A control system of an intelligent drilling anchor robot for a mechanical arm to cooperate with a drilling machine

Abstract

The application discloses a control system of an intelligent drilling-anchor robot for cooperation of a mechanical arm and a drilling machine, a task timing and parallel arrangement subsystem comprises a task timing and parallel characteristic analysis module and a device task sequencing module; a scanning detection strategy task allocation system comprises a device scanning detection module, a task process detection module and a task allocation module; a path planning obstacle avoidance system comprises a motion device working space construction module, a shortest path analysis module, a rod expansion module and a trajectory obstacle avoidance analysis module; a visual positioning system comprises a body position calibration module, an interactive point visual identification module and an interactive calibration module; and a device motion control system comprises a body advancing control module, a path point picking module, a joint variable calculation module, a fuzzy control module and an interactive device adjustment module. The application greatly promotes the intelligentization and unmanned development of downhole operations, reduces the work intensity of artificial labor and improves the work efficiency of the drilling-anchor robot.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of drilling and anchoring robot multi-task parallel, and particularly relates to a control system of an intelligent drilling and anchoring robot for collaborative operation of a mechanical arm and a drilling machine. BACKGROUND

[0002] Coal resources occupy the main position of energy in China, and the demand increases year by year. However, the current coal underground operation has the problem of "fast digging and slow supporting", and the heavy drilling and anchoring operation leads to the stagnation of the overall mining work. At present, the drilling and anchoring operation is usually completed by manual operation, and the manual operation often has the problems of unreasonable task allocation, safety hazards in the operation process, and reduced efficiency caused by personnel scheduling, which greatly limits the efficiency of underground operation.

[0003] In recent years, with the large-scale mechanization and intelligentization of underground operation machinery, drilling and anchoring operations have also been widely applied. Drilling and anchoring robots assist workers to complete drilling and anchoring operations, which improves the efficiency of drilling and anchoring support and improves the working environment of workers. The current drilling and anchoring robot can perform drilling and anchoring operations according to the drilling requirements and in combination with various drilling machines, but still needs manual operation, and there is still room for improvement in the accuracy, efficiency and safety of the operation. SUMMARY

[0004] In order to overcome the shortcomings of low work efficiency of drilling and anchoring robots, complex work handover process, and large demand for manpower caused by manual operation in coal mine roadway support operation, the present application provides a control system of an intelligent drilling and anchoring robot for collaborative operation of a mechanical arm and a drilling machine.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] A control system of an intelligent drilling and anchoring robot for collaborative operation of a mechanical arm and a drilling machine, characterized in that it comprises:

[0007] A task timing and parallel arrangement subsystem, comprising a task timing and parallel characteristic analysis module and a device task sequencing module;

[0008] A scanning detection strategy task allocation system, comprising a device scanning detection module, a task process detection module and a task allocation module;

[0009] A path planning obstacle avoidance system, comprising a motion device workspace construction module, a shortest path analysis module, a rod extension module and a trajectory obstacle avoidance analysis module;

[0010] A visual positioning system, comprising a body position calibration module, an interactive point visual recognition module and an interactive calibration module;

[0011] The device motion control system comprises a body travel control module, a path point picking module, a joint variable calculation module, a fuzzy control module and an interactive device adjustment module.

[0012] Further, the parallel characteristic analysis module collects each device task of the intelligent drilling and anchoring robot, verifies the parallelism between different device tasks on the premise of time sequence, and then delivers the analyzed task characteristics to the device task sequencing module for processing to output a task time sequence arrangement table.

[0013] Further, the motion device workspace construction module is used for simulating the robot workspace and identifying obstacles; the rod extension module is used for adjusting the volume of the tracing point in the vertical direction according to the type of the rod for the rod-related task to avoid interference between the rod and the obstacle; and the shortest path analysis module calculates a plurality of task paths by a task content loading algorithm, and the trajectory obstacle avoidance analysis module verifies the solved paths to output the best task path.

[0014] Further, the workflow of the visual positioning system is as follows:

[0015] S1, before the drilling and anchoring robot starts to work, the body position calibration module is used to check and verify the drilling and anchoring robot travel direction variable and travel distance variable based on the last working position as the reference, determine the current drilling and anchoring robot actual position according to the measured variable, compare the measured data with the reference data, obtain the position error, deliver the position error to the motion control system, and adjust the robot position.

[0016] S2, before the task starts, the drilling and anchoring robot device position self-checking process is performed, the current coordinate information of each device is positioned in the space through the recognition of each device feature point, and is delivered to the path planning obstacle avoidance system to plan the device motion path.

[0017] S3, during the task, the interactive point visual recognition module works, the binocular camera is used to realize real-time image acquisition, the adaptive image enhancement algorithm is used to collect pictures, the depth learning training is performed with the help of the graphic sample, the target detection model is finally obtained to realize the underground target recognition, and the obtained target space coordinate information is input into the interactive calibration module to realize accurate positioning.

[0018] Further, the path point picking module, the joint variable calculation module, the fuzzy control module and the interactive device adjustment module are used to realize path point segmentation, multi-point continuous operation and parameter-driven interactive calibration during the robot travel process, so as to drive the task content in the drilling and anchoring robot work to control the drilling machine horizontal and vertical displacement, the mechanical arm end travel and the anchor rod warehouse rotation to supply the rod.

[0019] Further, the body travel control module mainly controls the drill anchor robot walking track drive wheel, controls the drill anchor robot travel and steering through the drive wheel same direction same speed, different direction differential operation, and combines the visual positioning system to ensure the accuracy of the drill anchor robot operation position.

[0020] Compared with the prior art, the present application has the following beneficial effects:

[0021] 1) The adopted scanning detection strategy task allocation method has high applicability. As long as a series of task contents and task characteristics are clear, a suitable task flow can be selected through allocation simulation.

[0022] 2) The adopted algorithm is simple, the algorithm content logic is simple, and the implementation is easy, and the algorithm content can be modified for different situations.

[0023] 3) The adopted scanning detection strategy can maximize the guarantee of parallel operation of multiple devices, and the interference between devices is minimized. The simulation results are clear and intuitive, and can fully represent the working conditions and time distribution of different devices in the entire operation process.

[0024] 4) The basic path planning algorithm is adopted, which has strong convenience and practicality. According to the task content, on the one hand, the low efficiency caused by simultaneous processing of multiple path interference problems is improved, and on the other hand, the operation process is modularized, which is convenient for optimization of part of the module.

[0025] 5) The visual positioning method is adopted, which ensures the accurate interaction process between the moving devices, makes the devices connect smoothly, and also ensures the accuracy of the operation.

[0026] In summary, the present application is simple to implement and has certain intelligence and automation, which can help the drill anchor robot platform to realize autonomous drilling, anchoring, charging, fastening and other functions, provide safety guarantee for drilling operation, improve the working efficiency of the drill anchor robot, and the whole operation process involves less human intervention, greatly promotes the intelligent and unmanned development of downhole operation, has strong practicality and wide application range. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 It is a structural schematic diagram of the intelligent drill anchor robot for the cooperative operation of the mechanical arm and the drill rig in the embodiment of the present application.

[0028] Figure 2 It is a module block diagram of the control system of the intelligent drill anchor robot for the cooperative operation of the mechanical arm and the drill rig in the embodiment of the present application.

[0029] Figure 3 It is a flow chart of the visual positioning system in the embodiment of the present application.

[0030] Figure 4 This is a flowchart of the PID control system in an embodiment of the present invention.

[0031] Figure 5 This is a flowchart of the control system in an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives and advantages of the present invention clearer, the present invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0033] To address the limitations of existing drilling and anchoring robot operations, this invention provides a control system for an intelligent drilling and anchoring robot that coordinates the operation of a robotic arm and a drilling rig. This control system enables the robot to autonomously perform drilling and anchoring support work, thereby achieving high efficiency and unmanned operation. This control system is designed for intelligent drilling and anchoring robots such as… Figure 1 As shown, the system includes a robot platform, a walking track 1 mounted below the robot platform, a drill rod and anchor bolt magazine 2 mounted above the robot platform, a six-degree-of-freedom robotic arm 3, a drilling rig 4, a vertical lifting guide rail 5, and a horizontal moving guide rail 6; the control system is as follows: Figure 2 As shown, it includes:

[0034] The task timing and parallel arrangement subsystem includes a task timing and parallel characteristic analysis module and a device task sorting module;

[0035] The scanning and detection strategy task allocation system includes a device scanning and detection module, a task progress detection module, and a task allocation module.

[0036] The path planning and obstacle avoidance system includes a workspace construction module for motion equipment, a shortest path analysis module, a pole extension module, and a trajectory obstacle avoidance analysis module.

[0037] The visual positioning system includes a body position calibration module, an interaction point visual recognition module, and an interaction calibration module;

[0038] The equipment motion control system includes a body movement control module, a path point picking module, a joint variable calculation module, a fuzzy control module, and an interactive equipment adjustment module.

[0039] The control method of the control system for an intelligent drilling and anchoring robot that coordinates the operation of a robotic arm and a drilling rig, as described in this invention, includes the following steps:

[0040] S1, in the task timing, parallelism analysis module, according to the process flow, the corresponding timing characteristics are given to various tasks, and the parallel characteristics are given to each device task according to the interaction content between devices, the task content with defined characteristics is handed over to the device task scheduling module for overall process flow arrangement of the drilling and anchoring robot, and the analysis result is handed over to the scanning detection strategy task allocation system for processing.

[0041] S2, simulate the complete operation process of the drilling and anchoring robot in the task allocation system. The device scanning module includes a device state database, which is composed of an (m+1)×n matrix, and the matrix is as follows:

[0042]

[0043] m represents the number of devices included in the drilling and anchoring robot, n represents the operation time of the drilling and anchoring robot, the assignment of each device in the simulation system on the corresponding working time column represents the current device working state, for example: "0" represents that the device has not started, "1" represents that the device is working, "2" represents that the device is on standby, and "3" represents that all tasks of the device are completed.

[0044] Similarly, the task progress database in the task progress detection module is composed of a (k+1)×n matrix, and the matrix is as follows:

[0045]

[0046] k represents different processing holes of the drilling and anchoring robot, and n represents the operation time of the drilling and anchoring robot. In the simulation system, the processing progress of each processing hole will be marked with the current task number on the corresponding time column of each processing hole, so as to reflect the entire drilling and anchoring process.

[0047] After the simulation system works, a complete operation time list will be given, and the tasks will be allocated to the motion task list and the interaction task list, providing a reference for manual adjustment of the process.

[0048] S3, in the path planning obstacle avoidance system, the motion device in different tasks, the starting point and the ending point of the motion are determined according to the given motion device task list, and the path planning algorithm is used for device motion trajectory planning, taking A-star algorithm as an example. The A-star algorithm is a heuristic method for searching path, which is more directional than the basic "netting type" path planning method. In the constructed map, the distance cost evaluation of each node around the starting point is carried out by using the heuristic function. The heuristic function is as follows:

[0049] f(n)=g(n)+h(n)

[0050] Wherein, g(n) indicates the actual cost from the initial point to the node, and the cost value is different according to different routes, assuming that the straight line travel cost is 10, and the diagonal line travel cost is 14, then the cost from the current node to the next different node will change due to different paths. h(n) indicates the estimated cost from the current node to the terminal. For path planning in the spatial coordinate system, the nodes in 26 directions around the current node need to be traversed at the same time, and the values of f(n) of each node are calculated respectively in order to select the best motion path. The determined node will be stored in the corresponding matrix until the terminal is included in the traversed nodes, at which time the path planning is completed, and the corresponding path can be obtained from the corresponding matrix.

[0051] S4, for the interactive device, visual positioning is realized by means of deep learning. In network model training, the resolution of all input pictures is uniformly set to 640*640, asynchronous stochastic gradient descent method with momentum term of 0.937 is adopted for training, 16 images are contained in each batch of training, and a total of 12 times are sent into the training network. The learn_rate_init of the first 200 rounds of training is set to 0.01, and as the iteration number increases, the learn_rate_init of the training is reduced to 0.001 in the last 100 iterations, and the training result is obtained.

[0052] The detection result of the YOLOv5 algorithm adopted in the application in the data set after preprocessing is as shown in Figure 3 The results show that the algorithm can complete anchor net detection under multi-scale multi-target, different shooting condition attributes, and can complete anchor net detection under various detection objects, shielding, anchor net junction and anchor net overlap, and the generalization degree meets the underground detection demand. The running center point extraction algorithm outputs the center point position coordinate information in the detection result.

[0053] S5, after the device motion control system obtains the path information, the pickup path point is fitted to the planned path. Taking a mechanical arm as an example, in order to ensure that the end effector of the mechanical arm travels along the planned trajectory, inverse solution calculation is performed on multiple path points, and the deflection degrees of each joint angle are solved according to the spatial coordinates of the end effector. Kinematics analysis is performed by using link transformation, and several basic rotation transformation matrices are introduced as follows: rotation matrix around x axis:

[0054]

[0055] Rotation matrix around y axis:

[0056]

[0057] Rotation matrix around z axis:

[0058]

[0059] The translation transformation can be divided into coordinate transformation along each axis direction, so that basic translation matrices Px, Py, Pz can be obtained, and a certain end pose can be represented by multiplying each conversion matrix as follows:

[0060] T = R(x, a) · R(y, a) · R(z, a) · Px · Py · Pz

[0061] According to the obtained joint angle transformation angle corresponding to the path point, a traditional fuzzy PID control method is used, and the transfer function is as follows:

[0062]

[0063] The integral, differential and proportional links are used to eliminate the rotation error between the joint angles. The control flow chart is shown in Figure 4 .

[0064] The specific embodiments of the present application are described above. It should be understood that the present application is not limited to the specific embodiments described above, and various modifications or changes can be made by those skilled in the art within the scope of the claims, which does not affect the essential content of the present application.

Claims

1. A control system for an intelligent drilling and anchoring robot that enables collaborative operation between a robotic arm and a drilling rig, characterized in that, include: The task timing and parallel arrangement subsystem includes a task timing and parallel characteristic analysis module and a device task sorting module; The scanning and detection strategy task allocation system includes a device scanning and detection module, a task progress detection module, and a task allocation module. The path planning and obstacle avoidance system includes a workspace construction module for motion equipment, a shortest path analysis module, a pole extension module, and a trajectory obstacle avoidance analysis module. The visual positioning system includes a body position calibration module, an interaction point visual recognition module, and an interaction calibration module; The equipment motion control system includes a body movement control module, a path point picking module, a joint variable calculation module, a fuzzy control module, and an interactive equipment adjustment module; The parallel characteristic analysis module collects the tasks of each device of the intelligent drilling and anchoring robot, examines the parallelism between different device tasks based on the time sequence, and then hands over the task time sequence and parallelism results obtained from the analysis to the device task sorting module for processing and outputs the task time sequence arrangement table. The motion device workspace construction module is used to simulate the robot's workspace and mark obstacles; the link extension module is used to adjust the volume of the tracking point according to the different types of links for tasks related to links, increasing the volume in the vertical direction to avoid interference between links and obstacles; the shortest path analysis module calculates and solves multiple task paths using the task content loading algorithm, and the trajectory obstacle avoidance analysis module verifies the solved paths for obstacle avoidance and outputs the optimal task path; The workflow of the visual positioning system is as follows: S1. Before the drilling and anchoring robot starts drilling and anchoring, the robot's direction of travel and distance of travel are checked and verified by the body position calibration module, using the previous working position as a reference. The actual position of the drilling and anchoring robot is determined based on the calculated variables. The measured data is compared with the reference data to obtain the position error. The position error is then handed over to the motion control system to adjust the robot's position. S2. Before the task begins, the drilling and anchoring robot performs a self-check of its equipment position. By identifying the feature points of each piece of equipment, the robot locates the current coordinates of each piece of equipment in space and then hands them over to the path planning and obstacle avoidance system to plan the movement path of the equipment. S3. During the task, the interactive point visual recognition module works, acquiring real-time images based on a binocular camera, collecting images through an adaptive image enhancement algorithm, and using graphic samples for deep learning training. Finally, a target detection model is derived to achieve downhole target recognition. The acquired target spatial coordinate information is input into the interactive calibration module to achieve accurate positioning.

2. The intelligent drilling and anchoring robot control system for collaborative operation of a robotic arm and drilling rig as described in claim 1, characterized in that, The path point picking module, joint variable calculation module, fuzzy control module, and interactive device adjustment module are used to realize path point segmentation, multi-point continuous operation, and parameter-driven interactive calibration during the robot's movement. Thus, driven by the task content of the drilling and anchoring robot, the horizontal and vertical displacement of the drilling rig, the movement of the robotic arm end effector, and the rotation of the anchor bar supply rod are controlled respectively.

3. The intelligent drilling and anchoring robot control system for collaborative operation of a robotic arm and drilling rig as described in claim 1, characterized in that, The main body movement control module controls the drive wheels of the drilling and anchoring robot's tracks. By controlling the movement and turning of the drilling and anchoring robot through the same speed in the same direction and different speed in opposite directions, the module, combined with the vision positioning system, ensures the accuracy of the drilling and anchoring robot's working position.

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