image is scaled to the size of the network input and then normalized.
In the training process, the method further comprises the operations of randomly zooming, cutting, arranging and the like on the input pictures to realize the enhancement of the mosaics data, enrich the detection data set, improve the training speed and enhance the detection effect on the small targets. And after the image enrichment is finished, carrying out normalization processing.
In YOLOV S target identification network, the specific structure of the backbone network is as follows:
The backbone Network mainly comprises modules such as a Focus module, a CBL (Cross-STAGE PARTIAL Network Lite, cross-stage part Network lightweight), a CSP (Cross STAGE PARTIAL, cross-stage part connection), an SPP (SPATIAL PYRAMID Pooling, space pyramid pooling) and the like, wherein the Focus module slices an input image of 608 x 3 into a characteristic diagram of 304 x 12; the CBL module includes a convolutional layer (conv), a batch normalization (Batch Normalization), and an activation function (LeakyReLU), changing the profile of 304 x 12 to a profile of 304 x 32; the CSP module is provided with a CSP1_X structure for a backbone network and a CSP2_X structure for a neck, wherein the CSP1_X divides an input characteristic diagram into two branches, the first branch firstly passes through CBL and then passes through X residual structures (Bottleneck) and then carries out Conv convolution once, the other branch directly carries out Conv convolution, and then tensor splicing (Concat), batch Normalization (BN) and activation (LeakyReLU) operations are carried out on the two branches; CPS2_X is used for the neck (Neck) network, unlike CSP1_X in that X residual structures are replaced with 2X CBLs.
In YOLOV S target identification network, the neck network solves the problem of multi-scale fusion of feature information through the structure of FPN+PAN, wherein a feature pyramid network (Feature Pyramid Networks, FPN) is responsible for up-sampling operation from top to bottom, and strong semantic features of the upper layer of the pyramid network are transmitted downwards; the pyramid attention network (Pyramid Attention Networks, PAN) is then responsible for the downsampling operation from bottom to top, passing the strong locating features of the underlying pyramid network up. Through cooperation of the two network structures, feature aggregation of different detection layers by different trunk layers is realized, and feature fusion capability of the whole network structure is enhanced.
In YOLOV S target identification networks, the output layer includes Bounding box loss function and NMS (Non-Maximum Supression) Non-maximum suppression, which is used in target detection post-processing to take charge of target frame screening tasks.
The optimal path searching module obtains the optimal travelling path of the mobile robot by adopting an improved artificial fish swarm algorithm, and the specific implementation process is as follows:
setting the moving environment of the mobile robot as a two-dimensional space, (xS, yS) as the initial position coordinate of the mobile robot and (xT, yT) as the target position coordinate, wherein the initial position coordinate and the target position coordinate are issued by an upper computer control system. The objective of mobile robot trajectory planning is to obtain a path with the shortest trajectory under the constraint condition, so that the objective function is designed as follows:
F＝minl(θ)，
Where θ represents the constraint condition that the trajectory planning needs to satisfy, and l represents the path length of the mobile robot trajectory planning.
Establishing a mathematical model according to an artificial fish swarm algorithm, wherein the initial assumption and corresponding relation between the model and the algorithm are as follows:
1) The robot is assumed to be artificial fish in the artificial fish swarm, and the number of the robot is N; the target is food in a 2-dimensional space, and the quantity is set to be dimension 1, which shows that the whole optimizing process has only one optimal value, namely only one optimal path;
2) The state Xi of each artificial fish can be simply considered as the position of the artificial fish, and the objective function y=f (Xi) is the food concentration corresponding to the position, and the closer the artificial fish is to the food, the higher the food concentration, and the larger the objective function value.
The specific implementation process of the algorithm is as follows:
Initializing various parameters of the improved artificial fish swarm algorithm, including: setting the number N of artificial fish shoals, the self-adaptive Step length Step, the minimum Step length Step min, the self-adaptive Visual field, the minimum Visual field min, the maximum probing times Try number, the crowding factor delta, the iteration times t and the maximum iteration times Max Iter;
the Visual was improved by introducing the parameter α:
Wherein k1 and k2 are undetermined parameters;
When Max Iter =50 is set, the analysis of the curve graph of the parameter α with k1 and k2 shows that the curves of k1=1 and k2=3 are the most satisfactory; the α parameter introduced in this embodiment is set as:
the adaptive field of view function is updated as:
Visual(t+1)＝Visual(t)*α+Visualmin (3)
further, in order to avoid unbalance of artificial fish shoal swimming caused by overlarge difference between the step length and the visual field change speed, the self-adaptive step length is updated as follows:
Step(t+1)＝Step(t)*α+Stepmin (4)
Initializing artificial fish shoals, which specifically are: the first generation artificial fish school is randomly distributed in the search area according to normal distribution, the current state of all artificial fish is expressed as X i＝(X1,X2,X3,…,Xn),Yi＝f(Xi) is the food concentration of the current position X i,
The fish school starts to swim; x i performs a clustering behavior and a rear-end collision behavior to obtain (X swarm,yswarm) and (X follow,yfollow) respectively, and compares Y swarm with Y follow, and updates X i and Y i by taking optimal values; returning to the optimal solutions X i and Y i.
Further, the grouping behavior is a behavior in which artificial fish are gathered toward the center of the field of view, the center of the field of view of the artificial fish in the state of X i (t) is set to X c (t), and the number of artificial fish in the field of view (d i,j<Visual,di,j is the distance between individual artificial fish) is set to N f; if X c(t)/Nf>δ*Yi (t), indicating that there is more food in the center of the field of view and not crowded, further forward of the center of the field of view may be performed according to equation (6), otherwise foraging is performed,
Further, the rear-end collision behavior means that the artificial fish X i (t) finds the artificial fish X s (t) with the highest food concentration in the visual field, if Y s(t)/Nf>δ*Yi (t) is satisfied, it indicates that the state of the artificial fish X s (t) has the higher food concentration Y s (t) and the surroundings thereof are not crowded, at this time, the artificial fish X i (t) moves to X s (t) by one step according to the formula (7), otherwise, the foraging behavior is performed,
Further, the foraging behavior is one of the most basic behaviors of the fish shoal, and is that artificial fish with finger state of X i (t) randomly selects a state of X j (t) in Visual field according to a formula (8); if Y j(t)>Yi (t), then move in the Y j (t) state direction according to equation (9); if Y j(t)<Yi (t), then X j (t) is again randomly selected; if Y j(t)>Yi (t) is not satisfied yet by trying to the maximum number of heuristics Try number, then a random behavior is performed,
Xj(t)＝Xi(t)+Visual*rand(0,1) (8)
Further, in order to reduce the random process, reduce the time cost and improve the path quality, the embodiment introduces a heuristic direction operator, and in the foraging process, the artificial fish shoals are inspired by the heuristic operator, and the optimal position is selected under the condition of not trying the infeasible direction; assume thatRepresenting potential positions X j (t) within the artificial fish X i (t) step size range; p represents a set of all possible positions, which is expressed as:
In the foraging behavior, the food concentration at all possible positions in P is calculated, and then the optimal position is selected As the next step, as shown in formulas (11), (12):
Further, after the random process is eliminated, the adaptability of the algorithm is weakened, and local optimization is easier to reach; the present embodiment therefore introduces a probability weight factor m, following the bernoulli distribution; the artificial fish shoal jumps out of the local optimum at a certain frequency; formula (12) is rewritten as formula (13),
Further, to prevent local optimality, if other actions cannot be performed, artificial fish shoals will perform the immediate actions; random behavior refers to randomly selecting any state in the X i field of view and then swimming toward the selected state, described as:
Xi(t+1)＝Xi(t)+Step(t)*rand(0,1) (14)
judging whether the maximum iteration number Max Iter is reached, if so, exiting the loop, and returning to an optimal value; if not, t=t+1, and continuing the swimming of the fish school.
Referring to fig. 1 and 7, the positioning navigation module includes a beidou positioning system 6, a laser range finder 4 and a camera 3. The Beidou positioning system 6, the laser range finder 4 and the camera 3 are electrically connected with the single-chip microcomputer control board 20, and the camera 3 and the laser range finder 4 are arranged at the front end of the farmland robot and are required to be ensured not to be shielded.
The camera 3 is used for collecting image information in the environment and sending the image information to the singlechip control board 20; the laser range finder 4 can provide functions of accurate distance measurement, three-dimensional modeling, obstacle detection, memory, positioning, navigation and the like, and provides reliable data support for map construction and environment perception; the Beidou positioning system 6 can provide navigation service for the mobile robot, help the mobile robot determine current position information, and ensure that the mobile robot moves according to a correct planning path.
Preferably, the Beidou positioning system 6 comprises a BIRF civil satellite navigation system radio frequency chip and a Beidou vehicle navigation system, and the laser range finder 4 is matched with the Beidou positioning system to well control the position of the mobile robot, so that the mobile robot can avoid obstacles in time when encountering obstacles.
Referring to fig. 7 and 8, the wireless transmission module 14 performs wireless communication with the upper computer control system through the 4G network connector card 17, so that the upper computer control system can control and monitor the mobile robot in real time, and the upper computer control system can receive image information in real time, monitor soil, meteorological data and fertilization conditions. As shown in fig. 2 and 3, the wireless transmission module 14 is disposed on the surface of the farm mobile robot, and the 4G network connection port card 17 is disposed inside the farm mobile robot.
Preferably, the mobile robot may further be configured with a radar antenna 12, and the wireless transmission module 14 and the radar antenna 12 are connected together through a network, and give instructions and collect environmental image information.
Preferably, the master control board 19 is a raspberry group 4B master control board.
Preferably, the SCM control board 20 is an STM32 SCM control board.
Referring to fig. 7 and 9, the data acquisition module includes a soil and air detection module and a weather monitoring module, and is used for acquiring relevant data information and weather data in the soil and air in real time, and the data acquisition module is electrically connected with the single-chip microcomputer control board 20 and is used for transmitting the detection data to the single-chip microcomputer control board 20.
Referring to fig. 3 and 4, in one embodiment of the present invention, the soil and air detection module mainly includes a measuring rod, a lifting mechanism 18 and information collecting sensors; the lifting mechanism 18 is electrically connected with the singlechip control board 20; the measuring rod is divided into an upper half part and a lower half part, wherein the upper half part is fixed on a mobile robot body and is provided with a PM2.5 content measuring sensor 23, a PM10 content measuring sensor 24, a NO 2 content measuring sensor 25, an SO 2 content measuring sensor 26, a CO 2 content measuring sensor 27, an O 2 content measuring sensor 28, an air temperature sensor 29, an air temperature sensor 22 and an air pressure sensor 21; the lower half part of the measuring rod is controlled to be inserted into the soil through the lifting mechanism 18; the lower half part of the measuring rod is provided with a soil nitrogen, phosphorus and potassium content measuring sensor 30, a soil PH content measuring sensor 31 and a soil temperature and humidity measuring sensor 32. Each information acquisition sensor is electrically connected with the singlechip control board 20.
The meteorological monitoring module comprises a radiation sensor 1 and a wind speed sensor 2; the radiation sensor 1 and the wind speed sensor 2 are arranged at the top of the mobile robot and are electrically connected with the singlechip control board 20; when the farmland weather environment is monitored automatically and severe or sudden meteorological conditions are met, an alarm can be sent to an operator in time. The wind speed sensor is a three-cup type wind speed measuring sensor.
Referring to fig. 9, in actual use, the single-chip microcomputer control board 20 converts the data sent by the sensors of the soil and air detection module and the weather monitoring module into the electric signals usable by the single-chip microcomputer control board 20, when the current electric signal value exceeds the preset initial value, the alarm is given through the active buzzer connected to the single-chip microcomputer control board 20, and the electric signal value at the moment is wirelessly transmitted to the upper computer control system through the wireless transmission module 14, so that the user can check the data in real time. In addition, the single-chip microcomputer control board 20 also transmits each sensor detection data to the main control board 19.
Preferably, in one embodiment of the invention, the mobile robot is further provided with a display screen 11, see in particular figure 2,
The display screen 11 is electrically connected with the single-chip microcomputer control board 20, and the display screen 11 is used for throwing out soil and air detection data and weather detection data acquired by the single-chip microcomputer control board 20, so that an operator can more intuitively see relevant parameters.
Referring to fig. 1, in this embodiment, the drive and movement system is composed of a motor and an electronic governor, a servo motor, a power source, a drive wheel 15, and a crawler 16. The driving wheel 15 and the crawler 16 are provided on both sides of the mobile robot, respectively.
The driving and moving system is electrically connected with the single-chip microcomputer control board 20, and the driving and moving system is controlled to work through the single-chip microcomputer control board 20, so that the mobile robot moves along a preset path.
The weeding system and the fertilizing system are electrically connected with the single-chip microcomputer control board 20.
Referring to fig. 5, in one embodiment of the present invention, the weeding system includes weeding blades 34, rotating wheel sets 33, links 13, and weeding steering engine 5;
The weeding system is arranged right in front of the mobile robot, the weeding system is contracted in front of the robot when the robot moves, when weeding is needed, the lower computer control system issues weeding control instructions to the main control board, the main control board issues the instructions to the single chip microcomputer control board, the rotation of the rotating wheel set 33 and the rotation of the connecting rod 13 are controlled, the weeding blade 34 at the front end reaches a preset pose, and weeding work is completed through shearing force.
The fertilization system comprises a fertilizer hopper 10, a fertilizer outlet 9, a packing auger 8 and a fertilization pipe 7; the fertilization system is arranged right behind the mobile robot; the fertilizer bucket 10 of the fertilizer application system is used for containing fertilizer, the fertilizer outlet 9 is used for leaking out the fertilizer in the fertilizer bucket, the auger 8 rotates at a constant speed, and the fertilizer is extruded at a constant speed, so that the fertilizer application uniformity is ensured. When fertilization is needed, the lower computer control system issues fertilization control instructions to the main control board, the main control board issues the instructions to the single-chip microcomputer control board, and the single-chip microcomputer control board controls the fertilization system to operate.
The upper computer control system in the farmland environment detection and management system is specifically described below.
In the embodiment, the upper computer control system adopts a centralized control mode for the farmland mobile robot cluster,
Referring to fig. 6, in order to implement effective control on a mobile robot cluster to complete various complex cluster tasks, a mobile robot cluster task plan is decomposed into a decision layer, a path planning layer, a track generation layer and a control layer, wherein the decision layer is responsible for task planning and distribution, collision avoidance and the like in a mobile robot cluster system; the path planning layer is responsible for converting task decision data into route points so as to guide the mobile robot to complete tasks and avoid obstacles; the track generation layer generates a feasible path of the mobile robot according to the environment sensing information of the mobile robot; the control layer controls the mobile robot to move according to the generated track.
Referring to fig. 7 and 8, the upper computer control system is equipped with an android operating system and an ROS operating system, and is used for sending instructions to the mobile robot, the instructions are transmitted to the main control board 19 through a TCP/IP protocol, the main control board 19 transmits the instructions to the single-chip microcomputer control board 20 through serial port communication, and the single-chip microcomputer control board 20 collects and controls the motor drive of the mobile robot through PWM encoding, so that the mobile robot can move according to the instructions of the upper computer control system.
The upper computer control system is mainly responsible for controlling and monitoring the lower computer, and can also send instructions to the lower computer, in particular to instructions for analyzing and processing soil detection data, issuing weeding and fertilizing instructions and instructions for controlling the movement of the robot when necessary.
It should be noted that the upper computer control system is also used for,
And numbering the priorities of all the mobile robots, and controlling the mobile robots to move according to a preset optimal travelling path. When two robots meet, the mobile robot with high priority moves according to the planned global optimal path, the mobile robot with low priority waits for one step length in situ before reaching a conflict point, and the mobile robot with high priority continues to pass according to the original path after passing; when the mobile robot encounters a moving obstacle, the mobile robot also waits for a step length in situ before reaching a conflict point, and continues to pass according to the original path after waiting for the moving obstacle to pass.
In the upper computer control system, the positions of the mobile robots in the global grid map are displayed in real time; the camera and the laser range finder of the mobile robot can acquire the front information of the robot, if the distance between the camera and the laser range finder is smaller than the preset safety distance, the obstacle avoidance function of the robot can be triggered, at the moment, a control instruction is issued, and the mobile robot with low priority waits for a step length in situ before reaching a conflict point.
In one embodiment of the invention, an upper computer control system is provided with an environment model establishment module and a traveling target distribution module;
the environment model building module is used for obtaining the topography and the landform of each place sent by the farmland mobile robot cluster and fusing the topography and the landform to form a global grid map of the whole farmland environment;
the travelling target distribution module is used for marking the self position information and the weed position information sent by the farmland mobile robot cluster in the global grid map by taking the self position of the mobile robot as a starting point and the weed position as a target point and sending the self position information and the weed position information to the mobile robot.
Based on the farmland environment detection and management system, an embodiment of the invention also provides a farmland environment detection and management method based on the clustered farmland mobile robot, which comprises the following steps:
detecting and alarming farmland soil, air and environment;
weeding the farmland based on the optimal path;
And carrying out fertilization management on farmlands.
Specifically, detect the warning to farmland soil, air and environment, include:
The single-chip microcomputer control board acquires data sent by each sensor of the soil and air detection module and the weather monitoring module, converts the data into an available electric signal, alarms through an active buzzer connected to the single-chip microcomputer control board when the current electric signal value exceeds a preset range, and wirelessly transmits the current electric signal value to the upper computer control system through the wireless transmission module so as to enable users to check the data in real time.
Weeding is carried out on farmlands based on the optimal path, and the concrete implementation process is as follows:
Each mobile robot obtains a topography image of the location of the mobile robot through a camera, transmits the topography image to a main control board, and sends the topography image to an upper computer control system through the main control board;
each mobile robot adopts a weed identification model to identify the obtained topography and relief image, identifies the weed position and sends the weed position to an upper computer control system;
The upper computer control system fuses the local topography sent by each mobile robot to obtain a global grid map of the whole farmland environment, marks the global grid map by taking the position of the mobile robot as a starting point and the position of weeds as a target point on the basis of the position information and the weed position information sent by the farmland mobile robot cluster, and sends the global grid map to the mobile robots;
Each mobile robot adopts an improved artificial fish swarm algorithm to solve and obtain an optimal travelling path of the mobile robot based on the starting point and the target point;
And controlling the mobile robot to travel to the weed position along the optimal travel path and performing weeding operation.
Fertilizing management of farmland, see fig. 10, comprising:
The single chip microcomputer control board acquires data sent by each sensor of the soil and air detection module and the weather monitoring module, converts the data into an available electric signal, and when the current electric signal value exceeds a preset range, wirelessly transmits the electric signal value to the upper computer control system through the wireless transmission module, and the upper computer control system analyzes and records the data, issues a control instruction and controls the fertilization system to fertilize.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. A farmland environment detection and management system based on a clustered robot is characterized by comprising:
the farmland mobile robot cluster comprises farmland mobile robots distributed at different places in the farmland, wherein each farmland mobile robot is used for shooting the landform of the place, identifying the weed position and sending the landform, the weed position information and the self position information to the upper computer control system; detecting soil, air and environment data of a place and sending the data to an upper computer control system; determining an optimal travelling path based on a moving starting point and a target point issued by the upper computer control system, and performing weeding operation along the optimal travelling path; performing fertilization operation and movement based on an upper computer control instruction;
And, a step of, in the first embodiment,
The upper computer control system is used for detecting, analyzing and early warning farmland soil, air and environment; constructing a farmland global map based on the topography and the landform sent by the farmland mobile robot clusters; distributing a moving starting point and a target point for each farmland mobile robot based on the farmland global map, the weed position information and the position information of each farmland mobile robot; and issuing control instructions for weeding, fertilizing and moving.
2. The farmland environment detection and management system based on clustered robots according to claim 1, wherein the farmland mobile robots comprise a control module, a positioning navigation module, a wireless transmission module, a data acquisition module, a driving and moving system, a weeding system and a fertilizing system;
the positioning navigation module is used for collecting environment image information of a place where the mobile robot is located and position information of the mobile robot and transmitting the environment image information and the position information to the singlechip control board;
The data acquisition module is used for detecting soil, air and environment information of a place where the mobile robot is located and sending the soil, air and environment information to the singlechip control board;
The driving and moving system is used for driving the mobile robot to move along a preset path;
the weeding system is arranged right in front of the mobile robot, and the fertilization system is arranged right behind the mobile robot;
the control module comprises a main control board and a singlechip control board;
The single chip microcomputer control board is used for transmitting the detection data of the data acquisition module, the position information of the mobile robot and the environmental image information acquired by the positioning navigation module to the main control board, controlling the mobile robot to move along an optimal path according to the instruction of the main control board, and controlling the action of the fertilization system and the weeding system according to the instruction of the main control board;
The main control board is used for identifying weeds in a farmland based on the environmental image information acquired by the positioning navigation module and obtaining weed position information, transmitting the acquired environmental image information, weed position information and mobile robot position information to the upper computer control system, determining an optimal travelling path of the mobile robot based on a moving starting point and a target point issued by the upper computer control system and issuing the optimal travelling path to the single chip microcomputer control board, and issuing weeding, fertilizing and controller robot moving instructions issued by the upper computer control system to the single chip microcomputer control board;
and the positioning navigation module, the driving and moving system, the data acquisition module, the weeding system and the fertilization system are electrically connected with the singlechip control board.
3. The farmland environment detection and management system based on the clustered robot according to claim 2, wherein the single-chip microcomputer control board is further used for,
The soil, air and weather detection data sent by the data detection module are converted into self-usable electric signals, when the current value of the electric signals exceeds a preset range, an active buzzer connected with the electric signals is controlled to alarm, and the value of the electric signals is transmitted to an upper computer control system through the wireless transmission module.
4. The system for detecting and managing farmland environment based on clustered robots according to claim 2, wherein the main control board is configured with a target recognition module and an optimal path search module,
The target identification module is used for identifying the positions of weeds in the farmland based on the environmental image information acquired by the positioning navigation module;
the optimal path searching module is used for determining an optimal travelling path of the mobile robot based on the starting point and the target point of the mobile robot.
5. The system for detecting and managing a farm environment based on a clustered robot as claimed in claim 4, wherein the object recognition module is specifically configured to,
Weed positions in the farmland are identified based on YOLOV S target identification algorithm.
6. The system for detecting and managing farmland environment based on clustered robots as claimed in claim 4, wherein said optimal path search module is specifically configured to,
The optimal travelling path of the mobile robot is obtained by adopting an improved artificial fish swarm algorithm, and the specific implementation mode is as follows:
Setting the mobile environment of the mobile robot as a two-dimensional space, and acquiring the initial position coordinate and the target position coordinate of the mobile robot from an upper computer control system; assuming that the mobile robot is an artificial fish in the artificial fish swarm, the state of each artificial fish is the position of the artificial fish, namely the position of the mobile robot, the target is food in a 2-dimensional space, and the quantity is set to be dimension 1, so that the whole optimizing process has only one optimal value, namely only one optimal path;
In the improved artificial fish swarm algorithm,
The adaptive field of view function is updated as:
Visual(t+1)＝Visual(t)*α+Visualmin；
The updating of the self-adaptive step length is as follows:
Step(t+1)＝Step(t)*α+Stepmin；
Wherein Visual (t+1) represents the adaptive field of view of the t+1st iteration, visual (t) represents the adaptive field of view of the t iteration, visual min is a preset minimum field of view, step (t+1) represents the adaptive Step size of the t+1st iteration, step (t) represents the adaptive Step size of the t iteration, step min is a preset minimum Step size, alpha is a parameter,
Alpha is set as follows: wherein, max Iter is the preset maximum iteration number;
in the course of the said optimizing process,
The location update mode of the grouping behavior is as follows:
Wherein, X i (t+1) represents the position of the t+1st iteration, X i (t) represents the position of the t iteration, X c (t) represents the center of the field of view of X i (t), and rand (0, 1) represents a random number between 0 and 1;
the position updating mode of the rear-end collision behavior is as follows:
Wherein X s (t) represents the artificial fish position with the highest food concentration in the field of view of the artificial fish X i (t);
the position updating mode of the foraging behavior is as follows:
wherein, Representing the optimal position of the potential position within the step size range of the artificial fish X i (t), X j (t) represents a random position state of the artificial fish X i (t) within the visual field range, and m is a probability weight factor following Bernoulli distribution.
7. The farmland environment detection and management system based on clustered robots according to claim 2, wherein the positioning navigation module comprises a Beidou positioning system, a laser range finder and a camera;
The Beidou positioning system, the laser range finder and the camera are electrically connected with the singlechip control board;
The Beidou positioning system comprises a BIRF civil satellite navigation system radio frequency chip and a Beidou vehicle-mounted navigation system;
The laser range finder and the Beidou positioning system are used for positioning the position of the mobile robot and collecting the front information of the mobile robot;
the camera is used for collecting environment image information of the place where the mobile robot is located.
8. The farmland environment detection and management system based on clustered robots according to claim 2, wherein the data acquisition module comprises a soil and air detection module and a meteorological monitoring module;
the soil and air detection module comprises a measuring rod, a lifting mechanism and information acquisition sensors;
The lifting mechanism is electrically connected with the singlechip control board;
The measuring rod is divided into an upper half part and a lower half part, wherein the upper half part is fixed on a mobile robot body and is provided with a PM2.5 content measuring sensor, a PM10 content measuring sensor, a NO 2 content measuring sensor, an SO 2 content measuring sensor, a CO 2 content measuring sensor, an O 2 content measuring sensor, an air temperature sensor and an air pressure sensor; the lower half part is controlled to be inserted into the soil through the lifting mechanism, and is provided with a soil nitrogen, phosphorus and potassium content measuring sensor, a soil PH content measuring sensor and a soil temperature and humidity measuring sensor; each information acquisition sensor is electrically connected with the singlechip control board;
The weather monitoring module comprises a radiation sensor and a wind speed sensor;
The radiation sensor and the wind speed sensor are arranged at the top of the mobile robot and are electrically connected with the singlechip control board.
9. The farmland environment detection and management system based on clustered robots according to claim 2, wherein the upper computer control system controls the farmland mobile robot clusters in a centralized control manner;
The upper computer control system is provided with:
the environment model building module is used for obtaining the topography and the landform of each place sent by the farmland mobile robot cluster and fusing the topography and the landform to form a global grid map of the whole farmland environment;
The travelling target distribution module is used for marking the self position information and the weed position information sent by the farmland mobile robot cluster in the global grid map by taking the self position of the mobile robot as a starting point and the weed position as a target point and issuing the self position information and the weed position information to the corresponding mobile robot.
10. The farmland environment detection and management system based on clustered robots as set forth in claim 9, wherein said upper computer control system is further configured to,
Numbering the priorities of all the mobile robots, and displaying the positions of the mobile robots in real time in a global grid map;
When the distance between the two mobile robots is smaller than the preset safety distance, triggering the obstacle avoidance function of the mobile robots, at the moment, issuing a control instruction to enable the mobile robot with low priority to wait for a step length in situ before reaching a conflict point, and continuing to pass according to the original path after the mobile robot with high priority passes;
When the mobile robot encounters a mobile obstacle, if the distance between the two is smaller than a preset safety distance, triggering the obstacle avoidance function of the mobile robot, and at the moment, issuing a control instruction to enable the mobile robot to wait for a step length in situ before reaching a conflict point, and continuing to pass through according to an original path after waiting for the mobile obstacle to pass through.
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