Adaptive control method for cutting robots

Abstract

The present invention relates to the technical field of mechanical control methods and provides an adaptive control method for a cutting robot. The method first determines the target position of the cutting robot's blade. Then, based on the target position, a model predictive control algorithm and a blade follower mechanism are applied to calculate the optimal motion parameters of the cutting robot's three-degree-of-freedom robotic arm. Finally, based on the optimal motion parameters, the three-degree-of-freedom robotic arm is controlled to drive the blade to perform a cutting-type plucking operation on the tea bushes to be cut. This method utilizes the current height of the blade and its current vertical speed at the current moment through the blade follower mechanism to determine the predicted height of the blade at the next moment, ensuring that the blade can accurately follow the shape of the tea bushes to perform plucking operations and adapt to changes in the height of the tea bushes to be cut. Combining the model predictive control algorithm and the blade follower mechanism ensures that the blade accurately matches the shape and position of the tea bushes to be cut in three-dimensional space.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical control methods, and in particular to an adaptive control method for a cutting robot. Background Art

[0002] Tea picking is a crucial technology in modern agricultural production. To improve labor efficiency and production scale, tea plucking machines can be used to automate the process, replacing manual labor. Tea plucking machines have evolved from small handheld models to large self-propelled and ride-on models, offering a variety of picking methods, including robotic gripping, multi-cutting, horizontal disc blade, and spiral hob blade.

[0003] However, existing tea picking methods using tea plucking machines mainly rely on simple control strategies, such as the Proportional-Integral-Derivative Control (PID) control algorithm, which is difficult to cope with multivariable systems in complex tea garden environments. This results in inaccurate control of the cutter position and posture, and is unable to achieve adaptive adjustment in different tea garden environments, resulting in low picking efficiency and poor tea quality. Summary of the Invention

[0004] The present invention provides a cutting robot adaptive control method to solve the defects in the prior art.

[0005] The present invention provides a cutting robot adaptive control method, comprising:

[0006] Determine the target position of the cutting knife of the cutting robot;

[0007] Based on the target posture, the optimal motion parameters of the three-degree-of-freedom manipulator of the cutting robot are calculated by applying a model predictive control algorithm and a cutting knife following mechanism;

[0008] Based on the optimal motion parameters, the three-degree-of-freedom robotic arm is controlled to drive the cutting knife to perform cutting-type picking on the tea trees to be cut;

[0009] The cutting knife following mechanism is used to determine the predicted height of the cutting knife at the next moment based on the current height of the cutting knife and the current speed in the vertical direction at the current moment.

[0010] According to the present invention, an adaptive control method for a cutting robot is provided, wherein the optimal motion parameters of the cutting robot's mechanical arm are calculated based on the target posture by applying a model predictive control algorithm and a cutter follower mechanism, including:

[0011] Based on the current posture of the cutting knife, a system state model of the cutting robot is constructed; the current posture includes the current height;

[0012] constructing a system control input for the cutting robot based on current motion parameters of the robotic arm;

[0013] Determining a system state equation of the cutting robot based on the system state model and the system control input, and calculating a predicted position and posture of the cutting knife at a next moment based on the system state equation; the predicted position and posture includes the predicted height;

[0014] Based on the predicted posture, the system control input and the target posture, an objective function is constructed, and the objective function is minimized, and the system control input is optimized to obtain the optimal motion parameters.

[0015] According to a cutting robot adaptive control method provided by the present invention, the calculation formula of the objective function includes:

[0016] ;

[0017] Wherein, J is the objective function, N is the prediction time step, is the predicted position of the cutting knife at time t+k, is the target position of the cutting knife at time t+k, is the system control input at time t+k-1, t is the current time, k is the number of the predicted time step, is the smoothing factor, and ||*|| represents the Euclidean norm of *.

[0018] According to an adaptive control method for a cutting robot provided by the present invention, the objective function is constructed based on the predicted posture, the system control input, and the target posture, and the objective function is minimized, the system control input is optimized, and the optimal motion parameters are obtained, which then includes:

[0019] Monitoring the real-time position of the cutting knife;

[0020] Based on the error between the real-time posture and the target posture, the optimal motion parameter is adjusted in real time.

[0021] According to an adaptive control method for a cutting robot provided by the present invention, the target posture is determined by a host computer, and the host computer is used to obtain point cloud data of the tea tree to be cut, and based on the point cloud data, extract key feature points of the tea tree to be cut, and calculate the target posture based on the key feature points.

[0022] The present invention also provides a cutting robot, comprising: a control unit and a robot body;

[0023] The control unit is arranged on the robot body, a three-degree-of-freedom robotic arm is arranged on the robot body, and a cutting knife is connected to the end of the three-degree-of-freedom robotic arm;

[0024] The control unit is used to execute the above-mentioned cutting robot adaptive control method.

[0025] The cutting robot is characterized in that an inertial sensor is further provided at the end of the three-degree-of-freedom robotic arm, and the inertial sensor is connected to the control unit;

[0026] The inertial sensor is used to monitor the real-time position and posture of the cutting knife and transmit the real-time position and posture to the control unit.

[0027] According to a cutting robot provided by the present invention, the cutting knife is arranged in a cutting module, and the three-degree-of-freedom robotic arm is arranged in a motion module;

[0028] The cutting module also includes a cutter motor, which is connected to the control unit and the cutter respectively, and the control unit is further used to control the cutter motor to drive the cutter to rotate;

[0029] The motion module also includes joint motors, which are respectively connected to the control unit and each joint of the three-degree-of-freedom robotic arm. The control unit is also used to control the joint motors to drive each joint to move.

[0030] A cutting robot provided according to the present invention further includes a servo drive;

[0031] The control unit is further configured to generate a controller area network message based on the optimal motion parameters, and send the controller area network message to the servo drive;

[0032] The servo driver is used to parse the controller area network message and control the movement of the joint motor according to the parsing result.

[0033] A cutting robot according to the present invention further includes a host computer;

[0034] The robot body is provided with a depth camera and / or a negative pressure collection module;

[0035] The depth camera is used to collect point cloud data of the tea tree to be cut and transmit the point cloud data to the host computer;

[0036] The host computer is used to obtain the point cloud data, extract key feature points of the tea tree to be cut based on the point cloud data, and calculate the target pose based on the key feature points;

[0037] The negative pressure collection module is installed on the robot body and is used to collect the picked tea leaves.

[0038] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the adaptive control method for a cutting robot as described above is implemented.

[0039] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cutting robot adaptive control method as described in any one of the above.

[0040] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any one of the above-described cutting robot adaptive control methods.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The adaptive control method for a cutting robot provided by the present invention first determines the target position of the robot's cutting blade. Based on the target position, a model predictive control algorithm and a blade-following mechanism are then applied to calculate the optimal motion parameters of the cutting robot's three-degree-of-freedom robotic arm. Finally, based on the optimal motion parameters, the three-degree-of-freedom robotic arm is controlled to drive the cutting blade to perform cutting-type plucking of tea trees. This method utilizes the current height and vertical velocity of the cutting blade at the current moment through the blade-following mechanism to determine the predicted height of the cutting blade at the next moment, ensuring that the cutting blade can accurately follow the shape of the tea bush during the plucking operation and adapt to the height changes of the tea bush. Combining the model predictive control algorithm and the blade-following mechanism ensures that the cutting blade accurately matches the shape and position of the tea bush in three-dimensional space. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings in the following description without any creative work.

[0044] Figure 1 1 is a flow chart of the adaptive control method for a cutting robot provided by the present invention;

[0045] Figure 2 This is one of the structural schematic diagrams of the cutting robot provided by the present invention;

[0046] Figure 3 This is the second structural diagram of the cutting robot provided by the present invention;

[0047] Figure 4 It is a schematic structural diagram of the adaptive control device for a cutting robot provided by the present invention;

[0048] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention.

[0049] Among them, 21, control unit; 22, robot body; 23, three-degree-of-freedom robotic arm; 24, cutting knife; 25, inertial sensor; 26, depth camera; 27, camera mounting bracket; 28, negative pressure exhaust fan; 29, funnel-shaped collection pipe; 210, tea cart; 211, knife-edge fan; 212, connecting rod; 213, track wheel; 214, drive motor; 215, battery. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0051] Existing robotic tea-picking methods struggle to cope with the multivariable systems found in complex tea garden environments. This results in inaccurate control of the blade's position and posture, and an inability to achieve adaptive adjustments under varying tea garden conditions. This leads to low picking efficiency and poor tea quality. To address this, an embodiment of the present invention provides an adaptive control method for a cutting robot.

[0052] Figure 1 FIG. 1 is a flow chart of an adaptive control method for a cutting robot provided in an embodiment of the present invention, as shown in FIG. Figure 1 As shown, the method includes:

[0053] S1, determining the target position of the cutting knife of the cutting robot;

[0054] S2, based on the target posture, applying a model predictive control algorithm and a cutter follower mechanism to calculate optimal motion parameters of a three-degree-of-freedom manipulator of the cutting robot;

[0055] S3, based on the optimal motion parameters, controlling the three-degree-of-freedom robotic arm to drive the cutting knife to perform cutting-type picking on the tea bushes to be cut;

[0056] The cutting knife following mechanism is used to determine the predicted height of the cutting knife at the next moment based on the current height of the cutting knife and the current speed in the vertical direction at the current moment.

[0057] Specifically, the adaptive control method for a cutting robot provided in an embodiment of the present invention is executed by a control unit built into the cutting robot. The control unit can be a lower computer that can receive the target position of the cutting knife of the cutting robot determined by the upper computer.

[0058] First, step S1 is executed to receive the target position of the cutting knife of the cutting robot sent by the host computer. The cutting robot may include a three-degree-of-freedom robotic arm, and the end of the three-degree-of-freedom robotic arm is connected to the cutting knife. The target position of the cutting knife refers to the optimal cutting position of the cutting knife, which may include the optimal cutting position of the cutting knife. and optimal cutting posture .in, are the optimal coordinates of the cutting knife on the x, y, and z axes, respectively. are the optimal rotation angles of the cutting knife around the x, y, and z axes respectively.

[0059] Then, step S2 is performed. Using the target pose, a model predictive control (MPC) algorithm and a cutter follower mechanism are applied to calculate the optimal motion parameters of the cutting robot's three-degree-of-freedom arm. The three-degree-of-freedom arm may include three joints, and the optimal motion parameters may include the rotation angles of the three joints and the velocities of the three-degree-of-freedom arm in the x, y, and z axes.

[0060] The blade tracking mechanism is used to determine the blade's predicted height at the next moment based on the blade's current height (i.e., position along the z-axis) and its current vertical speed (i.e., along the z-axis). This ensures that the blade can adjust in real time to the height fluctuations of the tea bushes being harvested. The core goal of this blade tracking mechanism is to handle vertical variations in the tea field topography, ensuring that the blade's height always matches the optimal cutting position for the tea bushes being harvested.

[0061] The cutting blade's position in the x- and y-axes, as well as its posture, can be adjusted using the optimal motion parameters of the 3-DOF manipulator, determined by a model predictive control algorithm. This algorithm comprehensively considers the cutting blade's three-dimensional position and posture, optimizing the manipulator's motion parameters in real time to ensure the cutting blade accurately follows the pre-set path in both the x- and y-axes while maintaining the appropriate cutting angle.

[0062] Finally, step S3 is executed to use the optimal motion parameters to control the three-degree-of-freedom robotic arm to drive the cutting knife to move, so that the cutting knife is in the target position and the tea trees to be cut are picked by cutting.

[0063] The adaptive control method for a cutting robot provided in an embodiment of the present invention first determines the target position of the cutting robot's blade. Based on the target position, a model predictive control algorithm and a blade-following mechanism are then applied to calculate the optimal motion parameters of the cutting robot's three-degree-of-freedom robotic arm. Finally, based on the optimal motion parameters, the three-degree-of-freedom robotic arm is controlled to drive the blade to perform a cutting-type plucking operation on the tea bushes to be cut. This method utilizes the current height of the blade and its current vertical velocity at the current moment through the blade-following mechanism to determine the predicted height of the blade at the next moment, ensuring that the blade can accurately follow the shape of the tea bushes to perform plucking operations and adapt to the height changes of the tea bushes to be cut. Combining the model predictive control algorithm and the blade-following mechanism ensures that the blade accurately matches the shape and position of the tea bushes to be cut in three-dimensional space.

[0064] On the basis of the above embodiment, the method of calculating the optimal motion parameters of the three-degree-of-freedom manipulator of the cutting robot based on the target posture and applying the model predictive control algorithm and the cutter follower mechanism includes:

[0065] Based on the current posture of the cutting knife, a system state model of the cutting robot is constructed; the current posture includes the current height;

[0066] constructing a system control input for the cutting robot based on current motion parameters of the three-degree-of-freedom robotic arm;

[0067] Determining a system state equation of the cutting robot based on the system state model and the system control input, and calculating a predicted position and posture of the cutting knife at a next moment based on the system state equation; the predicted position and posture includes the predicted height;

[0068] Based on the predicted posture, the system control input and the target posture, an objective function is constructed, and the objective function is minimized, and the system control input is optimized to obtain the optimal motion parameters.

[0069] Specifically, when calculating the optimal motion parameters of a cutting robot's three-degree-of-freedom manipulator, the system state model of the cutting robot is first constructed using the current position of the cutting blade. The current position of the cutting blade can be acquired using an inertial measurement unit (IMU) and transmitted to the execution entity.

[0070] The current state vector of the system state model Including the current position and posture of the cutting knife, that is, the current position and current posture of the cutting knife, there are:

[0071] ;

[0072] in, Indicates the current position of the cutting knife, which are the current coordinates of the cutting knife on the x, y, and z axes. Indicates the current posture of the cutting knife, which are the rotation angles of the cutting knife around the x, y, and z axes. It can be understood that The current height of the cutter.

[0073] After that, the current motion parameters of the three-degree-of-freedom manipulator are used to construct the system control input of the cutting robot. . System control input Including the current speed of the three-degree-of-freedom robot arm in the x, y, and z axes and the current rotation angles of the three joints, there are:

[0074] ;

[0075] in, are the current rotation angles of the three joints of the three-degree-of-freedom robotic arm, are the current speeds of the three-degree-of-freedom robotic arm in the x-, y-, and z-axis directions respectively.

[0076] Afterwards, the current state vector of the system state model is used and system control inputs , determine the system state equation of the cutting robot. This system state equation can represent the evolution process of the state vector of the system state model, that is:

[0077] ;

[0078] in, is the state vector of the system state model at the next moment (i.e., moment t+1), is the state transition matrix, which represents the natural evolution of the state vector of the system state model without external system control input; is the input matrix, which is used to describe the influence of the system control input on the state vector of the system state model; is an external disturbance matrix used to simulate the effects of environmental noise or terrain undulation.

[0079] Using the system state equation, the predicted position of the cutter at the next moment can be calculated, that is, the state vector of the system state model at the next moment The predicted position and posture may include the predicted position and predicted posture of the cutting knife. The predicted position may include the predicted height of the cutting knife. And the predicted coordinates of the cutter on the x-axis and y-axis respectively 、 .

[0080] Finally, the objective function is constructed using the predicted pose, system control input, and target pose:

[0081] ;

[0082] Where N is the prediction time step, J is the objective function, which represents the sum of errors in the next N steps. is the predicted position of the cutting knife at time t+k, is the target position of the cutting knife at time t+k, is the system control input at time t+k-1, t is the current time, k is the number of the predicted time step, is a smoothing factor used to balance the smoothness and accuracy of the cutting action, and ||*|| represents the Euclidean norm of *.

[0083] By minimizing the objective function, the system control inputs can be optimized to obtain the optimal motion parameters. This objective function optimization process selects the control input sequence that minimizes the error within the constraints of the given system control inputs, thereby achieving precise control of the cutter's position.

[0084] Since the real-time feedback and correction mechanism in the prior art is not perfect, the precise positioning and cutting of the cutter are limited. Therefore, based on the above embodiment, the objective function is constructed based on the predicted posture, the system control input and the target posture, and the objective function is minimized, the system control input is optimized, and the optimal motion parameters are obtained, which then includes:

[0085] Monitoring the real-time position of the cutting knife;

[0086] Based on the error between the real-time posture and the target posture, the optimal motion parameter is adjusted in real time.

[0087] Specifically, after obtaining the optimal motion parameters, the real-time position and posture of the cutting knife can be monitored by the IMU, and then the error between the real-time position and posture of the cutting knife and the target position can be used to adjust the optimal motion parameters in real time.

[0088] The error between the real-time pose and the target pose may include the height error of the cutting knife. and attitude error , height error and attitude error They can be expressed as:

[0089] ;

[0090] in, is the optimal height of the cutter at time t, that is, the optimal coordinate of the cutter on the z-axis at time t, is the real-time height of the cutting knife at time t, that is, the real-time coordinate of the cutting knife on the z-axis at time t. are the optimal rotation angles of the cutting knife around the x, y, and z axes at time t, respectively. They are the real-time rotation angles of the cutting knife around the x, y, and z axes at time t.

[0091] The error correction control signal generation formula of the optimal motion parameters is:

[0092] ;

[0093] in, To correct the gain, it is used to convert the error into a specific control signal; is the error correction control signal at time t.

[0094] Through the error correction control signal, the three-degree-of-freedom robotic arm can be controlled to move, and then the position and posture of the cutter in all directions can be quickly adjusted to ensure that the cutter is accurately aligned with the optimal picking position of the tea tree to be cut during the actual picking process, and high precision can be maintained even in complex terrain and external interference.

[0095] Based on the above embodiment, the target posture is determined by a host computer, which is used to obtain point cloud data of the tea tree to be cut, and based on the point cloud data, extract key feature points of the tea tree to be cut, and calculate the target posture based on the key feature points.

[0096] Specifically, the three-dimensional spatial data of the tea trees to be harvested can be captured by a depth camera to generate high-precision point cloud data. The point cloud data of the tea trees to be harvested reflects the spatial feature information such as the height, density, and curvature of the tea trees to be harvested.

[0097] The depth camera then sends the point cloud data to a host computer. The host computer analyzes the point cloud data to estimate the optimal picking area for the tea bushes, extract key feature points, and calculate the target position of the blade based on these key feature points. This target position can include the blade's optimal cutting position and posture.

[0098] Based on the above embodiments, Figure 2 As shown, an embodiment of the present invention further provides a cutting robot, comprising: a control unit 21 and a robot body 22;

[0099] The control unit 21 is provided on the robot body 22 , and a three-degree-of-freedom robotic arm 23 is provided on the robot body 22 , and a cutting knife 24 is connected to the end of the three-degree-of-freedom robotic arm 23 ;

[0100] The control unit 21 is used to execute the cutting robot adaptive control method provided in the above embodiments.

[0101] Specifically, during the design process of the robot body 22, the focus was on lightweighting the vehicle body. It is built with aluminum alloy, which can reduce the weight of the vehicle body while maintaining sufficient structural strength to ensure stable operation in complex terrain.

[0102] The three-degree-of-freedom robotic arm is controlled by the control unit 21 to drive the cutter to move, and the cutter is controlled to cut and pick the tea trees to be cut. In this way, the cutter can accurately follow the shape of the tea ridge to perform picking operations, so as to adapt to the height changes of the tea trees to be cut, and ensure that the cutter accurately matches the shape and position of the tea trees to be cut in three-dimensional space.

[0103] Based on the above embodiments, Figure 3 As shown, an inertial sensor 25 is also provided at the end of the three-degree-of-freedom robotic arm 23. The inertial sensor 25 is used to monitor the real-time position and posture of the cutting knife 24. The inertial sensor 25 is connected to the control unit 21 and can transmit the real-time position and posture to the control unit 21. The control unit 21 receives the real-time position and posture and, in combination with the target position and posture, can adjust the optimal motion parameters in real time.

[0104] The existing robot modular design is not perfect, and it is difficult to quickly adjust or upgrade to adapt to different tea garden terrains and picking tasks, which increases the difficulty of operation and maintenance.

[0105] On the basis of the above embodiment, the cutting knife 24 is arranged in the cutting module, and the three-degree-of-freedom robot arm 23 is arranged in the motion module;

[0106] The cutting module also includes a cutter motor, which is connected to the control unit 21 and the cutter 24 respectively. The control unit 21 is also used to control the cutter motor to drive the cutter 24 to rotate, shearing the root and stem parts of the tea leaves on the tea tree to be cut, completing cutting-type picking.

[0107] The motion module also includes joint motors, which are respectively connected to the control unit 21 and each joint of the three-degree-of-freedom robotic arm 23. The control unit 21 is also used to control the joint motors to drive each joint to move.

[0108] Specifically, in this embodiment of the present invention, control unit 21 is a slave computer, which may employ a 407 industrial drive control board and is responsible for driving and communicating with the joint motors of the three-degree-of-freedom robotic arm 23. Control unit 21 may be configured with a CAN communication interface module, each of which is assigned a node address to facilitate communication with the various joint motors of the three-degree-of-freedom robotic arm 23.

[0109] The cutting and motion modules in the cutting robot can be independently installed and removed, simplifying the cutting robot's maintenance and upgrades, allowing it to adapt to different tea picking tasks and environmental conditions. Standardized interfaces ensure efficient interoperability between modules, facilitating rapid replacement and configuration. This modular design also allows for easy expansion of the machine's functionality. By replacing or adding new modules, the cutting robot can adapt to new crops or different picking requirements, further extending its application range and service life.

[0110] In this embodiment of the present invention, by introducing a cutting module and a motion module, a modular design of the cutting robot can be achieved. This reduces the cutting robot's maintenance costs and allows operators to quickly replace faulty parts or upgrade the cutting robot, ensuring the robot's long-term and efficient operation. The modular design of the cutting robot not only improves its adaptability and flexibility in various complex environments, but also opens up greater possibilities for future technological upgrades and functional expansion.

[0111] Based on the above embodiment, the cutting robot further includes a servo drive. The control unit 21 is further configured to generate a controller area network (CAN) message using the optimal motion parameters and send the CAN message to the servo drive. The CAN message contains the optimal motion parameters for each joint of the three-degree-of-freedom robotic arm 23.

[0112] The servo driver is used to parse the CAN message and control the movement of the joint motor according to the analysis result, so that the joint motor drives the movement of each joint of the three-degree-of-freedom robot arm 23 according to the optimal motion parameters, adjusts the actual position and posture of the cutting knife, and makes the cutting knife reach the predetermined target posture.

[0113] Based on the above embodiments, Figure 3 As shown, the cutting robot also includes a host computer; a depth camera 26 and / or a negative pressure collection module is provided on the robot body 22; the depth camera 26 is used to collect point cloud data of the tea tree to be cut and transmit the point cloud data to the host computer; the host computer is used to obtain the point cloud data, extract the key feature points of the tea tree to be cut based on the point cloud data, and calculate the target posture based on the key feature points; the negative pressure collection module is installed on the robot body 22 for collecting the picked tea leaves.

[0114] Specifically, the depth camera 26 can be fixed to the robot body 22 via a camera mounting bracket 27. The negative pressure collection module can include a negative pressure exhaust fan 28, a funnel-shaped collection pipe 29, a tea trough 210, and a knife-edge blower 211. The knife-edge blowers 211 can include two. The negative pressure exhaust fan 28 can be installed in the tea trough 210, which is connected to the robot body 22 via a connecting rod 212.

[0115] The funnel-shaped collecting pipe 29 can be arranged behind the cutting knife 24 , and the knife-edge blower 211 can be arranged at the cutting edge of the cutting knife 24 .

[0116] Start the knife-edge fan 211, which blows the tea leaves picked by the cutting knife 24 into the funnel-shaped collecting tube 29 for collection, and forms a negative pressure inside the tea trolley 210 through the negative pressure exhaust fan 28, sucking the tea leaves into the tea trolley 210 through the funnel-shaped collecting tube 29, completing the automated picking and collection process.

[0117] In terms of structural design, existing robots suffer from poor terrain adaptability, excessive weight, and insufficient modularity. Many robots struggle to maintain stability in complex terrain, and their overall structure is too bulky, resulting in high energy consumption and poor operational flexibility.

[0118] Based on the above embodiments, Figure 3 As shown, the robot body 22 moves on track wheels 213, enabling the cutting robot to maintain stability and good maneuverability in the rugged terrain of the tea garden. Track wheels 213 are set on both sides of the robot body 22, each driven by an independent drive motor 214. This design allows the cutting robot to move synchronously and efficiently pick tea leaves in a straight line. It also supports differential turning, allowing the cutting robot to flexibly adjust its direction between tea ridges to adapt to the different terrains of the tea garden.

[0119] Based on the above embodiments, Figure 3 As shown, the control unit 21 can be arranged in a control cabinet, and the control cabinet can be arranged above the track wheel 213. A battery 215 is also arranged in the control cabinet, and the battery 215 is used to power the cutting robot.

[0120] Based on the above embodiment, the control unit 21 may also have a built-in exception handling control program to deal with abnormal situations, such as motor overload, communication interruption, or cutter obstruction. In abnormal situations, the control unit 21 can immediately control the three-degree-of-freedom robotic arm 23 to stop moving to prevent damage to the equipment or tea plants.

[0121] The control unit 21 can also load a pre-programmed test program and execute it to perform step-by-step motion control tests to verify that the cutter's trajectory is as expected. By adjusting MPC parameters and motion logic, the cutter can ensure precise cutting. Integrating the motion control program into the entire cutting robot's control system ensures that the picking module works in conjunction with the linear guide system, vision module, and fan system to complete automated picking tasks.

[0122] In summary, the cutting robot provided in the embodiment of the present invention realizes the automated harvesting of tea leaves through the intelligent scheduling of the new structural design and the integrated control unit, thereby improving the picking efficiency and tea quality. The motion module can ensure the stable movement and flexible turning of the cutting robot on different tea garden terrains. The negative pressure collection module can ensure that the cut tea leaves are not missed. The inertial sensor can collect the real-time position and posture of the cutting knife, laying the foundation for the precise control of the cutting knife. The control unit can accurately control the position and posture of the cutting knife through the model predictive control algorithm, and adjust the height of the cutting knife in real time through the cutting knife follow-up mechanism to ensure the precise cutting of the cutting knife in complex terrain. Automated picking can reduce manual intervention, reduce economic costs, improve production efficiency, and meet the requirements of sustainable development.

[0123] like Figure 4 As shown, based on the above embodiment, an embodiment of the present invention provides an adaptive control device for a cutting robot, comprising:

[0124] A target posture determination module 41 is used to determine the target posture of the cutting knife of the cutting robot;

[0125] a motion parameter determination module 42 for calculating the optimal motion parameters of the three-degree-of-freedom manipulator of the cutting robot based on the target posture by applying a model predictive control algorithm and a cutter follower mechanism;

[0126] A picking control module 43 is configured to control the three-degree-of-freedom robotic arm to drive the cutting knife to perform cutting-type picking on the tea bushes to be cut based on the optimal motion parameters;

[0127] The cutting knife following mechanism is used to determine the predicted height of the cutting knife at the next moment based on the current height of the cutting knife and the current speed in the vertical direction at the current moment.

[0128] On the basis of the above embodiment, in the cutting robot adaptive control device provided in the embodiment of the present invention, the motion parameter determination module is specifically used to:

[0129] Based on the current posture of the cutting knife, a system state model of the cutting robot is constructed; the current posture includes the current height;

[0130] constructing a system control input for the cutting robot based on current motion parameters of the three-degree-of-freedom robotic arm;

[0131] Determining a system state equation of the cutting robot based on the system state model and the system control input, and calculating a predicted position and posture of the cutting knife at a next moment based on the system state equation; the predicted position and posture includes the predicted height;

[0132] Based on the predicted posture, the system control input and the target posture, an objective function is constructed, and the objective function is minimized, and the system control input is optimized to obtain the optimal motion parameters.

[0133] On the basis of the above embodiment, in the cutting robot adaptive control device provided in the embodiment of the present invention, the calculation formula of the objective function includes:

[0134] ;

[0135] Wherein, J is the objective function, N is the prediction time step, is the predicted position of the cutting knife at time t+k, is the target position of the cutting knife at time t+k, is the system control input at time t+k-1, t is the current time, k is the number of the predicted time step, is the smoothing factor, and ||*|| represents the Euclidean norm of *.

[0136] On the basis of the above embodiment, the cutting robot adaptive control device provided in the embodiment of the present invention further includes a parameter adjustment module for:

[0137] Monitoring the real-time position of the cutting knife;

[0138] Based on the error between the real-time posture and the target posture, the optimal motion parameter is adjusted in real time.

[0139] On the basis of the above embodiments, in the adaptive control device for a cutting robot provided in an embodiment of the present invention, the target posture is determined by a host computer, and the host computer is used to obtain point cloud data of the tea tree to be cut, and based on the point cloud data, extract key feature points of the tea tree to be cut, and calculate the target posture based on the key feature points.

[0140] Specifically, the functions of each module in the adaptive control device of the cutting robot provided in the embodiment of the present invention correspond one-to-one to the operating procedures of each step in the above-mentioned method embodiment, and the effects achieved are also consistent. Please refer to the above-mentioned embodiment for details, and no further details will be given in the embodiment of the present invention.

[0141] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communication bus 540. The processor 510, the communications interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the cutting robot adaptive control method provided in the above embodiments.

[0142] Furthermore, the logic instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0143] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the cutting robot adaptive control method provided in the above embodiments.

[0144] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the cutting robot adaptive control method provided in the above embodiments.

[0145] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0146] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A cutting robot adaptive control method, characterized in that: include: Determine the target position of the cutting knife of the cutting robot; Based on the target posture, the optimal motion parameters of the three-degree-of-freedom manipulator of the cutting robot are calculated by applying a model predictive control algorithm and a cutting knife following mechanism; Based on the optimal motion parameters, the three-degree-of-freedom robotic arm is controlled to drive the cutting knife to perform cutting-type picking on the tea trees to be cut; The cutting knife following mechanism is used to determine the predicted height of the cutting knife at the next moment based on the current height of the cutting knife and the current speed in the vertical direction at the current moment; The method of calculating the optimal motion parameters of the three-degree-of-freedom manipulator of the cutting robot based on the target posture and applying the model predictive control algorithm and the cutter follower mechanism includes: Based on the current posture of the cutting knife, a system state model of the cutting robot is constructed; the current posture includes the current height; constructing a system control input for the cutting robot based on current motion parameters of the three-degree-of-freedom robotic arm; Determining a system state equation of the cutting robot based on the system state model and the system control input, and calculating a predicted position and posture of the cutting knife at a next moment based on the system state equation; the predicted position and posture includes the predicted height; Based on the predicted posture, the system control input and the target posture, an objective function is constructed, and the objective function is minimized, and the system control input is optimized to obtain the optimal motion parameters.

2. The cutting robot adaptive control method according to claim 1, characterized in that: The calculation formula of the objective function includes: ； Wherein, J is the objective function, N is the prediction time step, is the predicted position of the cutting knife at time t+k, is the target position of the cutting knife at time t+k, is the system control input at time t+k-1, t is the current time, k is the number of the predicted time step, is the smoothing factor, and ||*|| represents the Euclidean norm of *.

3. The cutting robot adaptive control method according to claim 1, characterized in that: The method further comprises: constructing an objective function based on the predicted posture, the system control input, and the target posture, minimizing the objective function, optimizing the system control input, and obtaining the optimal motion parameters; and then comprising: Monitoring the real-time position of the cutting knife; Based on the error between the real-time posture and the target posture, the optimal motion parameter is adjusted in real time.

4. The cutting robot adaptive control method according to any one of claims 1 to 3, characterized in that: The target pose is determined by a host computer, which is used to obtain point cloud data of the tea tree to be cut, extract key feature points of the tea tree to be cut based on the point cloud data, and calculate the target pose based on the key feature points.

5. A cutting robot, characterized in that: include: Control unit and robot body; The control unit is arranged on the robot body, a three-degree-of-freedom robotic arm is arranged on the robot body, and a cutting knife is connected to the end of the three-degree-of-freedom robotic arm; The control unit is used to execute the cutting robot adaptive control method according to any one of claims 1 to 4.

6. The cutting robot according to claim 5, characterized in that: An inertial sensor is also provided at the end of the three-degree-of-freedom robotic arm, and the inertial sensor is connected to the control unit; The inertial sensor is used to monitor the real-time position and posture of the cutting knife and transmit the real-time position and posture to the control unit.

7. The cutting robot according to claim 5, characterized in that: The cutting knife is arranged in the cutting module, and the mechanical arm is arranged in the motion module; The cutting module also includes a cutter motor, which is connected to the control unit and the cutter respectively, and the control unit is further used to control the cutter motor to drive the cutter to rotate; The motion module also includes joint motors, which are respectively connected to the control unit and each joint of the three-degree-of-freedom robotic arm. The control unit is also used to control the joint motors to drive each joint to move.

8. The cutting robot according to claim 7, characterized in that: Also includes servo drives; The control unit is further configured to generate a controller area network message based on the optimal motion parameters, and send the controller area network message to the servo drive; The servo driver is used to parse the controller area network message and control the movement of the joint motor according to the parsing result.

9. The cutting robot according to any one of claims 5 to 8, characterized in that: Also includes the host computer; The robot body is provided with a depth camera and / or a negative pressure collection module; The depth camera is used to collect point cloud data of the tea tree to be cut and transmit the point cloud data to the host computer; The host computer is used to obtain the point cloud data, extract key feature points of the tea tree to be cut based on the point cloud data, and calculate the target pose based on the key feature points; The negative pressure collection module is installed on the robot body and is used to collect the picked tea leaves.

Images