A control system for a clinker screw ship unloader with a co-robot

Abstract

The application discloses a clinker screw ship unloader control system with a collaborative robot, which comprises a plurality of mechanical arms and a collaborative robot, the end of the mechanical arm is provided with a spiral material taking device, the first end and the end of the mechanical arm are respectively 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 interconnected and used for detecting the relative dynamic position between the collaborative robot and the mechanical arm, the third data and the second data are mutually interactive and corrected to generate fifth data, and the fifth data is the position change and the time change between the mechanical arm and the collaborative robot.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automatic control system of clinker screw unloader, in particular to a clinker screw unloader control system with collaborative robot. BACKGROUND

[0002] The screw unloader is a special mechanical device that uses continuous conveying machinery to produce a machine head that can lift bulk materials, or has self-retrieval capability, or is equipped with a retrieval and feeding device, which can continuously unload bulk materials from the ship cabin to the arm or frame and can be transported to the main conveying system on the shore. Using the screw unloader can greatly improve the unloading efficiency, and the minimum dust pollution can keep the environment clean and environmentally friendly.

[0003] The existing screw unloader is difficult to achieve fully automatic control due to its large size, but with the progress of the times, how to achieve precise control and precise monitoring of the screw unloader is also a key design.

[0004] However, the existing clinker (such as sand, dust, cement, etc.) is prone to dust when loading and unloading, which blocks the view. The existing method is generally to spray liquid to achieve dust reduction, so as to control the position of the screw retrieval device. However, there is still a problem of visual obstruction. Of course, there is also a structure that uses a vision device to achieve position management. Although this device can achieve remote control, it still has drawbacks in remote control and remote position monitoring, which affects the safety of operation.

[0005] For example, Chinese patent 202111416038.3 uses a space positioning technology to achieve anti-collision protection of the screw retrieval device screw unloader, with no detection blind spot, high safety and reliability. At the same time, this technology can be integrated with conventional anti-collision protection technologies based on limit switches, laser ranging, millimeter wave radar, etc., forming redundant protection in control, further enhancing the safety and reliability of the screw retrieval device screw unloader operation.

[0006] However, in the actual system, there is still a time and space delay, so the control of uncertain factors is also a key to monitoring. SUMMARY

[0007] The main purpose of the present application is to provide a clinker screw unloader control system with collaborative robot, which aims to use collaborative robots and various control systems of screw unloaders to achieve networking, which can realize time monitoring and space monitoring, and ensure control accuracy in uncertain factors.

[0008] To achieve the above purpose, the present application provides a clinker screw unloader control system with collaborative robot, comprising:

[0009] A mechanical arm is provided with a plurality of, the terminal mechanical arm is provided with a spiral material taking device, the first end and the terminal of the mechanical arm are respectively provided with a first inertial measurement unit, the spiral ship unloader body is provided with a first vision sensor, the first vision sensor generates first data, the first inertial measurement unit generates second data;

[0010] A 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;

[0011] The first inertial measurement unit and the second inertial measurement unit are interconnected and used for detecting the relative dynamic position between the collaborative robot and the mechanical arm, the third data and the second data are interacted and corrected to generate fifth data, and the fifth data is the position change and the time change between the mechanical arm and the collaborative robot;

[0012] 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 attitude data of the mechanical 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, and the second virtual model and the first virtual model are provided with a predetermined interval, 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 in the mechanical arm and a second subsystem provided in 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,

[0016] The first subsystem and the second subsystem are provided with a plurality of and the data are interacted to form a first block chain;

[0017] The star network includes a central system and a third subsystem connected with the central system, and the third subsystem is used for directly acquiring the first subsystem data and forming a second block chain,

[0018] The central system is used for mutually checking the first block chain and the second block chain, and can control the mechanical arm by itself or through the collaborative robot.

[0019] In actual design, the first visual sensor arranged on the body of the screw ship unloader can generate an environment model, and the first inertia measurement unit can accurately obtain the relative position of the mechanical arm and generate a first virtual model (including position (XYZ), size and environment) with the environment model.

[0020] The second visual sensor and the second inertia measurement unit of the collaborative robot, wherein the collaborative robot can be a mobile robot or a fixed robot, and the fixed robot can be arranged on the base of the screw ship unloader or a predetermined loading and unloading position. Through the data interaction of the first inertia measurement unit and the second inertia measurement unit, the relative position of the collaborative robot and the mechanical arm is obtained, and a second virtual model is generated, thereby realizing the position detection of the mechanical arm, and generating a second virtual model (the second virtual model is generated reversely),

[0021] Through the data checking of the first and second blockchains (through simple blockchain, the data checking is realized, and the position difference is reduced), the position and attitude of the mechanical arm are accurately obtained through the central system, control errors or position errors are avoided, the deviation problem of the existing visual system is effectively solved, and the control precision is improved.

[0022] Meanwhile, the first and second inertia measurement units can further obtain the accurate position of the mechanical arm, and the fifth data of the first and second inertia measurement units can further improve the detection precision, avoiding errors in single monitoring,

[0023] Meanwhile, the visual error and control error of the screw material taking device can also be effectively reduced, thereby improving the production safety and production efficiency.

[0024] The realization of data synchronization comparison and differential comparison is a good reference model for large machinery. BRIEF DESCRIPTION OF DRAWINGS

[0025] Fig. 1 The relative position relationship between the mechanical arm and the collaborative robot;

[0026] Fig. 2 The data comparison between the mechanical arm, the collaborative robot and the central system;

[0027] Fig. 3 The data flowchart. DETAILED DESCRIPTION

[0028] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0029] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, top, bottom, inner, outer, vertical, horizontal, longitudinal, counterclockwise, clockwise, circumferential, radial, axial, etc.), the directional indications are only used to explain the relative positional relationship, motion condition, etc. between components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications also change accordingly.

[0030] In addition, if the embodiments of the present application involve descriptions such as "first" or "second", the descriptions of "first" or "second" are only for description purposes, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist and is not within the protection scope of the present application.

[0031] As shown in Figs. 1 to 3 A clinker screw ship unloader control system with a collaborative robot includes:

[0032] A plurality of mechanical arms are provided, and the end mechanical arm is provided with a screw material taking device. The first end and the end of the mechanical arm are respectively provided with a first inertial measurement unit,

[0033] The screw ship unloader body is provided with a first vision sensor, and the first vision sensor generates first data. The first inertial measurement unit generates second data.

[0034] A 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.

[0035] 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 mechanical arm. The third data and the second data are mutually interactive and are used to generate fifth data. The fifth data is the position change and the time change between the mechanical arm and the collaborative robot.

[0036] 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 mechanical arm and the spiral material taking device at the central system end;

[0037] The second data and the fourth data generate a second virtual model, which is provided with a predetermined interval from the first virtual model and generates dynamic monitoring data through the fifth data;

[0038] The control architecture includes a mesh network and a star network;

[0039] The mesh network is a first subsystem provided on the mechanical 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,

[0040] The first subsystem and the second subsystem are provided with a plurality of and data interact to form a first block chain;

[0041] The star network includes a central system and a third subsystem connected with the central system, the third subsystem is used to directly acquire the first subsystem data and form a second block chain,

[0042] The central system is used to check each other for the first block chain and the second block chain, and can control the mechanical arm by itself or through the collaborative robot.

[0043] In actual design, the environment model can be generated by setting the first visual sensor on the mechanical arm, and the relative position of the mechanical arm can be accurately obtained through the first inertial measurement unit and the environment model to generate the first virtual model (which includes position (XYZ), size and environment);

[0044] The second visual sensor and the second inertial measurement unit of the collaborative robot, wherein the collaborative robot can be a mobile robot or a fixed robot, wherein the fixed robot can be arranged on the base of the spiral ship unloader or a predetermined loading and unloading position, the data of the first inertial measurement unit and the second inertial measurement unit are interacted, the relative position of the collaborative robot and the mechanical arm is obtained, and then the second virtual model is generated, and the position detection of the mechanical arm is realized, and then the second virtual model is generated (the second virtual model is reversely generated),

[0045] Through the data checking of the first block chain and the second block chain (through the simple block chain, the data checking is realized, and the position difference is reduced), the position and the attitude of the mechanical arm are accurately obtained through the central system, the control error or the position error can be avoided, the problem that the existing visual system deviates is effectively solved, and the control precision is improved.

[0046] At the same time, the accurate position of the mechanical arm can be further obtained through the first and second inertial measurement units, and the detection accuracy can be further improved through the fifth data of the first and second inertial measurement units, so as to avoid errors caused by single monitoring,

[0047] At the same time, visual errors and control errors can also be effectively reduced, thereby improving the safety and production efficiency of production.

[0048] Specifically, the first and second inertial measurement units are millimeter wave radars, ultrasonic sensors and gyroscopes.

[0049] The first and second visual sensors are thermoforming instruments and binocular cameras.

[0050] Through the above-mentioned sensors, the relative position of the mechanical arm can be obtained, and through the relative position, point cloud data can be generated in the first and second virtual models, and real-time 3D demonstration of the model can be realized.

[0051] Specifically, the first visual sensor, the first inertial measurement unit, the second visual 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 mechanical arm power supply or a dual built-in power supply.

[0052] The mesh network and the star network adopt Ethernet, 5G network and Wi-Fi network joint networking.

[0053] By reasonably selecting the sensor networking mode and the communication protocol, the collaborative work of multiple sensors can be realized at the precision of μs. It is recommended to preferentially adopt deterministic network protocols such as EtherCAT (among them, the network of industrial level can effectively improve the time precision, reduce the delay, and further improve the control stability), and deploy edge computing nodes to reduce the load of the central controller.

[0054] EtherCAT (Ethernet for Control Automation Technology) is an open architecture fieldbus system based on Ethernet. The CAT in the name of EtherCAT is the abbreviation of the first letter of Control Automation Technology. It was initially developed by Beckhoff Automation GmbH in Germany. EtherCAT sets a new standard for the real-time performance of the system and the flexibility of the topology, and at the same time, it also meets or reduces the use cost of the fieldbus. The characteristics of EtherCAT also include high-precision device synchronization, optional cable redundancy, functional safety protocol (SIL3), clock synchronization:

[0055] Adopt IEEE 1588 PTP protocol; deploy GPS / Beidou time service module (1PPS signal) data fusion: use the rclcpp library of ROS2 to realize node communication; deploy Kalman filter. Kalman filtering is an algorithm that uses linear system state equations to optimally estimate the system state from system input and output observation data. Since the observation data includes the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process.

[0056] Hardware synchronization: PTP (Precision Time Protocol) realizes μs-level synchronization (for industrial time protocol, the time difference affects the relative displacement);

[0057] Software synchronization: ROS's tf library realizes the unification of space-time coordinates (i.e. the positions of the first virtual model and the second virtual model);

[0058] Event triggering: IMU burst data triggers camera snapshot (when the error between the first blockchain and the second blockchain is greater than a predetermined value, multiple robot arms stop, thereby avoiding errors).

[0059] Specifically, the collaborative robot can be a mobile robot or a fixed robot, wherein the fixed robot can be arranged at the base of the spiral ship unloader or a predetermined loading and unloading position;

[0060] The mobile robot is a slide rail robot or an unmanned aerial vehicle, and different collaborative robots are used according to different environments, thereby effectively realizing data interaction.

[0061] Specifically, the fifth data is used to predict the displacement position and speed of the next step of the robot arm through the first and second inertial measurement units, and to determine the next action of the robot arm;

[0062] When the database of the robot arm is stored to a predetermined value, the motion trajectory is optimized by the central system to simplify the displacement data. In an actual spiral ship unloader, multiple robot arms are provided, and therefore how to obtain a shorter adjustment stroke and adjustment time can be optimized by the stored motion trajectory, wherein the optimization can adopt manual optimization or operation optimization of the central system;

[0063] Unlike existing small devices, the robot arm of the spiral ship unloader and the spiral material taking device have a large volume, and some even span tens of meters, so in addition to the speed, the safety is the most important factor in planning the stroke, and therefore the optimization of the stroke is the key to the design.

[0064] Specifically, the first virtual model adopts the MADDPG framework;

[0065] Mechanical arm space-time constraint equation:

[0066] min∑(α·trajectory deviation + β·energy consumption + γ·timing conflict)

[0067] s.t.joint torque ≤ τ_max

[0068] motion velocity ≤ v_safe

[0069] spatial distance ≥ d_min.

[0070] Specifically, the dynamic path planning of the mechanical arm adopts an improved RRT algorithm.

[0071] The dynamic path planning further includes a safety protection mechanism, which includes first blockchain and second blockchain data not corresponding, data loss, network loss, time error.

[0072] Three-level emergency response system:

[0073] Level 1: speed limit mode, running efficiency reduced to 60%;

[0074] Level 2: local area freeze, lock conflict joint;

[0075] Level 3: whole system emergency stop, trigger mechanical brake.

[0076] In order to ensure the safety of automatic control or automatic auxiliary control, the safety protection mechanism is designed to effectively reduce port accidents or problems.

[0077] The RRT algorithm, full name Rapidly-exploring Random Tree, was first proposed by Steven M. LaValle in 1998. It is a sampling-based algorithm that generates a tree structure by random sampling and gradually expands to the target area, eventually connecting the starting point and the end point to form a feasible path. Compared with traditional grid search or heuristic search algorithms, RRT algorithm has significant advantages in high-dimensional space and complex environment. This is because in high-dimensional space, the computational complexity of grid search grows exponentially, while heuristic search is prone to local optimal solution. RRT algorithm effectively avoids these problems by random sampling, making it able to quickly explore the search space.

[0078] Specifically, the optimized path training includes:

[0079] S1: initialize the tree, which may contain the starting point;

[0080] S2: in each iteration, generate a biased random point;

[0081] S3: find the nearest tree node, expand the new node in the direction of the random point;

[0082] S4: check if the new node is feasible, no collision and meets dynamic constraints;

[0083] S5: if feasible, optimize the path, possibly reconnect to shorten the path;

[0084] S6: update the coordination matrix, and the collaborative robot generates new fifth data according to the optimized path (further reducing the interference of the robot operation, and improving the position of the operation);

[0085] S7: repeat until the maximum number of iterations is reached, return the optimized path.

[0086] Specifically, the second data is point cloud shaping;

[0087] The fourth data is inverse point cloud shaping;

[0088] The fifth data is generated by inverse point cloud shaping + point cloud shaping to simulate the robot,

[0089] Wherein the data of the robot is determined data, therefore the second virtual model can be generated by the second data, the fourth data, the fifth data and the robot data.

[0090] The scheme can realize precise loading and unloading of 1200 tons of cement per hour through virtual-real fusion control technology, which is more than 40% more efficient than traditional schemes, and is especially suitable for harsh industrial environments with high dust and strong vibration.

[0091] In order to realize the precise position and precise control of such large machinery, the model system comprises:

[0092] 1. A robot arm and a first inertial measurement unit arranged at both ends of the robot arm and a first vision sensor arranged at the body of the screw unloader,

[0093] Wherein the size data and parameter data of the robot arm are stored in the first virtual model, and the environmental data (such as size and obstacle position data) of the additional screw unloader are also stored in the first virtual model;

[0094] And by comparing the environmental data of the first vision sensor with the first virtual model, the predetermined reference point or reference position is obtained, wherein the position reference point can be obtained by the first inertial measurement unit, the first vision sensor, or an FRID reading device can be set at the predetermined position to obtain the predetermined position data at the predetermined distance,

[0095] Through each sensor of the first inertial measurement unit, and when the mechanical arm moves, the first inertial measurement unit can obtain the spatial coordinates (X1, Y1, Z1) of the mechanical arm and the accurate time (T1);

[0096] Further generate the first blockchain first sub-data;

[0097] 2. The collaborative robot, wherein the second inertial measurement unit of the collaborative robot can directly monitor the mechanical arm, wherein the size parameters of the collaborative robot include spatial coordinates (X2, Y2, Z2) and accurate time (T2), and the spatial coordinates (X3, Y3, Z3) of the mechanical arm and the accurate time (T3) can also be obtained through the position distance between the first inertial measurement unit and the second inertial measurement unit;

[0098] Further generate the second sub-data of the first blockchain;

[0099] Through the first sub-data and the second sub-data of the first blockchain, the position of the mechanical arm is reversely generated, that is, the first blockchain demonstrates a virtual mechanical arm model;

[0100] 3. The central system, said central system and the third subsystem, said third subsystem is used for directly obtaining the first subsystem data (that is, the spatial coordinates (X2, Y2, Z2) of the mechanical arm and the accurate time (T2)) and forming the second blockchain;

[0101] Wherein, 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 the predetermined threshold value, the mechanical arm makes a safety protection mechanism.

[0102] Wherein, the mechanical arm is provided with multiple, so in the actual acquisition of the travel data of the spiral taking device, the optimal route mechanical arm size needs to be acquired according to the RRT algorithm.

[0103] Wherein, the 3D image generation of the first virtual model and the second virtual model can adopt existing software data, and monitoring can be realized.

[0104] In actual design, the spiral taking device can also be understood as a mechanical arm, but it is a working unit, so it is greatly interfered by the outside world, so other structures can be used for monitoring, such as servo motor, gyroscope and other sensors.

[0105] In actual monitoring, in addition to displacement monitoring, work data monitoring is also included, for example, when the spiral taking device is in the loading process, when the resistance is greater than the predetermined value or the empty material, the displacement data will also produce a predetermined deviation, so that the state of the spiral taking device can be found in time;

[0106] When the visual visibility of the working environment is greater than a predetermined value, the second visual sensor of the collaborative robot can also directly monitor the state of the mechanical arm and the spiral taking device, thereby realizing multiple monitoring and comparison of multiple data.

[0107] The above merely describes the preferred embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or the like within the inventive concept of the present application, and the contents of the present application specification and drawings are included in the patent protection scope of the present application.

Claims

1. A clinker screw unloader control system with a collaborative robot, characterized in that: include: A robotic arm, wherein the robotic arm is provided with multiple, The end of the mechanical arm is provided with a spiral material taking device, and the head 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 are used to detect the relative dynamic position between the collaborative robot and the robotic arm. The third data and the second data are interacted with each other and 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 reclaiming device on 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 by the fifth data; Control architectures include mesh and star networks; The mesh network is a first subsystem provided in the robotic 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 dual built-in power supplies; 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 can be a mobile robot or a fixed robot, wherein the fixed robot can be set at the base of the screw ship unloader or 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 by the first inertial measurement unit and the second inertial measurement unit to predict the displacement position and speed of the next step of the robotic arm, 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 dynamic path planning of the robotic arm adopts an improved RRT algorithm; The dynamic path planning includes a security protection mechanism, which includes the mismatch between the first blockchain and the second blockchain data, 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 local areas and lock conflicting joints; Level 3: The entire system comes to an emergency stop, triggering the mechanical brake.

7. The clinker screw unloader control system with a collaborative robot according to claim 6, characterized in that: The optimized path training of the robotic arm includes: S1: Initialize the tree, which may include the starting point; S2: Generate biased random points in each iteration; 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 collisions and satisfies dynamic constraints; S5: If feasible, optimize the path, possibly reconnecting to shorten the path; S6: Update 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.

8. The clinker screw 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 robotic arm is confirmed data, so the second virtual model can be generated by the second data, the fourth data, the fifth data and the robotic arm data.

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