Clinker screw ship unloader control system with collaborative robot

Abstract

The clinker spiral ship unloader control system comprises mechanical arms and the collaborative robot, the multiple mechanical arms are arranged, the mechanical arm at the tail end is provided with a spiral material taking device, and the head end and the tail end of each mechanical arm are each provided with a first inertial measurement unit. The first visual sensor generates first data, the first inertial measurement unit generates second data, the collaborative robot is provided with a second visual sensor and a second inertial measurement unit, the second visual sensor generates third data, and the second inertial measurement unit generates fourth data; the first inertial measurement unit and the second inertial measurement unit are linked with each other and are used for detecting the relative dynamic position between the collaborative robot and the mechanical arm, the third data and the second data interact with each other and are corrected to generate fifth data, and the fifth data are position change and time change between the mechanical arm and the collaborative robot.

Description

Technical Field

[0001] The present invention relates to the field of automatic control systems for clinker screw ship unloaders, and particularly to a control system for a clinker screw ship unloader with a collaborative robot. Background Art

[0002] A screw ship unloader is a special machine that uses a continuous conveying machine to make a head capable of lifting bulk materials, or has the ability to take materials by itself, or is equipped with a material taking and feeding device to continuously lift bulk materials out of the cabin, and then unload them onto the boom or rack and can be transported to the main conveying system on the shore. Using a screw ship unloader can greatly improve the unloading efficiency, and the minimum dust pollution can keep the environment clean, which is efficient and environmentally friendly.

[0003] Due to its large volume, it is difficult to achieve full automation control for existing screw ship unloaders. However, with the progress of the times, how to achieve precise control and precise monitoring of screw ship unloaders is also the key to the design.

[0004] However, existing clinker (such as sand, dust, cement, etc.) is prone to generate dust during loading and unloading, which blocks the line of sight. The existing method is generally to spray liquid to reduce dust in order to achieve position control of the screw feeding device. However, there is still a problem of visual occlusion. Of course, there are also structures that use visual devices to achieve position management. Although this device can achieve the function of remote control, there are still drawbacks in remote control and remote position monitoring, which affects the safety of operation.

[0005] For example, Chinese Patent No. 202111416038.3 uses spatial positioning technology to achieve anti-collision protection for the overall screw ship unloader of the screw feeding device, without detection blind spots, and has high safety and reliability. At the same time, this technology can be integrated with conventional anti-collision protection technologies based on detection means such as limit switches, laser rangefinders, and millimeter-wave radars to form redundant protection in control, further enhancing the safety and reliability of the operation of the screw ship unloader of the screw feeding device.

[0006] However, in the actual system, there are still delays in time and space, so control uncertainties are also the key to monitoring. Summary of the Invention

[0007] The main object of the present invention is to propose a control system for a clinker screw ship unloader with a collaborative robot, aiming to use the collaborative robot and each control system of the screw ship unloader to form a network, which can not only achieve time monitoring but also space monitoring, thereby ensuring the control accuracy under uncertain factors.

[0008] To achieve the above object, the present invention proposes a control system for a clinker screw ship unloader with a collaborative robot, including:

[0009] There are multiple robotic arms, and a spiral material taking device is provided on the robotic arm at the end. First inertial measurement units are respectively provided at the head and the end of the robotic arm. A first vision sensor is provided on the spiral unloader body. The first vision sensor generates first data, and the first inertial measurement unit generates second data;

[0010] There is a collaborative robot, which is provided with a second vision sensor and a second inertial measurement unit. The second vision sensor generates third data and the second inertial measurement unit generates fourth data;

[0011] The first inertial measurement unit and the second inertial measurement unit are interconnected and used to detect the relative dynamic position between the collaborative robot and the robotic arm. The third data and the second data interact with each other and are calibrated to generate fifth data. The fifth data is the position change and time change between the robotic arm and the collaborative robot;

[0012] The first data and the second data generate a first virtual model, which is used to generate the first position data and the first attitude data of the robotic arm and the spiral material taking device at the central system end;

[0013] The second data and the fourth data generate a second virtual model. There is a predetermined distance between the second virtual model and the first virtual model, and dynamic monitoring data is generated through the fifth data;

[0014] The control architecture includes a mesh network and a star network;

[0015] The mesh network is a first subsystem provided on the robotic arm and a second subsystem provided on the collaborative robot. The first subsystem includes the first virtual model, the first data and the second data. The second subsystem includes the second virtual model, the third data and the fourth data,

[0016] There are several first subsystems and second subsystems, and the data interact with each other to form a first blockchain;

[0017] The star network includes a central system and a third subsystem connected to the central system. The third subsystem is used to directly obtain the first subsystem data and form a second blockchain,

[0018] The central system is used to check the first blockchain and the second blockchain against each other, and can control the robotic arm by itself or through the collaborative robot.

[0019] In actual design, setting a first vision sensor through a robotic arm can generate an environmental model, and through a first inertial measurement unit, the relative position of the robotic arm can be accurately obtained and a first virtual model (including position (XYZ), dimensions, and environment) can be generated with the environmental model;

[0020] Through the second vision sensor and the second inertial measurement unit of a collaborative robot, where the collaborative robot can be a mobile robot or a fixed robot, and the fixed robot can be set at the base of a screw ship unloader or a predetermined loading and unloading position. Through the data interaction between the first inertial measurement unit and the second inertial measurement unit, and through the relative position between the collaborative robot and the robotic arm, a second virtual model is then generated, and then the position detection of the robotic arm is realized, and then a second virtual model is generated (where the second virtual model is generated reversely),

[0021] In the data verification through the first blockchain and the second blockchain (through a simple blockchain, data verification is realized, and the position difference is reduced), and then through the central system, the position and posture of the robotic arm can be accurately obtained, and then control errors or position errors can be avoided, effectively solving the problem of deviation in the existing vision system and improving the control accuracy.)

[0022] At the same time, through the first inertial measurement unit and the second inertial measurement unit, the accurate position of the robotic arm can be further obtained. At the same time, through the fifth data of the first inertial measurement unit and the second inertial measurement unit, the detection accuracy can be further improved, avoiding errors in single monitoring,

[0023] At the same time, it can also effectively reduce the vision error and control error of the screw feeding device, thereby improving production safety and production efficiency.

[0024] Realizing synchronous comparison and differential comparison of data is a good reference model for large machinery. Brief Description of the Drawings

[0025] Figure 1 It is the relative position relationship between the robotic arm and the collaborative robot;

[0026] Figure 2 It is the data comparison between the robotic arm, the collaborative robot and the central system;

[0027] Figure 3 It is the data flow diagram. Detailed Implementation Manner

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0029] It should be noted that if there are directional indications (such as up, down, left, right, front, back, top, bottom, inside, outside, vertical, horizontal, longitudinal, counterclockwise, clockwise, circumferential, radial, axial...) involved in the embodiments of the present invention, then such directional indications are only used to explain the relative positional relationship and movement conditions between components in a certain specific posture (as shown in the accompanying drawings). If this specific posture changes, then the directional indications will also change accordingly.

[0030] In addition, if there are descriptions involving "first" or "second" in the embodiments of the present invention, then such descriptions of "first" or "second" are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0031] As Figures 1 to 3 shown, a control system for a clinker screw unloader with a collaborative robot includes:

[0032] Robotic arms, there are multiple of the robotic arms, a screw feeding device is provided at the end of the robotic arms, first inertial measurement units are respectively provided at the head and the end of the robotic arms, the first vision sensor generates first data, and the first inertial measurement unit generates second data;

[0033] A collaborative robot, the collaborative robot is provided with a second vision sensor and a second inertial measurement unit, the second vision sensor generates third data, and the second inertial measurement unit generates fourth data;

[0034] The first inertial measurement unit and the second inertial measurement unit are interconnected and used to detect the relative dynamic position between the collaborative robot and the robotic arms. The third data and the second data are mutually interacted and calibrated to generate fifth data, and the fifth data is the position change and time change between the robotic arms and the collaborative robot;

[0035] The first data and the second data generate a first virtual model, which is used to generate first position data and first attitude data of the robotic arm and the spiral material taking device at the central system end;

[0036] The second data and the fourth data generate a second virtual model. There is a predetermined distance between the second virtual model and the first virtual model, and dynamic monitoring data is generated through the fifth data;

[0037] The control architecture includes a mesh network and a star network;

[0038] The mesh network is a first subsystem provided on the robotic arm and a second subsystem provided on the collaborative robot. The first subsystem includes the first virtual model, the first data and the second data, and the second subsystem includes the second virtual model, the third data and the fourth data.

[0039] There are several first subsystems and second subsystems, and their data interact with each other to form a first blockchain;

[0040] The star network includes a central system and a third subsystem connected to the central system. The third subsystem is used to directly obtain the data of the first subsystem and form a second blockchain.

[0041] The central system is used to check the first blockchain and the second blockchain against each other, and can control the robotic arm by itself or control the robotic arm through the collaborative robot.

[0042] In actual design, a first visual sensor is set on the robotic arm to generate an environmental model, and a first inertial measurement unit can accurately obtain the relative position of the robotic arm and generate a first virtual model with the environmental model (including position (XYZ), size, and environment);

[0043] Through the second visual sensor and the second inertial measurement unit of the collaborative robot, where the collaborative robot can be a mobile robot or a fixed robot, and the fixed robot can be set at the base of the screw unloader or a predetermined loading and unloading position. Through the data interaction between the first inertial measurement unit and the second inertial measurement unit, and through the relative position between the collaborative robot and the robotic arm, a second virtual model is generated, and then the position detection of the robotic arm is realized, and then a second virtual model is generated (where the second virtual model is generated reversely).

[0044] By checking the data of the first blockchain and the second blockchain (through a simple blockchain, data checking is realized, and the position difference is reduced), and then the position and attitude of the robotic arm can be accurately known through the central system, and then control errors or position errors can be avoided, effectively solving the problem of deviation of the existing vision system and improving the control accuracy.

[0045] Meanwhile, the precise position of the robotic arm can be further obtained through the first inertial measurement unit and the second inertial measurement unit. At the same time, the fifth data of the first inertial measurement unit and the second inertial measurement unit can further improve the detection accuracy, avoiding errors caused by single monitoring.

[0046] It can also effectively reduce visual errors and control errors, thereby improving production safety and production efficiency.

[0047] Specifically, the first inertial measurement unit and the second inertial measurement unit are millimeter-wave radars, ultrasonic sensors, and gyroscopes.

[0048] The first vision sensor and the second vision sensor are thermoformers and binocular cameras.

[0049] Through the above sensors, the relative position of the robotic arm can be obtained, and based on the relative position, point cloud data can be generated in the first virtual model and the second virtual model, thereby realizing real-time 3D demonstration of the model.

[0050] Specifically, the first vision sensor, the first inertial measurement unit, the second vision sensor, and the second inertial measurement unit adopt a dual power supply module, and the dual power supply module includes a built-in power supply and a robotic arm power supply or two built-in power supplies.

[0051] The mesh network and the star network are jointly networked using Ethernet, 5G network, and Wi-Fi network.

[0052] By reasonably selecting the sensor networking method and communication protocol, multi-sensor collaborative work with μs-level accuracy can be achieved. It is recommended to preferably use deterministic network protocols such as EtherCAT (where industrial-level networks can effectively improve time accuracy, reduce latency, and thereby improve control stability), and deploy edge computing nodes to reduce the load on the central controller.

[0053] EtherCAT (Ethernet Control Automation Technology) is an open architecture fieldbus system based on Ethernet. The CAT in EtherCAT is the abbreviation of the first letter of Control Automation Technology. It was initially developed by Beckhoff Automation GmbH in Germany. EtherCAT has set a new standard for the real-time performance of the system and the flexibility of the topology. At the same time, it also meets or even reduces the usage cost of the fieldbus. The features of EtherCAT also include high-precision device synchronization, optional cable redundancy, functional safety protocol (SIL3), and clock synchronization.

[0054] Adopt the IEEE 1588 PTP protocol; deploy the GPS / Beidou timing module (1PPS signal) data fusion: use the rclcpp library of ROS2 to implement node communication; deploy the Kalman filter. Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through the system input and output observation data. Since the observation data includes the influence of noise and interference in the system, the optimal estimate can also be regarded as a filtering process.

[0055] Hardware synchronization: PTP (Precision Time Protocol) realizes μs-level synchronization (for industrial-level time protocols, the time difference is related to the relative displacement).

[0056] Software synchronization: The tf library of ROS realizes the unification of space-time coordinates (i.e., the positions of the first virtual model and the second virtual model).

[0057] Event trigger: The IMU burst data triggers the camera to capture (when the error between the first blockchain and the second blockchain is greater than the predetermined value, multiple robotic arms stop, thus avoiding the generation of errors).

[0058] Specifically, the collaborative robot can be a mobile robot or a fixed robot, and the fixed robot can be set at the base of the screw unloader or the predetermined loading and unloading position.

[0059] The mobile robot is a rail robot or an unmanned aerial vehicle. Different collaborative robots are adopted according to different environments, and thus the data interaction can be effectively realized.

[0060] Specifically, the fifth data pre-judges the displacement position and speed of the next step of the robotic arm through the inertial measurement unit and the second inertial measurement unit, and then judges the next action of the robotic arm.

[0061] When the database of the robotic arm is stored to the predetermined value, its motion trajectory is optimized by the central system to streamline its displacement data. In an actual screw unloader, there are multiple robotic arms. Therefore, how to obtain a shorter adjustment stroke and adjustment time can be optimized through the stored motion trajectory, and the optimization can adopt manual optimization or the operation optimization of the central system.

[0062] Different from existing small devices, the robotic arms and screw feeding devices of the screw unloader are relatively large in volume, and some even span dozens of meters. Therefore, in addition to the speed, the most important consideration in the stroke planning is safety. Therefore, optimizing the stroke is the key to the design.

[0063] Specifically, the first virtual model adopts the MADDPG framework.

[0064] Spatial-temporal constraint equation of the robotic arm:

[0065] min Σ(α·Trajectory deviation + β·Energy consumption + γ·Timing conflict)

[0066] s.t. Joint torque ≤ τ_max

[0067] Motion speed ≤ v_safe

[0068] Spatial spacing ≥ d_min

[0069] Specifically, the dynamic path planning of the robotic arm adopts an improved RRT* algorithm

[0070] The dynamic path planning also includes a safety protection mechanism, which includes situations where the first blockchain and the second blockchain data do not correspond, data loss, network loss, and time error

[0071] Three-level emergency response system

[0072] Level 1: Speed limit mode, with the operating efficiency reduced to 60%

[0073] Level 2: Local area freezing, locking conflicting joints

[0074] Level 3: Full system emergency stop, triggering the mechanical brake

[0075] To ensure the safety of automatic control or automatic auxiliary control, a safety protection mechanism is designed to effectively reduce port accidents or problems

[0076] Among them, the RRT algorithm, whose full name is Rapidly-exploring Random Tree, was first proposed by Steven M. LaValle in 1998. It is a sampling-based algorithm that generates a tree-like structure through random sampling and gradually expands to the target area, finally connecting the starting point and the ending point to form a feasible path. Compared with traditional algorithms based on grid search or heuristic search, the RRT algorithm has significant advantages in high-dimensional spaces and complex environments. This is because in high-dimensional spaces, the computational complexity of grid search grows exponentially, while heuristic search is prone to falling into local optimal solutions. The RRT algorithm effectively avoids these problems through random sampling, enabling it to quickly explore the search space

[0077] Specifically, the optimized path training includes

[0078] S1: Initialize the tree, which may include the starting point

[0079] S2: In each iteration, generate a biased random point

[0080] S3: Find the nearest tree node and expand a new node in the direction of the random point;

[0081] S4: Check whether the new node is feasible, collision-free and satisfies the dynamic constraints;

[0082] S5: If feasible, optimize the path and possibly reconnect to shorten the path;

[0083] S6: Update the coordination matrix, and the collaborative robot generates new fifth data according to the optimized path (thus reducing the interference of the robotic arm operation and improving the operating position at the same time);

[0084] S7: Repeat until the maximum number of iterations is reached, and return the optimized path.

[0085] Specifically, the second data is point cloud forming;

[0086] The fourth data is reverse point cloud forming;

[0087] The fifth data generates a simulated robotic arm by reverse point cloud forming + point cloud forming,

[0088] Wherein the data of the robotic arm is determined data, so a second virtual model can be generated through the second data, the fourth data, the fifth data and the robotic arm data.

[0089] This solution can achieve precise loading and unloading of 1200 tons of cement per hour through virtual-real fusion control technology, with an efficiency improvement of more than 40% compared with the traditional solution, and is especially suitable for harsh industrial environments with high dust and strong vibration.

[0090] In order to achieve the precise position and precise control of this type of large machinery, the model system includes:

[0091] 1. A robotic arm, a first inertial measurement unit provided at both ends of the robotic arm, and a first vision sensor provided on the screw ship unloader,

[0092] Wherein the size data and parameter data of the robotic arm are stored in the first virtual model, and the environmental data of the screw ship unloader (such as size and obstacle position data, etc.) are also stored in the first virtual model;

[0093] And by comparing the environmental data of the first vision sensor with the first virtual model, a predetermined reference point or reference position is obtained. Among them, the position reference point can be obtained through the first inertial measurement unit and the first vision sensor, or an FRID reading device can be set at a predetermined position to obtain predetermined position data at a predetermined distance.

[0094] Through the various sensors of the first inertial measurement unit, and then when the robotic arm moves, the spatial coordinates (X1, Y1, Z1) and accurate time (T1) of the robotic arm can be obtained through the first inertial measurement unit;

[0095] And then generate the first sub-data of the first blockchain;

[0096] 2. Collaborative robot, where the second inertial measurement unit of the collaborative robot can directly monitor the robotic arm. The dimensional parameters of the collaborative robot include spatial coordinates (X2, Y2, Z2) and accurate time (T2), or the spatial coordinates (X3, Y3, Z3) and accurate time (T3) of the robotic arm can be obtained through the position spacing between the first inertial measurement unit and the second inertial measurement unit;

[0097] And then generate the second sub-data of the first blockchain;

[0098] Based on the first sub-data and the second sub-data of the first blockchain, the position of the robotic arm is reversely generated, that is, the first blockchain demonstrates a virtual robotic arm model;

[0099] 3. Central system, the central system is connected to the third subsystem. The third subsystem is used to directly obtain the first subsystem data (that is, the spatial coordinates (X2, Y2, Z2) and accurate time (T2) of the robotic arm) and form the second blockchain;

[0100] When the first blockchain and the second blockchain are compared, when the error values between the spatial coordinates (X1, Y1, Z1), (X2, Y2, Z2), and (X3, Y3, Z3), and the times (T1), (T2), and (T3) exceed a predetermined threshold, the robotic arm makes a safety protection mechanism.

[0101] There are multiple robotic arms. Therefore, in actual acquisition of the travel data of the spiral feeding device, the optimal route and robotic arm size need to be obtained according to the RRT algorithm.

[0102] The generation of the 3D images of the first virtual model and the second virtual model can use existing software data to achieve monitoring.

[0103] In actual design, the spiral feeding device can also be understood as a robotic arm, but it is a working unit and is thus greatly interfered by the outside world. Therefore, other structures such as servo motors and gyroscopes can be used for monitoring.

[0104] In actual monitoring, in addition to displacement monitoring, it also includes working data monitoring. For example, when the spiral feeding device is feeding materials, when the resistance is greater than a predetermined value or there is no material, its displacement data will also have a predetermined deviation, and thus the state of the spiral feeding device can be detected in a timely manner;

[0105] When the visual visibility of the working environment is greater than a predetermined value, the second vision sensor of the collaborative robot can also directly monitor the state of the mechanical wall and the spiral material taking device, thereby realizing multiple monitoring and comparison of multiple data.

[0106] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structural transformation made under the inventive concept of the present invention by using the content of the specification and drawings of the present invention, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. A clinker screw unloader control system with a collaborative robot, characterized in that: include: A mechanical arm, wherein the mechanical arm is provided with a plurality of The mechanical arm at the end is provided with a spiral material taking device, and the first end and the end of the mechanical arm are respectively provided with a first inertial measurement unit. The screw ship unloader body is provided with a first visual sensor, the first visual sensor generates first data, and the first inertial measurement unit generates second data; A collaborative robot, wherein the collaborative robot is provided with a second visual sensor and a second inertial measurement unit, wherein the second visual sensor generates third data and the second inertial measurement unit generates fourth data; The first inertial measurement unit and the second inertial measurement unit are linked to each other and used to detect the relative dynamic position between the collaborative robot and the robotic arm. The third data and the second data interact with each other and are collated to generate fifth data. The fifth data is the position change and time change between the robotic arm and the collaborative robot. The first data and the second data generate a first virtual model, and the first virtual model is used to generate first position data and first posture data of the robot arm and the spiral material taking device at the central system end; The second data and the fourth data generate a second virtual model, a predetermined distance is set between the second virtual model and the first virtual model, and dynamic monitoring data is generated through the fifth data; Control architectures include mesh and star networks; The mesh network is a first subsystem provided in the robot arm and a second subsystem provided in the collaborative robot, wherein the first subsystem includes a first virtual model, first data and second data, and the second subsystem includes a second virtual model, third data and fourth data. The first subsystem and the second subsystem are provided with a plurality of data and interact with each other to form a first blockchain; The star network includes a central system and a third subsystem connected to the central system, wherein the third subsystem is used to directly obtain data from the first subsystem and form a second blockchain. The central system is used to cross-check the first blockchain and the second blockchain, and can control the robotic arm itself or through a collaborative robot.

2. The clinker screw unloader control system with a collaborative robot according to claim 1, characterized in that: The first inertial measurement unit and the second inertial measurement unit are millimeter wave radar, ultrasonic sensor and gyroscope; The first visual sensor and the second visual sensor are a thermoforming device and a binocular camera.

3. The clinker screw unloader control system with a collaborative robot according to claim 1, characterized in that: The first visual sensor, the first inertial measurement unit, the second visual sensor and the second inertial measurement unit use a dual power supply module, and the dual power supply module includes a built-in power supply and a robotic arm power supply or a dual built-in power supply; The mesh network and star network are jointly networked using Ethernet, 5G network, and Wi-Fi network.

4. The clinker screw unloader control system with a collaborative robot according to claim 1, characterized in that: The collaborative robot may be a mobile robot or a fixed robot, wherein the fixed robot may be arranged at the base of the screw ship unloader or at a predetermined loading and unloading position; The mobile robot is a slide robot or an unmanned aerial vehicle.

5. The clinker screw unloader control system with a collaborative robot according to claim 1, characterized in that: The fifth data is used to predict the displacement position and speed of the next step of the robotic arm through the inertial measurement unit and the second inertial measurement unit, thereby determining the next action of the robotic arm; When the database of the robot arm stores a predetermined value, its motion trajectory is optimized by the central system to simplify its displacement data.

6. The clinker screw unloader control system with a collaborative robot according to claim 5, characterized in that: The first virtual model adopts the MADDPG framework; The space-time constraint equations of the robot arm: minΣ(α·trajectory deviation+β·energy consumption+γ·timing conflict); st joint torque ≤ τ_max; Movement speed ≤ v_safe; Spatial spacing ≥ d_min.

7. The clinker screw unloader control system with a collaborative robot according to claim 6, characterized in that: The dynamic path planning of the robot arm adopts an improved RRT* algorithm; The dynamic path planning also includes a security protection mechanism, which includes data mismatch between the first blockchain and the second blockchain, data loss, network loss, and time error; Three-level emergency response system: Level 1: Speed ​​limit mode, operating efficiency reduced to 60%; Level 2: Freeze the local area and lock the conflicting joints; Level 3: The entire system comes to an emergency stop, triggering the mechanical brake.

8. The clinker screw ship unloader control system with a collaborative robot according to claim 6, characterized in that: The optimization path training includes: S1: Initialize the tree, which may include the starting point; S2: In each iteration, generate biased random points; S3: Find the nearest tree node and expand the new node in the direction of the random point; S4: Check whether the new node is feasible, has no collision and satisfies dynamic constraints; S5: Optimize the path if feasible, possibly reconnecting to shorten the path; S6: updating the coordination matrix, and the collaborative robot generates new fifth data according to the optimized path; S7: Repeat until the maximum number of iterations is reached and return the optimized path.

9. The clinker screw ship unloader control system with a collaborative robot according to claim 1, characterized in that: The second data is point cloud forming; The fourth data is reverse point cloud forming; The fifth data is generated by reverse point cloud forming + point cloud forming to simulate the robotic arm, The data of the robot arm is determined data, so the second virtual model can be generated through the second data, the fourth data, the fifth data and the robot arm data.

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