CN116058176A - Fruit and vegetable picking mechanical arm control system based on double-phase combined positioning - Google Patents
Fruit and vegetable picking mechanical arm control system based on double-phase combined positioning Download PDFInfo
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- 235000012055 fruits and vegetables Nutrition 0.000 title claims abstract description 64
- 235000013399 edible fruits Nutrition 0.000 claims abstract description 28
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D46/00—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs
- A01D46/30—Robotic devices for individually picking crops
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention relates to the field of agricultural automation control, in particular to a fruit and vegetable picking mechanical arm control system based on double-phase combined positioning. The invention specifically comprises a vision system, an upper computer control system, a mechanical arm control system and a lower computer control system; the upper computer control system generates a motion control instruction according to image information acquired by the vision system, the motion control instruction is sent to the robot arm control system to generate a robot arm joint value sequence, and after the robot arm joint value sequence is sent to the lower computer control system, the lower computer control system is assisted by the vision system to execute the picking action control of fruits at the tail end of the robot arm. The invention is used for realizing stronger robustness and better control effect of the mechanical arm in the motion control process.
Description
Technical Field
The invention relates to the field of agricultural automation control, in particular to a fruit and vegetable picking mechanical arm control system based on double-phase combined positioning.
Background
The agricultural land has the advantages of large cultivated area and various crops, and in the long-term agricultural cultivation technology development process, agricultural production gradually advances from the original cultivation of knife and fire to agricultural mechanization and intellectualization. Advances in the agricultural development level mean a reduction in agricultural production costs and an improvement in agricultural production efficiency, wherein fruit and vegetable picking is an important part of the development of agricultural automation as a research hotspot in the agricultural field. The existing fruit and vegetable picking process has the problems that the fruit and vegetable picking efficiency is low, and the traditional mechanical picking mode is easy to cause damage to the fruits and vegetables. In order to solve the problem, a plurality of expert scholars combine the traditional machinery with intelligent control by utilizing the technologies such as a machine learning algorithm, an image processing technology, an intelligent optimization algorithm and the like, so that the fruit and vegetable picking process is further optimized. However, in the control algorithm of the existing fruit and vegetable picking robot, the success rate of fruit and vegetable picking is seriously affected by the positioning accuracy of the target fruits, and the problem that the overall efficiency of fruit and vegetable picking is low and the picking effect is poor is caused.
The patent CN108848936a discloses an end effector of a round fruit and vegetable picking robot and a realizing method thereof, wherein a round fruit and vegetable limiting shell is established for positioning fruit and vegetable picking and cutting fruit stalks, but a fruit and vegetable identification device is not arranged in the patent, automatic cutting is performed after fruit and vegetable positioning is performed manually, and meanwhile, a specific mode of fruit stalk identification is not described. The Chinese patent with publication number of CN113994814A provides an intelligent fruit and vegetable picking robot, wherein the fruit and vegetable picking robot performs positioning and identification of fruits and vegetables by establishing a mode of controlling vision and a traveling device in a combined way so as to improve the accuracy of fruit and vegetable picking, but a specific control method of the picking stroke of the mechanical arm is not explicitly described when the traveling device and the mechanical arm are combined in the patent, and the influence of the control accuracy of the mechanical arm on the accuracy of fruit and vegetable picking cannot be further known.
Therefore, aiming at the problems existing in the optimal control of the existing fruit and vegetable picking robot, the invention provides a fruit and vegetable picking mechanical arm control system based on double-phase combined positioning.
Disclosure of Invention
Aiming at the problems, the invention provides a fruit and vegetable picking mechanical arm control system based on double-phase combined positioning, which specifically comprises a vision system, an upper computer control system, a mechanical arm control system and a lower computer control system; the upper computer control system generates a motion control instruction according to image information acquired by the vision system, the motion control instruction is sent to the robot arm control system to generate a robot arm joint value sequence, and after the robot arm joint value sequence is sent to the lower computer control system, the lower computer control system is assisted by the vision system to execute the picking action control of fruits at the tail end of the robot arm.
Specifically, the fruit and vegetable picking mechanical arm control system acquires accurate position information of fruits and vegetables through double-camera combined positioning, generates a mechanical arm end effector picking path according to the accurate position information of the fruits and vegetables, and establishes a mechanical arm joint value sequence according to the picking path.
Preferably, the vision system includes a far view camera and a near view camera.
Specifically, the distant view camera is used for pre-identifying the fruit and vegetable position area, and the close view camera is used for carrying out feedback adjustment on the positioning accuracy in the fruit and vegetable position positioning process.
Preferably, in the fruit and vegetable picking mechanical arm control system, the upper computer control system obtains three-dimensional coordinate information of the target fruit through a deep learning algorithm according to image information transmitted by a distant view camera.
Preferably, the deep learning algorithm adopts a YOLO V5 network model to complete the identification of the target fruits.
Preferably, the upper computer control system generates the mechanical arm tail end motion control instruction by establishing an obstacle avoidance path planning algorithm based on the mechanical arm tail end.
Specifically, the motion control instruction directly acts on the mechanical arm end effector to control the motion of the mechanical arm end effector.
Preferably, the obstacle avoidance path planning algorithm performs obstacle avoidance path planning calculation by establishing a bidirectional rapid expansion random tree based on improvement, wherein the improvement is that a target point deviation strategy is added and local optimization is performed in the path searching process.
Preferably, the motion control command is issued to the mechanical arm control system to generate a mechanical arm joint value sequence corresponding to the motion control command, and the mechanical arm is controlled to move to the vicinity of the target fruit according to the mechanical arm joint value sequence.
Preferably, the vision system assists, the image information transmitted by the near-field camera is used for obtaining image characteristics through an image processing method, and a vision servo control algorithm based on the image characteristics is used for controlling the tail end of the mechanical arm to adjust the gesture.
Preferably, the visual servo control algorithm is a closed-loop control algorithm based on image information, and the fruit and vegetable picking position deviation is corrected through feedback adjustment.
Preferably, the lower computer control system controls the screw transmission system to adjust proper picking height when receiving three-dimensional coordinate information of the fruit tree transmitted by the upper computer control system, so as to complete the height control of the fruit and vegetable picking lifting platform.
Compared with the prior art, the invention has the beneficial effects that:
according to the control system of the fruit and vegetable picking mechanical arm based on the double-phase combined positioning, the positioning error in the actual fruit and vegetable picking process is further reduced by building the double-phase combined positioning control method based on the near-view camera and the far-view camera, meanwhile, the fruit and vegetable picking lifting platform capable of being controlled in a lifting mode is built for further adjusting the fruit and vegetable picking height, and further, different types of fruit grabbing can be achieved.
Drawings
FIG. 1 is a control flow chart of a fruit and vegetable picking mechanical arm control system based on double-phase combined positioning;
FIG. 2 is a flow chart of a two-phase combined positioning control method in a fruit and vegetable picking mechanical arm control system based on two-phase combined positioning;
fig. 3 is an apple identification effect diagram in a fruit and vegetable picking mechanical arm system based on a YOLO V5 network model;
fig. 4 is a schematic diagram of improved node expansion in the planning calculation of obstacle avoidance paths by using an improved bidirectional fast expanding random tree.
Detailed Description
Examples:
the fruit and vegetable picking mechanical arm control system based on the double-phase combined positioning in the embodiment specifically comprises a vision system, an upper computer control system, a mechanical arm control system and a lower computer control system; the upper computer control system generates a motion control instruction according to image information acquired by the vision system, the motion control instruction is sent to the robot arm control system to generate a robot arm joint value sequence, and after the robot arm joint value sequence is sent to the lower computer control system, the lower computer control system is assisted by the vision system to execute the picking action control of fruits at the tail end of the robot arm.
In one embodiment, the vision system includes a far view camera and a near view camera.
Specifically, the long-range view camera is an Azure Kinect DK depth camera; the close-up camera is a Realsense D435i depth camera; in the near view camera and the far view camera, a dual-camera combined positioning control method is established, as shown in fig. 2, the dual-camera combined positioning control method specifically comprises the following steps:
s1, starting a distant view camera and obtaining three-dimensional coordinate information of a target fruit;
s2, establishing an obstacle avoidance path planning algorithm according to three-dimensional coordinate information of the target fruit, and controlling an end effector of the mechanical arm to reach a pre-picking point;
s3, updating joint angles of the six-axis mechanical arm on the basis of S2, and starting a close-range camera after setting pixel coordinates of expected feature points based on pre-picking points'
S4, acquiring current actual feature point pixel coordinates through a close-range camera;
s5, calculating the angles of all joints of the six-axis mechanical arm and updating the angles of the six joints after calculating the jacobian matrix based on the image information and the jacobian matrix at the tail end of the mechanical arm;
s6, judging whether the pixel coordinates of the actual feature points are consistent with the pixel coordinates of the expected feature points, if so, starting grabbing, and if not, returning to the step S5.
In one embodiment, in the control system of the fruit and vegetable picking mechanical arm, the upper computer control system obtains three-dimensional coordinate information of the target fruit through a deep learning algorithm according to image information transmitted by a distant view camera.
In one embodiment, the deep learning algorithm employs a YOLO V5-based network model to accomplish the identification of the target fruit.
Specifically, the method is applied to a fruit and vegetable picking mechanical arm system based on the YOLO V5 network model, and apple fruits are selected in the fruit and vegetable picking mechanical arm system to finish pre-experiment judgment, wherein accuracy of fruit coordinates under three-dimensional coordinate information is obtained through a perspective camera, 1000 pictures of apple trees under different conditions are collected and data enhancement and expansion data sets are performed, labelImg is used for marking the apple fruits by utilizing minimum circumscribed rectangles of each apple, and meanwhile, the method is characterized in that: after the training set and the verification set are divided according to the proportion of 2, a YOLO V5-based network model is adopted for detection, the size of the YOLO V5-based network model is 24.4M, as shown in figure 3, the experimental result shows that the average detection precision is 94.5%,
in one embodiment, the upper computer control system generates the arm end motion control instruction by establishing an obstacle avoidance path planning algorithm based on the arm end.
In one embodiment, the obstacle avoidance path planning algorithm performs obstacle avoidance path planning calculation by establishing a bidirectional rapid expansion random tree based on improvement, wherein the improvement comprises adding a target point deflection strategy and performing local optimization in the path searching process.
Specifically, the improved bidirectional rapid expansion random tree is used for performing obstacle avoidance path planning calculation, a random tree expansion node is added between a starting point and a target point based on the idea of multipoint sampling, the number of original random trees is multiplied, so that the searching efficiency of the nodes is improved, meanwhile, a target deflection strategy is introduced on the basis, namely, a target gravitation function is superimposed on the root node of each random tree, the searching range of a dead space is reduced, an improved node expansion schematic diagram is shown in fig. 4, the target gravitation function is G (n), and the calculation formula of G (n) is as follows:
G(n)＝u*ε*||P goal -P near || 2
u is the extended step size, ε is the gravitation function, ||P goal -P near || 2 Is the square of the Euclidean distance from the target point Pgol to the nearest point Pnear in the random tree.
P0 is an original expansion node, pnew1 is an expansion node after the target gravitation function is introduced, pnew2 is a latest expansion node after the gradual optimizing strategy is added.
In one embodiment, the motion control command is issued to a robot arm control system to generate a robot arm joint value sequence corresponding to the motion control command, and the robot arm is controlled to move to the vicinity of the target fruit according to the robot arm joint value sequence.
In one embodiment, the vision system assists, through the image information transmitted by the near-field camera, the image information is processed to obtain the image characteristics, and the vision servo control algorithm based on the image characteristics is adopted to control the tail end of the mechanical arm to adjust the gesture.
In one embodiment, the visual servo control algorithm is a closed-loop control algorithm based on image information, and the fruit and vegetable picking position deviation is corrected through feedback adjustment.
Specifically, the deviation correction in the visual servo control algorithm specifically comprises transverse deviation, longitudinal deviation and area deviation, and the transverse deviation, longitudinal deviation and area deviation of the target fruit image and the expected image are transmitted to an upper computer control system, and the adjusted joint value sequence is issued to a mechanical arm control system to realize secondary fine adjustment of the tail end control of the mechanical arm.
In one embodiment, the lower computer control system controls the screw transmission system to adjust proper picking height when receiving three-dimensional coordinate information of the fruit tree transmitted by the upper computer control system, so as to complete the height control of the fruit and vegetable picking lifting platform.
Specifically, as shown in fig. 1, the upper computer control system of the present invention is encapsulated with an obstacle avoidance path planning algorithm, a visual servo control algorithm, an image processing and deep learning algorithm; the lower computer control system is communicated with the upper computer control system through serial port communication, and comprises a lower computer control box, wherein an STM32F103 control board and a stepping motor controller are arranged in the lower computer control box; the stepping motor controller lifts the platform to perform ascending and descending motions; the mechanical arm control system is communicated with the upper computer control system through a network cable, and a mechanical arm controller is arranged in the mechanical arm control system and used for controlling the angle value of a mechanical arm joint; the vision system is communicated with the upper computer control system through the USB device and comprises a near view camera and a far view camera.
In summary, in the fruit and vegetable picking mechanical arm control system based on the dual-phase combined positioning in the patent, firstly, three-dimensional coordinate information of a target fruit transmitted by a distant camera is obtained in an upper computer control system, the three-dimensional coordinate information is converted to generate a corresponding mechanical arm joint value sequence, the mechanical arm joint value sequence is sent to the mechanical arm control system, the tail end of the mechanical arm is controlled to reach the vicinity of the target fruit through the improved obstacle avoidance path planning algorithm designed by the invention, on the basis, image information transmitted by a close camera is obtained in the upper computer control system, expected image characteristics are obtained after image processing, and image recognition errors are calculated according to the image characteristics, if the image recognition errors do not meet the requirements, the pose of an actuator at the tail end of the mechanical arm is continuously adjusted through a visual servo control algorithm based on the image characteristics, fruit and vegetable positioning is completed and fruit and vegetable grabbing are carried out until the expected errors in the image recognition errors are converged to 0, and the fruit and vegetable picking mechanical arm control system based on the dual-phase combined positioning is used for further improving the grabbing success rate and laying a foundation for improving the automation and the intelligent fruit and vegetable picking mechanical arm control system.
Claims (10)
1. The fruit and vegetable picking mechanical arm control system based on the double-phase combined positioning is characterized by comprising a vision system, an upper computer control system, a mechanical arm control system and a lower computer control system; the upper computer control system generates a motion control instruction according to image information acquired by the vision system, the motion control instruction is sent to the robot arm control system to generate a robot arm joint value sequence, and after the robot arm joint value sequence is sent to the lower computer control system, the lower computer control system is assisted by the vision system to execute the picking action control of fruits at the tail end of the robot arm.
2. The fruit and vegetable picking mechanical arm control system based on dual-camera combined positioning of claim 1, wherein the vision system comprises a far view camera and a near view camera.
3. The fruit and vegetable picking mechanical arm control system based on double-camera combined positioning according to claim 2, wherein in the fruit and vegetable picking mechanical arm control system, the upper computer control system obtains three-dimensional coordinate information of a target fruit through a deep learning algorithm according to image information transmitted by a distant view camera.
4. The fruit and vegetable picking mechanical arm control system based on double-phase combined positioning according to claim 3, wherein the deep learning algorithm is used for completing identification of target fruits by using a YOLO V5 network model.
5. The fruit and vegetable picking mechanical arm control system based on double-camera combined positioning according to claim 3, wherein the upper computer control system generates mechanical arm tail end motion control instructions by establishing an obstacle avoidance path planning algorithm based on the mechanical arm tail end.
6. The fruit and vegetable picking mechanical arm control system based on double-camera combined positioning according to claim 5, wherein the obstacle avoidance path planning algorithm performs obstacle avoidance path planning calculation by establishing a bidirectional rapid expansion random tree based on improvement, and the improvement comprises adding a target point deviation strategy and performing local optimization in a path searching process.
7. The fruit and vegetable picking mechanical arm control system based on double-phase combined positioning according to claim 5, wherein the motion control command is issued to the mechanical arm control system to generate a mechanical arm joint value sequence corresponding to the motion control command, and the mechanical arm is controlled to move to the vicinity of the target fruit according to the mechanical arm joint value sequence.
8. The fruit and vegetable picking mechanical arm control system based on double-camera combined positioning according to claim 1, wherein the vision system is assisted, image characteristics are obtained through image information transmitted by a close-range camera, and the tail end of the mechanical arm is controlled to adjust the gesture by adopting a vision servo control algorithm based on the image characteristics.
9. The control system of the fruit and vegetable picking mechanical arm based on double-phase combined positioning according to claim 8, wherein the visual servo control algorithm is a closed-loop control algorithm based on image information, and fruit and vegetable picking position deviation correction is performed through feedback adjustment.
10. The fruit and vegetable picking mechanical arm control system based on double-phase combined positioning according to claim 1, wherein the lower computer control system controls the screw transmission system to adjust proper picking height when receiving three-dimensional coordinate information of fruit trees transmitted by the upper computer control system, so as to complete height control of a fruit and vegetable picking lifting platform.
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