Three-dimensional space automatic route searching method and device, semi-physical simulation method and platform

Abstract

The application discloses a three-dimensional space automatic path searching method and device, a semi-physical simulation method and platform. s s , a plane (Y d d ) where an intermediate point P is located, and a plane (X d d ) where a target point D is located; the starting point S realizes path searching in the planes (X s s ), (Y d d ) and (X d d ) in sequence, passes through the intermediate point P first, and finally reaches the target point D, so that the automatic path searching in the three-dimensional space is realized, and the optimal automatic path searching is realized.The semi-physical simulation method and platform are based on the three-dimensional space automatic path searching method, and form an interactive training system combining an intelligent rigid body forming a dynamic mechanical hand model and an entity console operation, so that the interaction relationship between nuclear industry dynamic mechanical hands and the influence of an external environment on the nuclear industry dynamic mechanical hands can be more realistically reflected.​​​​​

Description

Technical Field

[0001] This invention relates to the field of simulation technology for nuclear industry power robots, specifically to a three-dimensional space automatic pathfinding method, device, hardware-in-the-loop simulation method and platform. Background Technology

[0002] With the application of artificial intelligence to nuclear industry powered robotic arms, their operation has become a comprehensive system integrating environmental perception, path planning, and motion control. However, directly operating these robotic arms incurs high trial-and-error costs, and current technologies lack hardware-in-the-loop simulation models for them. Therefore, it is necessary to research a simulation method or platform for simulating and training nuclear industry powered robotic arms.

[0003] Simulation includes pure virtual simulation and semi-physical simulation. Compared with pure virtual simulation, semi-physical simulation can more realistically reflect the interaction between nuclear power robots and the impact of the external environment on nuclear power robots.

[0004] Although the A* algorithm exists in the existing technology, it is a pathfinding and graph traversal algorithm, but it can only be used for two-dimensional pathfinding and cannot achieve pathfinding in three-dimensional space. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the prior art by providing a three-dimensional space automatic path finding method, device, hardware-in-the-loop simulation method and platform. The three-dimensional space automatic path finding method can automatically find the optimal path in three-dimensional space to realize hardware-in-the-loop simulation of a nuclear industry power robot with the optimal path.

[0006] In a first aspect, the present invention provides a three-dimensional automatic pathfinding method, the algorithm of which is used in nuclear industry powered robotic arms, and the algorithm includes the following steps:

[0007] Step S1: Determine the starting point S(X) in three-dimensional space s Y s Z s ), target point D(X) d Y d Z d ), and the intermediate point P(X) d Y d Z s The three-dimensional space includes the first plane (X) where the starting point is located. s Y s The second plane (Y) where the midpoint is located d Z d );

[0008] The first plane is the XY plane, and the second plane is the YZ plane, perpendicular to the first plane; the starting point S(X s Y s Z s ) and the intermediate point P(X) d Y d Z s The target point D(X) has the same Z-axis distance. d Y d Z d ) and the intermediate point P(X) d Y d Z s They have the same distance in the X direction and the same distance in the Y direction;

[0009] Step S2: Find the starting point S(X) on the first plane. s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path;

[0010] Step S3: Find the midpoint P(X) on the second plane d Y d Z s ) to target point D(X) d Y d Z d The optimal path, based on the starting point S(X) s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path and intermediate point P(X) d Y d Z s ) to target point D(X) d Y d Z d The optimal path to obtain the starting point S(X) s Y s Z s ) to target point D(X) d Y d Z d The optimal path is found, thus enabling automatic pathfinding in three-dimensional space.

[0011] Further, step S2 specifically includes:

[0012] Step S21: Based on the starting point S(X) sY s Z s ) and the intermediate point P(X) d Y d Z s The first plane is divided into grids based on the distance of ), resulting in the first two-dimensional grid;

[0013] Step S22: Set the starting point S(X) s Y s Z s Add the starting point S(X) to the first Close list and set the starting point S(X) to the first Close list. s Y s Z s Find the starting point S(X) as the first parent node. s Y s Z s The squares that can be reached in the first two-dimensional grid;

[0014] Step S23: Set the starting point S(X) s Y s Z s The squares reachable in the first two-dimensional grid are added to the first Open list, and the total consumption F at the starting point is calculated. s ,

[0015] F s =H s +G s ;

[0016] G s H represents the movement cost from the starting point s to the first square. s This indicates moving from the first square to the middle point P(X). d Y d Z s The estimated consumption of ) is calculated, with the first square being the starting point S(X). s Y s Z s The total consumption F in the reachable squares within the first two-dimensional grid. s The lowest square;

[0017] Step S24: Add the first square to the first Close list, and use the first square as the second parent node, and find the squares that the first square can reach in the first two-dimensional grid; exclude the squares that the first square can reach in the first two-dimensional grid from the first Close list.

[0018] Step S25: Add the cells reachable from the first cell in the first two-dimensional grid to the first Open list, and calculate the total consumption F of the first cell. n ,

[0019] F n =H n +G n ;

[0020] G n H represents the cost of moving from the nth square to the (n+1)th square. n This indicates moving from the (n+1)th square to the midpoint P(X). d Y d Z s The estimated consumption of the n+1th square is the total consumption F in the squares that the nth square can reach in the first two-dimensional grid. n The lowest square;

[0021] Step S26: Continue in this manner, repeating steps S24 and S25 until the intermediate point P(X). d Y d Z s It has been added to the first Close list;

[0022] Step S27: From the midpoint P(X) of the last item added to the first Close list d Y d Z s Starting from point S(X), the process backtracks step by step to the first parent node by finding the parent node, in order to form a path on the first plane starting from the starting point S(X). s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path.

[0023] Furthermore, in step S3, the intermediate point P(X) is found on the second plane. d Y d Z s ) to target point D(X) d Y d Z d The optimal path, specifically including the following steps:

[0024] Step S31: Based on the intermediate point P(X) d Y d Z s ) and target point D(X) d Y d Z d The second plane is divided into grids based on the distance of ), resulting in a second two-dimensional grid;

[0025] Step S32: Set the midpoint P(X) d Yd Z s Add the middle point P(X) to the second Close list and add it to the middle point P(X). d Y d Z s Using ) as the first parent node, find the intermediate point P(X). d Y d Z s The squares that can be reached in the second two-dimensional grid;

[0026] Step S33: Set the midpoint P(X) d Y d Z s The cells reachable in the second 2D grid are added to the second Open list, and the total consumption F is calculated. z ,

[0027] F z =H z +G z ;

[0028] G z H represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. z This indicates moving from the (m+1)th grid cell to the middle point P(X). d Y d Z s The estimated consumption of the m+1th grid is the total consumption F in the grids that the mth grid can reach in the first two-dimensional grid. z The lowest grid cell;

[0029] Step S34: Add the first grid cell to the second Close list, and use the first grid cell as the second parent node. Find the grid cells that the first grid cell can reach in the second two-dimensional grid. Exclude the grid cells that the first grid cell can reach in the second two-dimensional grid from the second Close list.

[0030] Step S35: Add the grids reachable from the first grid in the second two-dimensional grid to the second Open list, and calculate the total consumption F of the first grid. m ,

[0031] F m =H m +G m ;

[0032] G n H represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. n This indicates moving from the (m+1)th grid cell to the middle point P(X). d Y d Z sThe estimated consumption of the m+1th grid is the total consumption F in the grids that the mth grid can reach in the first two-dimensional grid. s The lowest grid cell;

[0033] Step S36: Continue in this manner, repeating steps S34 and S35 until the intermediate point P(X). d Y d Z s It has been added to the second Close list;

[0034] Step S37: From the target point D(X) that was last added to the second Close list d Y d Z d By searching for the parent node, we backtrack step by step to the first parent node, in order to form a path on the second plane at the intermediate point P(X). d Y d Z s ) to target point D(X) d Y d Z d The optimal path.

[0035] Furthermore, the three-dimensional space also includes the third plane (X) where the target point is located. d Y d ), the third plane (X) d Y d ) is the XY plane, and is adjacent to the first plane (X s Y s )parallel,

[0036] Step S3 is followed by step S4:

[0037] Step S4: Confirm the target point D(X) on the third plane. d Y d Z d If there is no deviation, then the target point D(X) is... d Y d Z d No correction is performed; if there is a deviation, then the target point D(X) is recalibrated on the third plane. d Y d Z d Automatic pathfinding deviation correction is performed.

[0038] The target point D(X) on the third plane d Y d Z d The automatic pathfinding deviation correction includes the following steps:

[0039] Step S41: Confirm the target point D(X) d Y d Z d ) and the target point E(X) e Y e Z e The target point is the final destination point.

[0040] Step S42: Based on the target point D(X) d Y d Z d ) and the target point E(X) e Y e Z e The third plane is divided into grids to obtain the third three-dimensional grid.

[0041] Step S43: Set the target point D(X) d Y d Z d Add the target point D(X) to the third Close list and set the target point D(X) to the Close list. d Y d Z d Using as the first node, find the starting point and target point D(X). d Y d Z d The squares that can be reached in the third-dimensional grid;

[0042] Step S44: Set the target point D(X) d Y d Z d The squares reachable in the third 2D grid are added to the third Open list, and the target point D(X) is calculated. d Y d Z d Total consumption F d ,

[0043] F d =H d +G d ;

[0044] G d H represents the movement cost from the starting point s to the first grid. s This indicates moving from the first grid to E(X). e Y e Z e The estimated consumption of ), with the first grid being the target point D(X). d Y d Z d The total consumption F in the reachable squares of the third-dimensional grid. d The lowest square;

[0045] S45: Add the first grid to the third Close list, and use the first grid as the second top node, and find the grids that the first grid can reach in the third 2D grid; exclude the grids that the first grid can reach in the third 2D grid from the third Close list;

[0046] Step S46: Add the grids reachable by the first grid in the first two-dimensional grid to the third Open list, and calculate the total consumption F of the first grid. k ,

[0047] F k =H k +G k ;

[0048] G k H represents the movement cost from grid k to grid (k+1). k This indicates moving from the (k+1)th grid to the midpoint P(X). d Y d Z s The estimated consumption of the n+1 grid is the total consumption F in the grids reachable from the nth grid in the first two-dimensional grid. s The lowest grid;

[0049] Step S47: Repeat steps S45 and S46 until the target point E(X) is reached. e Y e Z e It has been added to the third Close list;

[0050] Step S48: From the last point E(X) added to the third Close list e Y e Z e Starting from point D, the algorithm backtracks step by step to the first node by finding the previous node, in order to form a path on the third plane leading to the target point D(X). d Y d Z d ) to the target point E(X) e Y e Z e The correction path.

[0051] Secondly, this invention provides a hardware-in-the-loop simulation method for use in nuclear industry power robotic arms, the method comprising the following steps:

[0052] Step A1: Obtain monitoring information for monitoring the control box; and obtain the motion path of the nuclear industry power robot during movement according to the above-mentioned three-dimensional space automatic path finding method;

[0053] Step A2: Use the monitoring information and the motion path of the nuclear industry power robot as input control information to control the simulation operation of the nuclear industry power robot, so as to complete the semi-physical simulation of the nuclear industry power robot.

[0054] Further, step A2 specifically includes:

[0055] Step A21: The monitoring information and the motion path of the nuclear industry power robot during its movement are transmitted to the DSP control board as input control information;

[0056] Step A22: Based on the input control information, the DSP control board outputs corresponding control signals and control parameters to the control box;

[0057] Step A23: The control box controls the simulation operation of the nuclear industry power robot according to the control signals and control parameters output by the DSP control board, so as to complete the semi-physical simulation of the nuclear industry power robot.

[0058] Furthermore, step A23 also includes:

[0059] When the simulation operation of the nuclear industry powered robot is running, if the running distance of the nuclear industry powered robot is detected to be greater than a preset threshold, the robot will stop its current driving operation and be driven to run in the opposite direction for a certain distance, which is a preset safety distance.

[0060] Furthermore, step A0 is included before step A1.

[0061] A0: A virtual-real hybrid platform is pre-constructed, which is a semi-physical simulation platform for nuclear industry power robotic arms;

[0062] as well as,

[0063] Define and configure the operability of the nuclear industry powered robot by pre-defining its parameters;

[0064] Step A2 specifically involves transmitting the monitoring information and the motion path of the nuclear industry power robot as input control information to the DSP control board of the hardware-in-the-loop simulation platform of the nuclear industry power robot.

[0065] as well as,

[0066] Based on the input control information, the DSP control board outputs corresponding control signals and control parameters to the control box of the hardware-in-the-loop simulation platform for the nuclear industry power robot.

[0067] Furthermore, the parameters of the predefined nuclear industry power robot are defined, specifically including:

[0068] The parameters of the operating unit of the nuclear industry power robot are customized.

[0069] And / or,

[0070] This allows for the addition, deletion, modification, and search operations of the operating units of nuclear industry power robotic arms.

[0071] And / or,

[0072] Establish the relationships between the operating units of the nuclear industry's powered robotic arm.

[0073] And / or,

[0074] Based on the kinematic model of the nuclear industry power manipulator, the operating unit of the nuclear industry power manipulator is configured with equipment parameters, motion parameters, motion sequence, constraint parameters, and spatial parameters.

[0075] And / or,

[0076] Determine the degrees of freedom for the movement of the nuclear industry's powered robotic arm.

[0077] And / or,

[0078] The operating unit of the nuclear industry power robot is set to move according to the three-dimensional space automatic path finding method;

[0079] The operability of the nuclear industry powered robotic arm specifically includes:

[0080] Configuration of operating unit, degree of freedom, clamp coordinate system, base coordinate system, link coordinate system, and absolute coordinate system.

[0081] Furthermore, step A4 also includes:

[0082] When the simulation operation of the nuclear industry powered robot is running, if the running distance of the nuclear industry powered robot is detected to be greater than a preset threshold, the robot will stop its current driving operation and be driven to run in the opposite direction for a certain distance, which is a preset safety distance.

[0083] Thirdly, the present invention provides a three-dimensional spatial automatic pathfinding device, the device comprising:

[0084] Determining unit, used to determine the starting point S(X) in three-dimensional space. s Y s Z s ), target point D(X) d Y d Z d ), and the intermediate point P(X) d Y d Zs The three-dimensional space includes the first plane (X) where the starting point is located. s Y s The second plane (Y) where the midpoint is located d Z d );

[0085] Wherein, the first plane is the XY plane, and the second plane is the YZ plane, perpendicular to the first plane; the starting point S(X s Y s Z s ) and the intermediate point P(X) d Y d Z s The target point D(X) has the same Z-axis distance. d Y d Z d ) and the intermediate point P(X) d Y d Z s They have the same distance in the X direction and the same distance in the Y direction;

[0086] The first search unit, connected to the determining unit, is used to search for the starting point S(X) on the first plane. s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path;

[0087] The second search unit, connected to both the determining unit and the first search unit, is used to find the intermediate point P(X) on the second plane. d Y d Z s ) to target point D(X) d Y d Z d The optimal path, based on the starting point S(X) s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path and intermediate point P(X) d Y d Z s ) to target point D(X) d Y d Z d The optimal path to obtain the starting point S(X) s Y s Zs ) to target point D(X) d Y d Z d The optimal path is found, thus enabling automatic pathfinding in three-dimensional space.

[0088] Furthermore, the first search unit includes:

[0089] The first grid segmentation module, connected to the determining unit, is used to segment data based on the starting point S(X). s Y s Z s ) and the intermediate point P(X) d Y d Z s The first plane is divided into grids based on the distance of ), resulting in the first two-dimensional grid;

[0090] A first storage module, connected to the first grid segmentation module, is used to store a first Close list; the first Close list includes a first plane (X). s Y s Points that have already been processed;

[0091] The second storage module, connected to the first grid segmentation module, is used to store the first Open list; the first Open list includes the first plane (X). s Y s Unprocessed points on the surface;

[0092] The first searching module, connected to the first grid segmentation module, the first storage module, and the second storage module respectively, is used to find the total consumption F. n The smallest parent node, where the parent node is the middle point P(X) added last to the first Close list. d Y d Z s Starting from point S(X), the algorithm backtracks step by step to the first parent node to form a path on the first plane starting from the starting point S(X). s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path, with the first parent node being the starting point S(X). s Y s Z s The second parent node is the starting point S(X). s Y s Z sThe cell with the lowest total consumption among the cells reachable in the first two-dimensional grid is the next parent node, and so on.

[0093] Total consumption F of the square n The calculation formula is as follows:

[0094] F n =H n +G n ;

[0095] G n H represents the cost of moving from the nth square to the (n+1)th square. n This indicates moving from the (n+1)th square to the midpoint P(X). d Y d Z s The estimated consumption of the n+1th square is the total consumption F in the squares that the nth square can reach in the first two-dimensional grid. n The lowest square.

[0096] Furthermore, the second search unit includes:

[0097] The second grid segmentation module, connected to the determining unit, is used to segment data based on the intermediate point P(X). d Y d Z s ) and target point D(X) d Y d Z d The second plane is divided into grids based on the distance of ), resulting in a second two-dimensional grid;

[0098] The third storage module, connected to the second grid segmentation module, is used to store the second Close list; the second Close list includes the second plane (Y). d Z d Points that have already been processed;

[0099] The fourth storage module, connected to the second grid segmentation module, is used to store the second Open list; the first Open list includes the second plane (Y). d Z d Unprocessed points on the surface;

[0100] The second searching module, connected to the second grid segmentation module, the third storage module, and the fourth storage module respectively, is used to find the total consumption F. z The smallest of all parent nodes, the parent node is the target point D(X) that was last added to the second Close list. d Y d Zd Starting from point X, the algorithm backtracks step by step to the first parent node to form a path on the second plane at the intermediate point P(X). d Y d Z s ) to target point D(X) d Y d Z d The optimal path is where the first mother node is the intermediate point P(X). d Y d Z s The second mother node is the intermediate point P(X). d Y d Z s The next parent node is the cell with the lowest total consumption among the cells reachable from the previous parent node in the second two-dimensional grid.

[0101] Total consumption F of the grid z The calculation formula is as follows:

[0102] F z =H z +G z ;

[0103] G z H represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. z This indicates moving from the (m+1)th grid cell to the middle point P(X). d Y d Z s The estimated consumption of the m+1th grid is the total consumption F in the grids that the mth grid can reach in the first two-dimensional grid. z The lowest grid.

[0104] Fourthly, the present invention provides a hardware-in-the-loop simulation platform for use in nuclear industry power robotic arms, the platform comprising:

[0105] The host PC includes a determination unit, a first search unit, a second search unit, and a monitoring unit.

[0106] The determining unit is used to determine the starting point S(X) in three-dimensional space. s Y s Z s ), target point D(X) d Y d Z d ), and the intermediate point P(X) d Y d Z sThe three-dimensional space includes the first plane (X) where the starting point is located. s Y s The second plane (Y) where the midpoint is located d Z d );

[0107] Wherein, the first plane is the XY plane, and the second plane is the YZ plane, perpendicular to the first plane; the starting point S(X s Y s Z s ) and the intermediate point P(X) d Y d Z s The target point D(X) has the same Z-axis distance. d Y d Z d ) and the intermediate point P(X) d Y d Z s They have the same distance in the X direction and the same distance in the Y direction;

[0108] The first search unit is connected to the determining unit and is used to search for the starting point S(X) on the first plane. s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path;

[0109] The second search unit is connected to both the determining unit and the first search unit, and is used to find the midpoint P(X) on the second plane. d Y d Z s ) to target point D(X) d Y d Z d The optimal path, based on the starting point S(X) s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path and intermediate point P(X) d Y d Z s ) to target point D(X) d Y d Z d The optimal path to obtain the starting point S(X) s Y s Z s) to target point D(X) d Y d Z d The optimal path;

[0110] The monitoring unit is used to monitor the operation of the control box;

[0111] The DSP control board is connected to the host PC via a CAN bus and is used to output corresponding control signals and control parameters to the control box based on the input control information from the second lookup unit and the monitoring unit.

[0112] The control box, connected to the host PC via a CAN bus, is used to simulate the set actions of the nuclear industry power robot based on the control signals and control parameters output by the DSP control board, thereby completing the semi-physical simulation of the nuclear industry power robot.

[0113] Furthermore, the platform also includes:

[0114] The construction unit is used to pre-build a virtual-real hybrid platform, which is a semi-physical simulation platform for nuclear industry power robotic arms;

[0115] The hardware-in-the-loop simulation platform for nuclear industry power robots includes a virtual control unit and a field operation unit.

[0116] The virtual control unit, based on a PC, includes an algorithm module, a DSP control module, a monitoring module, a virtual button module, and a display interface module. The algorithm module is used to obtain the motion path of the nuclear industry power robot based on a three-dimensional automatic pathfinding method. The monitoring module is used to monitor the control box. The virtual button module is used to simulate the operation of the nuclear industry power robot. The display interface module is used to display the control information of the DSP control module, the monitoring information of the monitoring module, and the virtual buttons of the virtual button module.

[0117] The field operation unit includes physical buttons on the control box. There is a one-to-one mapping relationship between the physical buttons and the virtual buttons. When a physical button is pressed, the working state of the corresponding virtual button is configured to be active. When the physical button is not pressed, the working state of the corresponding virtual button is configured to be inactive.

[0118] Furthermore, the control box includes:

[0119] The data acquisition box and the operating component are interconnected. The data acquisition box is used to collect user input information from the operating component so that the DSP control board can output corresponding control signals and control parameters.

[0120] The operating components include a universal drive joystick, a software emergency stop button, a buzzer, a power indicator light, a fault indicator light, and an enable indicator light;

[0121] A universal drive joystick is used to precisely control the movement of nuclear industry power robotic arms;

[0122] The software emergency stop button is used to stop the software from running in case of an malfunction in the host PC.

[0123] Buzzers are often used to emit alert or alarm sounds;

[0124] The power indicator light is used to indicate the power supply status. When the power is on, the power indicator light is on; when the power is not on, the power indicator light is off.

[0125] Fault indicator lights are used to indicate faults or abnormalities;

[0126] The enable indicator light is used to indicate that the operation or function of the nuclear industry's powered robotic arm is normal.

[0127] The beneficial effects of this invention are:

[0128] (1) The three-dimensional automatic pathfinding method in this invention extends the idea of ​​A* two-dimensional pathfinding to three-dimensional space. Specifically, it divides the three-dimensional space into the plane (X) where the starting point S is located. s Y s ), the plane containing the midpoint P (Y) d Z d ) and the plane (X) where the target point D is located. d Y d The starting point s is located sequentially in the plane (X). s Y s ), (Y d Z d ) and (X d Y d The system achieves pathfinding by first passing through the intermediate point P and finally reaching the target point D, thus realizing automatic pathfinding in three-dimensional space.

[0129] (2) The nuclear industry power manipulator three-dimensional space automatic path finding method, device, semi-physical simulation method and platform in this invention is an interactive training system that combines intelligent rigid body and physical control console operation based on three-dimensional space automatic path finding to form a power manipulator model.

[0130] (3) The semi-physical simulation method for nuclear power manipulator in this invention also includes collision detection and reverse drive. When the running distance of the nuclear power manipulator is detected to be greater than a preset threshold, the nuclear power manipulator is controlled to stop the current drive operation and is driven to run in reverse for a distance. The distance is a preset safety distance, which ensures the safety of the system.

[0131] (4) The three-dimensional space automatic pathfinding method, device, semi-physical simulation method and platform in this invention can not only more realistically reflect the interaction between nuclear industry power robots and the influence of the external environment on nuclear industry power robots, but also be used to test, verify and improve existing procedures and processes to reduce errors and improve products or industrial processes. It can also enable trainees to better feel and understand relevant knowledge and skills, so that trainees can practice effectively in a more realistic environment, thereby accumulating practical experience, improving operational skills and enhancing practical confidence. Attached Figure Description

[0132] Figure 1 This is a system architecture diagram in an embodiment of the present invention;

[0133] Figure 2 This is a design diagram of the training options interface in an embodiment of the present invention;

[0134] Figure 3 This is a schematic diagram of the movement of a nuclear industry power robot in an embodiment of the present invention;

[0135] Figure 4 This is a multi-view display function diagram in an embodiment of the present invention;

[0136] Figure 5 This is a schematic diagram of the console's appearance in an embodiment of the present invention;

[0137] Figure 6 This is a schematic diagram of the control box in an embodiment of the present invention;

[0138] Figure 7 This is a schematic diagram of the console design in an embodiment of the present invention;

[0139] Figure 8 This is a schematic diagram of the planar partitioning of the automatic pathfinding method in an embodiment of the present invention;

[0140] Figure 9 This is a schematic diagram of the pathfinding plane at the starting position height in an embodiment of the present invention;

[0141] Figure 10 This is a schematic diagram of the optimal path obtained by the automatic pathfinding method in this embodiment of the invention;

[0142] Figure 11 This is a schematic diagram of the pathfinding plane for the target point height in an embodiment of the present invention.

[0143] Figure 5 and Figure 6The markings in the image are as follows: 1-Hanging ring, 2-Interactive software training screen display, 3-Running parameter display, 4-Computer switch panel, 5-Control box, 6-Cast, 7-Keyboard, 8-Mouse, 9-Power switch, 10-Interactive software virtual model display, 11-Interactive software control panel display, 12-Software emergency stop, 13-Buzzer, 14-Power green LED, 15-Fault red LED, 16-Enable yellow LED, 17-3.5-inch status display LCD, 18-Heavy crane control joystick, 19-Sleeve control joystick, 20-Shoulder / upper arm / forearm control joystick, 21-Wrist / clamping control joystick. Detailed Implementation

[0144] To enable those skilled in the art to better understand the technical solution of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0145] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining the invention and are not intended to limit the invention.

[0146] It is understood that, without conflict, the various embodiments and features in the embodiments of the present invention can be combined with each other.

[0147] It is understood that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, while the parts unrelated to the present invention are not shown in the drawings.

[0148] It is understood that each unit or module involved in the embodiments of the present invention may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure.

[0149] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this invention may occur in a different order than that marked in the accompanying drawings.

[0150] It is understood that the flowcharts and block diagrams of this invention illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this invention. Each block in the flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagram and flowchart can be implemented using a hardware-based system to achieve the specified function, or using a combination of hardware and computer instructions.

[0151] It is understood that the units and modules involved in the embodiments of the present invention can be implemented by software or by hardware. For example, the units and modules can be located in a processor.

[0152] Example 1: As Figure 8 , Figure 9 , Figure 10 and Figure 11 As shown, this embodiment provides a three-dimensional automatic pathfinding method. This algorithm is used in nuclear industry powered robotic arms and includes the following steps:

[0153] Step S1: Determine the starting point S(X) in three-dimensional space s Y s Z s ), target point D(X) d Y d Z d ), and the intermediate point P(X) d Y d Z s The three-dimensional space includes the first plane (X) where the starting point is located. s Y s The second plane (Y) where the midpoint is located d Z d );

[0154] The first plane is the XY plane, and the second plane is the YZ plane, perpendicular to the first plane; the starting point S(X s Y s Z s ) and the midpoint P(X) d Y d Z s The target point D(X) has the same Z-axis distance. d Y d Z d ) and the midpoint P(X) d Y d Z s They have the same distance in the X direction and the same distance in the Y direction;

[0155] Three-dimensional space also includes the third plane (X) where the target point is located. d Y d ), the third plane (X) d Y d ) is the XY plane, and is adjacent to the first plane (X s Y s )parallel,

[0156] Step S2: Find the starting point S(X) on the first plane. s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path;

[0157] Step S21: Based on the starting point S(X) s Y s Z s ) and the intermediate point P(X) d Y d Z s The first plane is divided into grids based on the distance of ), resulting in the first two-dimensional grid;

[0158] Step S22: Set the starting point S(X) s Y s Z s Add the starting point S(X) to the first Close list and set the starting point S(X) to the first Close list. s Y s Z s Find the starting point S(X) as the first parent node. s Y s Z s The squares that can be reached in the first two-dimensional grid;

[0159] Step S23: Set the starting point S(X) s Y s Z s The squares reachable in the first two-dimensional grid are added to the first Open list, and the total consumption F at the starting point is calculated. s ,

[0160] F s =H s +G s ;

[0161] G s H represents the movement cost from the starting point s to the first square. s This indicates moving from the first square to the middle point P(X). d Y d Z s The estimated consumption of ) is calculated, with the first square being the starting point S(X). s Y s Z s The total consumption F in the reachable squares within the first two-dimensional grid. s The lowest square;

[0162] Step S24: Add the first square to the first Close list, and use the first square as the second parent node, and find the squares that the first square can reach in the first two-dimensional grid; exclude the squares that the first square can reach in the first two-dimensional grid from the first Close list.

[0163] Step S25: Add the cells reachable from the first cell in the first two-dimensional grid to the first Open list, and calculate the total consumption F of the first cell. n ,

[0164] F n =H n +G n ;

[0165] G n H represents the cost of moving from the nth square to the (n+1)th square. n This indicates moving from the (n+1)th square to the midpoint P(X). d Y d Z s The estimated consumption of the n+1th square is the total consumption F in the squares that the nth square can reach in the first two-dimensional grid. n The lowest square;

[0166] Step S26: Continue in this manner, repeating steps S24 and S25 until the intermediate point P(X). d Y d Z s It has been added to the first Close list;

[0167] Step S27: From the midpoint P(X) of the last item added to the first Close list d Y d Z s Starting from point S(X), the process backtracks step by step to the first parent node by finding the parent node, in order to form a path on the first plane starting from the starting point S(X). s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path;

[0168] Step S3: Find the midpoint P(X) on the second plane d Y d Z s ) to target point D(X) d Y d Z d The optimal path, based on the starting point S(X) s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path and intermediate point P(X) d Y d Z s) to target point D(X) d Y d Z d The optimal path to obtain the starting point S(X) s Y s Z s ) to target point D(X) d Y d Z d The optimal path is found, thus enabling automatic pathfinding in three-dimensional space.

[0169] In step S3, the intermediate point P(X) is found on the second plane. d Y d Z s ) to target point D(X) d Y d Z d The optimal path, specifically including the following steps:

[0170] Step S31: Based on the intermediate point P(X) d Y d Z s ) and target point D(X) d Y d Z d The second plane is divided into grids based on the distance of ), resulting in a second two-dimensional grid;

[0171] Step S32: Set the midpoint P(X) d Y d Z s Add the middle point P(X) to the second Close list and add it to the middle point P(X). d Y d Z s Using ) as the first parent node, find the intermediate point P(X). d Y d Z s The squares that can be reached in the second two-dimensional grid;

[0172] Step S33: Set the midpoint P(X) d Y d Z s The cells reachable in the second 2D grid are added to the second Open list, and the total consumption F is calculated. z ,

[0173] F z =H z +G z ;

[0174] G z H represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. zThis indicates moving from the (m+1)th grid cell to the middle point P(X). d Y d Z s The estimated consumption of the m+1th grid is the total consumption F in the grids that the mth grid can reach in the first two-dimensional grid. z The lowest grid cell;

[0175] Step S34: Add the first grid cell to the second Close list, and use the first grid cell as the second parent node. Find the grid cells that the first grid cell can reach in the second two-dimensional grid. Exclude the grid cells that the first grid cell can reach in the second two-dimensional grid from the second Close list.

[0176] Step S35: Add the grids reachable from the first grid in the second two-dimensional grid to the second Open list, and calculate the total consumption F of the first grid. m ,

[0177] F m =H m +G m ;

[0178] G n H represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. n This indicates moving from the (m+1)th grid cell to the middle point P(X). d Y d Z s The estimated consumption of the m+1th grid is the total consumption F in the grids that the mth grid can reach in the first two-dimensional grid. s The lowest grid cell;

[0179] Step S36: Continue in this manner, repeating steps S34 and S35 until the intermediate point P(X). d Y d Z s It has been added to the second Close list;

[0180] Step S37: From the target point D(X) that was last added to the second Close list d Y d Z d By searching for the parent node, we backtrack step by step to the first parent node, in order to form a path on the second plane at the intermediate point P(X). d Y d Z s ) to target point D(X) d Y d Z d The optimal path.

[0181] In theory, with the first three steps, the starting point S can be reached to the target point D. However, for deviation correction, step S4 is included after step S3:

[0182] Step S4: Confirm the target point D(X) on the third plane. d Y d Z d If there is no deviation, then the target point D(X) is... d Y d Z d No correction is performed; if there is a deviation, then the target point D(X) is recalibrated on the third plane. d Y d Z d Automatic pathfinding deviation correction is performed.

[0183] For the target point D(X) on the third plane d Y d Z d The automatic pathfinding deviation correction includes the following steps:

[0184] Step S41: Confirm the target point D(X) d Y d Z d ) and the target point E(X) e Y e Z e The marked point is the final target point;

[0185] Step S42: Based on the target point D(X) d Y d Z d ) and the target point E(X) e Y e Z e The third plane is divided into grids to obtain the third three-dimensional grid.

[0186] Step S43: Set the target point D(X) d Y d Z d Add the target point D(X) to the third Close list and set the target point D(X) to the Close list. d Y d Z d Using as the first node, find the starting point and target point D(X). d Y d Z d The squares that can be reached in the third-dimensional grid;

[0187] Step S44: Set the target point D(X) d Y d Z dThe squares reachable in the third 2D grid are added to the third Open list, and the target point D(X) is calculated. d Y d Z d Total consumption F d ,

[0188] F d =H d +G d ;

[0189] G d H represents the movement cost from the starting point s to the first grid. s This indicates moving from the first grid to E(X). e Y e Z e The estimated consumption of ), with the first grid being the target point D(X). d Y d Z d The total consumption F in the reachable squares of the third-dimensional grid. d The lowest square;

[0190] S45: Add the first grid to the third Close list, and use the first grid as the second top node, and find the grids that the first grid can reach in the third 2D grid; exclude the grids that the first grid can reach in the third 2D grid from the third Close list;

[0191] Step S46: Add the grids reachable by the first grid in the first two-dimensional grid to the third Open list, and calculate the total consumption F of the first grid. k ,

[0192] F k =H k +G k ;

[0193] G k H represents the movement cost from grid k to grid (k+1). k This indicates moving from the (k+1)th grid to the midpoint P(X). d Y d Z s The estimated consumption of the n+1 grid is the total consumption F in the grids reachable from the nth grid in the first two-dimensional grid. s The lowest grid;

[0194] Step S47: Repeat steps S45 and S46 until the target point E(X) is reached. e Y e Z eIt has been added to the third Close list;

[0195] Step S48: From the last point E(X) added to the third Close list e Y e Z e Starting from point D, the algorithm backtracks step by step to the first node by finding the previous node, in order to form a path on the third plane leading to the target point D(X). d Y d Z d ) to the target point E(X) e Y e Z e The correction path.

[0196] Specifically, given the cost function g(n) representing the path length from the current node to the initial point, and h(n) representing the straight-line distance from the current node to the target point (x,y,z) as the heuristic function:

[0197]

[0198] The total cost f(n) of the node is then calculated using the evaluation function, which has the following form:

[0199] F n =H n +G n

[0200] The specific steps are as follows:

[0201] Based on the target point (x,y) plane, construct a pathfinding plane mesh for the starting position height.

[0202] Step 1: Set the starting position (x) s ,y s ,z s The origin (0,0,0) is used as the grid point and placed in the Open list; the installation position (x, y, y) is calculated based on the starting position coordinates, the installation position coordinates, and the step size. d ,y d Planar grid.

[0203] Step 2: Repeat the following steps:

[0204] Find the cell with the lowest F value in the Open list and use it as the current cell;

[0205] Add the current cell to the Close list; the Open list is used to store unvisited nodes, and the Close list is used to store visited nodes;

[0206] Process each of the 36 adjacent grid cells;

[0207] If it fails the collision interference check or is already in the Close list, skip it; otherwise, proceed as follows:

[0208] If it is not in the Open list, add it to the Open list, use the current grid as the parent node of the grid, and calculate the F, G, and H values ​​of the grid;

[0209] If it is already in the Open list, check if the new path is better with the G value as a reference. A lower G value means a better path. If so, change the parent node of the grid to the current grid and recalculate the F, G and H values ​​of the grid.

[0210] Stop. When the grid where the installation location is located is added to the Close list, the path is found. Or, if the target grid is not found and the Open list is empty, it means that the path does not exist.

[0211] Step 3: Save the path. Starting from the target grid, move along the parent node of each grid until you return to the original grid. This path is the found path (x). d ,y d The optimal path on the plane, such as Figure 10 As shown:

[0212] The depth-traveling robot moves to the (x,y) plane at the z-value of the target point.

[0213] Step 1: Extend and retract the sleeve along the z-value direction of the target point until the sleeve collides with the obstacle;

[0214] Step 2: Using the collision point (x) p ,y p Create a small mesh around the center, traverse the mesh to find all collision points within the range, define a ColPoints list to store the collision points, and continue to extend and retract the sleeve while avoiding collision points; repeat the above actions until the node moves to the (x,y) plane with z value.

[0215] Pathfinding mesh plotting in the (x,y) plane at the z-value of the target point

[0216] With the collision point (x) p ,y p Create a small mesh around the center, traverse the mesh to find all collision points within the range, define a ColPoints list to store the collision points, and continue to extend and retract the sleeve while avoiding collision points; repeat the above actions until the node moves to the (x,y) plane with z value.

[0217] The three-dimensional automatic pathfinding method in this embodiment extends the idea of ​​A* two-dimensional pathfinding to three-dimensional space. Specifically, it divides the three-dimensional space into the plane (X) where the starting point S is located. s Ys ), the plane containing the midpoint P (Y) d Z d ) and the plane (X) where the target point D is located. d Y d The starting point s is located sequentially in the plane (X). s Y s ), (Y d Z d ) and (X d Y d The system achieves pathfinding by first passing through the intermediate point P and finally reaching the target point D, thus realizing automatic pathfinding in three-dimensional space.

[0218] Example 2:

[0219] This embodiment provides a hardware-in-the-loop simulation method for a nuclear industry power robot, which includes the following steps:

[0220] Step A0: As Figure 1 , Figure 5 and Figure 6 As shown, a virtual-real hybrid platform is pre-constructed, which serves as a semi-physical simulation platform for nuclear industry power robotic arms;

[0221] as well as,

[0222] Define and configure the operability of the nuclear industry powered robot by pre-defining its parameters;

[0223] The hardware-in-the-loop simulation platform for nuclear industry power robots includes a virtual control unit and a field operation unit.

[0224] The virtual control unit includes an algorithm module, a DSP control module, a monitoring module, a virtual button module, and a display interface module. The algorithm module is used to obtain the motion path of the nuclear industry power robot based on the three-dimensional space automatic pathfinding method. The DSP control module is used to output corresponding control signals and control parameters to the control box based on the input control information. The monitoring module is used to monitor the control box. The virtual button module is used to simulate the operation of the nuclear industry power robot. The display interface module is used to display the control information of the DSP control module, the monitoring information of the monitoring module, and the virtual buttons of the virtual button module.

[0225] The field operation unit includes physical buttons on the control box. There is a one-to-one mapping relationship between physical buttons and virtual buttons. When the physical buttons are operated according to the parameter definition and operability configuration of the nuclear power robot, the working state of the virtual button corresponding to the physical button is configured to be active. When the physical button is not operated, the working state of the corresponding virtual button is configured to be inactive.

[0226] According to step A0, a semi-physical simulation platform for nuclear industry powered robotic arms, identical in size and layout to the actual equipment, is provided. This eliminates the feeling of unfamiliarity during training on the real equipment. Using the semi-physical simulation platform allows for a better understanding and mastery of the equipment's operation process, and displays operational prompts or error warnings within the simulation platform without posing any danger.

[0227] like Figure 1 and Figure 7 As shown, the system architecture of this nuclear industry power manipulator hardware-in-the-loop simulation platform consists of four parts: a graphical user interface (GUI), a simulation control program, a data interface program (API), and a physical control console. The GUI allows for kinematic interactive control, real-time status monitoring, and virtual teaching of the virtual power manipulator model. The simulation control program enables manipulator model state initialization, scene model loading and display, 3D automatic pathfinding calculation, and background forward and inverse kinematics calculations. The API facilitates communication between the simulation platform and the physical control console, allowing operation of the manipulator model in the virtual scene, recording the operation process, and analyzing the data to meet the needs of power manipulator operation training. The physical control console employs a two-level control mechanism based on a CAN bus. The upper-level computer is a PC, while the lower-level computer includes a DSP control board, a universal joystick, and a data acquisition box. Joystick data acquisition and power manipulator model control algorithms are implemented on the physical control console.

[0228] The system functions of this hardware-in-the-loop simulation platform for nuclear industry power robots include:

[0229] (1) Customization of device parameters

[0230] Based on the motion constraints and motion patterns of each part of the powered manipulator, a kinematic model matching the powered manipulator is established to realize the parameterization definition function of each part of the powered manipulator. This includes defining the motion parameters of the equipment, the motion sequence of the operating units, and the constraint parameters. Specific functions include:

[0231] Customize the parameters of the operating unit of the power robot;

[0232] It allows for operations such as adding, deleting, modifying, and searching of operation units;

[0233] The association relationships between operation units can be set;

[0234] The parameters of the operating unit are set according to the kinematic model of the power manipulator, including: equipment parameters, motion parameters, motion sequence, constraint parameters, spatial parameters, etc.

[0235] Constraint parameters can control the degrees of freedom of equipment movement;

[0236] Based on the kinematic model of the power robot, the movement of each operating unit is driven.

[0237] (2) Robotic arm three-dimensional spatial pathfinding

[0238] Based on custom parameters and the kinematic model data of the device, a three-dimensional spatial pathfinding method is developed (see Example 1 for the specific steps of the three-dimensional spatial pathfinding method). The spatial coordinates and attitudes of each unit of the power manipulator in the relative coordinate system are calculated, and the device's operating space analysis, operating route analysis, and collision avoidance are completed. Specific functions include:

[0239] Set critical path point: You can set the start coordinates, critical path point, and end coordinates of the path;

[0240] Set the spatial pathfinding method calculation step size: The calculation step size is designed to ensure that the automatic pathfinding analysis time within a 10-meter space does not exceed 1 hour;

[0241] Spatial pathfinding function: Calculates the optimal path based on the equipment, critical path points, and spatial pathfinding method;

[0242] Collision detection function: When navigating a technical space path, the robot can determine whether it will collide with surrounding objects based on the shape of the powered robot.

[0243] (3) Robotic arm operability design

[0244] By combining the various tools and equipment used in the operation, as well as key information such as the movement speed, acceleration, and limiting torque of the power manipulator, the operability simulation of the operation at the operation point is performed. The specific functions are as follows:

[0245] Operating unit configuration: The power robot mainly consists of a gripper and a motion mechanism. The gripper is the component used to grasp the workpiece, and the motion mechanism enables the hand to perform various rotations (swings), movements, or combined movements to achieve the specified actions and change the position and posture of the grasped object;

[0246] Degrees of freedom configuration: The independent motion modes of a motion mechanism, such as lifting, extension, and rotation, are called the degrees of freedom of a powered manipulator. Degrees of freedom are a key parameter in the design of powered manipulators. Generally, dedicated powered manipulators have 2 to 3 degrees of freedom.

[0247] Grip coordinate system configuration: This indicates the position and orientation of the gripper in the power manipulator coordinate system;

[0248] Base coordinate system configuration: It is the common reference coordinate system for all moving links and grippers of the power robot;

[0249] Link coordinate system configuration: It is a coordinate system fixed on each moving link of the power robot, which moves with the movement of the link;

[0250] Absolute coordinate system configuration: The coordinate system references the ground at the work site, which is the common reference coordinate system for all components of the power robot.

[0251] (4) Training Options Interface Design

[0252] The training interface for the powered robotic arm is integrated and designed to maintain consistency with the functional elements of the actual control software interface, such as... Figure 2 and Figure 3 As shown. Specific content includes: console control status connection; position and speed settings for nine axes of the power robot: trolley, carriage, sleeve extension, sleeve rotation, shoulder, upper arm, forearm, wrist extension, wrist rotation, and gripping; operation mode settings; gripping torque settings; trajectory teaching, etc.

[0253] (5) Multi-view display function

[0254] In the 3D scene of maintenance and repair operations, the upper left, lower left, and upper right views represent the images captured by three cameras on the powered robotic arm, while the lower right view is a free-view perspective. Figure 4 As shown. Users can control the rotation and zoom of the camera view, enable full-screen display of split viewpoints, and switch between different camera views inside the factory by clicking.

[0255] Step A1: Obtain monitoring information for monitoring the control box; and obtain the motion path of the nuclear industry power robot during movement according to the three-dimensional space automatic path finding method of Embodiment 1;

[0256] Step A2: The monitoring information and the motion path of the nuclear industry power robot during its movement are sent to the DSP control board as input control information;

[0257] Step A3: Based on the input control information, the DSP control board outputs corresponding control signals and control parameters to the control box;

[0258] Specifically, the input control information consists of monitoring information and the motion path of the nuclear industry power robot during its movement. This input control information is sent to the DSP control board of the hardware-in-the-loop simulation platform for the nuclear industry power robot. Based on the input control information, the DSP control board outputs corresponding control signals and control parameters to the control box of the hardware-in-the-loop simulation platform for the nuclear industry power robot.

[0259] Step A4: The control box controls the simulation operation of the nuclear industry power robot according to the control signals and control parameters output by the DSP control board, including collision detection and reverse drive, to complete the semi-physical simulation of the nuclear industry power robot.

[0260] The collision detection and reverse drive process is as follows: When the simulation of the nuclear power robot is running, if the running distance of the nuclear power robot is detected to be greater than the preset threshold, the nuclear power robot is stopped from its current drive operation and driven to run in reverse for a certain distance, which is the preset safety distance.

[0261] Example 3:

[0262] This embodiment provides a three-dimensional spatial automatic pathfinding device, which includes:

[0263] Determining unit, used to determine the starting point S(X) in three-dimensional space. s Y s Z s ), target point D(X) d Y d Z d ), and the intermediate point P(X) d Y d Z s The three-dimensional space includes the first plane (X) where the starting point is located. s Y s The second plane (Y) where the midpoint is located d Z d );

[0264] Wherein, the first plane is the XY plane, and the second plane is the YZ plane, perpendicular to the first plane; the starting point S(X s Y s Z s ) and the intermediate point P(X) d Y d Z s The target point D(X) has the same Z-axis distance. d Y d Z d ) and the intermediate point P(X) d Y d Z s They have the same distance in the X direction and the same distance in the Y direction;

[0265] The first search unit is used to search for the starting point S(X) on the first plane. s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path;

[0266] The first search unit includes:

[0267] The first grid segmentation module, connected to the determining unit, is used to segment data based on the starting point S(X). s Y s Z s ) and the intermediate point P(X) d Y d Z s The first plane is divided into grids based on the distance of ), resulting in the first two-dimensional grid;

[0268] A first storage module, connected to the first grid segmentation module, is used to store a first Close list; the first Close list includes a first plane (X). s Y s Points that have already been processed;

[0269] The second storage module, connected to the first grid segmentation module, is used to store the first Open list; the first Open list includes the first plane (X). s Y s Unprocessed points on the surface;

[0270] The first searching module, connected to the first grid segmentation module, the first storage module, and the second storage module respectively, is used to find the total consumption F. n The smallest parent node, where the parent node is the middle point P(X) added last to the first Close list. d Y d Z s Starting from point S(X), the algorithm backtracks step by step to the first parent node to form a path on the first plane starting from the starting point S(X). s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path, with the first parent node being the starting point S(X). s Y s Z s The second parent node is the starting point S(X). s Y s Z s The cell with the lowest total consumption among the cells reachable in the first two-dimensional grid is the next parent node, and so on.

[0271] Total consumption F of the square n The calculation formula is as follows:

[0272] F n =H n +Gn ;

[0273] G n H represents the cost of moving from the nth square to the (n+1)th square. n This indicates moving from the (n+1)th square to the midpoint P(X). d Y d Z s The estimated consumption of the n+1th square is the total consumption F in the squares that the nth square can reach in the first two-dimensional grid. n The lowest square;

[0274] The second search unit is used to find the intermediate point P(X) on the second plane. d Y d Z s ) to target point D(X) d Y d Z d The optimal path, based on the starting point S(X) s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path and intermediate point P(X) d Y d Z s ) to target point D(X) d Y d Z d The optimal path to obtain the starting point S(X) s Y s Z s ) to target point D(X) d Y d Z d The optimal path is found, thus enabling automatic pathfinding in three-dimensional space.

[0275] The second search unit includes:

[0276] The second grid segmentation module, connected to the determining unit, is used to segment data based on the intermediate point P(X). d Y d Z s ) and target point D(X) d Y d Z d The second plane is divided into grids based on the distance of ), resulting in a second two-dimensional grid;

[0277] The third storage module, connected to the second grid segmentation module, is used to store the second Close list; the second Close list includes the second plane (Y). dZ d Points that have already been processed;

[0278] The fourth storage module, connected to the second grid segmentation module, is used to store the second Open list; the first Open list includes the second plane (Y). d Z d Unprocessed points on the surface;

[0279] The second searching module, connected to the second grid segmentation module, the third storage module, and the fourth storage module respectively, is used to find the total consumption F. z The smallest of all parent nodes, the parent node is the target point D(X) that was last added to the second Close list. d Y d Z d Starting from point X, the algorithm backtracks step by step to the first parent node to form a path on the second plane at the intermediate point P(X). d Y d Z s ) to target point D(X) d Y d Z d The optimal path is where the first mother node is the intermediate point P(X). d Y d Z s The second mother node is the intermediate point P(X). d Y d Z s The next parent node is the cell with the lowest total consumption among the cells reachable from the previous parent node in the second two-dimensional grid.

[0280] Total consumption F of the grid z The calculation formula is as follows:

[0281] F z =H z +G z ;

[0282] G z H represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. z This indicates moving from the (m+1)th grid cell to the middle point P(X). d Y d Z s The estimated consumption of the m+1th grid is the total consumption F in the grids that the mth grid can reach in the first two-dimensional grid. z The lowest grid.

[0283] Example 4:

[0284] like Figure 1 As shown, this embodiment provides a hardware-in-the-loop simulation platform for a nuclear industry power robot:

[0285] 1. First, a study was conducted on the actual situation of nuclear industry power robotic arms.

[0286] 2. Construction of a Semi-physical Simulation Platform for Nuclear Industry Power Robots

[0287] Based on the actual conditions of nuclear industry power manipulators, a semi-physical simulation platform for nuclear industry power manipulators with the same size and layout as the real equipment is provided.

[0288] The platform specifically includes:

[0289] The host PC includes a determining unit, a first searching unit, a second searching unit, and a monitoring unit.

[0290] Determining unit, used to determine the starting point S(X) in three-dimensional space. s Y s Z s ), target point D(X) d Y d Z d ), and the intermediate point P(X) d Y d Z s The three-dimensional space includes the first plane (X) where the starting point is located. s Y s The second plane (Y) where the midpoint is located d Z d );

[0291] Wherein, the first plane is the XY plane, and the second plane is the YZ plane, perpendicular to the first plane; the starting point S(X s Y s Z s ) and the intermediate point P(X) d Y d Z s The target point D(X) has the same Z-axis distance. d Y d Z d ) and the intermediate point P(X) d Y d Z s They have the same distance in the X direction and the same distance in the Y direction;

[0292] The first search unit, connected to the determining unit, is used to search for the starting point S(X) on the first plane. s Ys Z s ) to the midpoint P(X) d Y d Z s The optimal path;

[0293] The second search unit is used to find the intermediate point P(X) on the second plane. d Y d Z s ) to target point D(X) d Y d Z d The optimal path, based on the starting point S(X) s Y s Z s ) to the midpoint P(X) d Y d Z s The optimal path and intermediate point P(X) d Y d Z s ) to target point D(X) d Y d Z d The optimal path to obtain the starting point S(X) s Y s Z s ) to target point D(X) d Y d Z d The optimal path is found, thus enabling automatic pathfinding in three-dimensional space.

[0294] The monitoring unit is used to monitor the operation of the control box;

[0295] The DSP control board is connected to the host PC via a CAN bus and is used to output corresponding control signals and control parameters to the control box based on the input control information from the second lookup unit and the monitoring unit.

[0296] The control box, connected to the host PC via a CAN bus, is used to simulate the set actions of the nuclear industry power robot based on the control signals and control parameters output by the DSP control board, thereby completing the semi-physical simulation of the nuclear industry power robot.

[0297] Specifically, the platform also includes a construction unit, which is used to pre-build a virtual-real hybrid platform, which is a semi-physical simulation platform for nuclear industry power robotic arms;

[0298] The hardware-in-the-loop simulation platform for nuclear industry power robots includes a virtual control unit and a field operation unit.

[0299] The virtual control unit is a PC-based system comprising an algorithm module, a DSP control module, a monitoring module, a virtual button module, and a display interface module. The algorithm module is used to obtain the motion path of the nuclear industry power robot based on the three-dimensional space automatic pathfinding method. The monitoring module is used to monitor the control box. The virtual button module is used to simulate the operation of the nuclear industry power robot. The display interface module is used to display the control information of the DSP control module, the monitoring information of the monitoring module, and the virtual buttons of the virtual button module.

[0300] The field operation unit includes physical buttons on the control box. There is a one-to-one mapping relationship between physical buttons and virtual buttons. When a physical button is pressed, the corresponding virtual button is configured to be active. When a physical button is not pressed, the corresponding virtual button is configured to be inactive.

[0301] Specifically, such as Figure 5 and Figure 6 As shown, the control box includes an interconnected data acquisition box and operating components. The data acquisition box collects user input information from the operating components to enable the DSP control board to output corresponding control signals and parameters. The operating components include a universal joystick, a software emergency stop button, a buzzer, a power indicator, a fault indicator, and an enable indicator.

[0302] A universal drive joystick is used to precisely control the movement of nuclear industry power robotic arms;

[0303] The software emergency stop button is used to stop the software from running in case of an malfunction in the host PC.

[0304] Buzzers are often used to emit alert or alarm sounds;

[0305] The power indicator light is used to indicate the power supply status. When the power is on, the power indicator light is on; when the power is not on, the power indicator light is off.

[0306] Fault indicator lights are used to indicate faults or abnormalities;

[0307] The enable indicator light is used to indicate that the operation or function of the nuclear industry's powered robotic arm is normal.

[0308] 3. Simulation Implementation

[0309] Based on the three-dimensional spatial pathfinding method, the simulation path in the three-dimensional interactive simulation scene is obtained; according to the simulation path, the simulation is carried out in the virtual-real combination platform to realize the semi-physical simulation of the nuclear industry power robot.

[0310] 4. The operation method of the simulation system in the simulation platform is as follows:

[0311] 4.1 Login Function

[0312] Username and password are the credentials for users to use the platform;

[0313] Users log in to the system using a username and password, for example, the default username and password are root and root. If a new user needs to be added, the administrator must add the new username and password to the gs_user table in the database.

[0314] 4.2 Operation of the Control Panel for the Powered Robotic Arm

[0315] The operation control display panel of the powered robot can display the position, speed and current feedback of each joint of the powered robot, display the trajectory information of the powered robot, and allow for fine adjustment of the position of each joint.

[0316] 4.2.1 Setting up the control panel for the robotic arm joints

[0317] The powered robotic arm displays operational information for a total of 10 joints: main carriage, auxiliary carriage, sleeve extension / retraction, sleeve rotation, shoulder, upper arm, forearm, wrist extension / retraction, wrist rotation, and gripping. Each joint displays the direction of movement, motor, position feedback, speed feedback, speed setting slider, zero-finding button, enable button, and jog button.

[0318] 4.2.2 Setting the speed indicator and current indicator

[0319] Speed ​​indicator: Displays the operating speed of the corresponding joint of the powered robotic arm; Current indicator: Displays the operating current of the corresponding joint of the powered robotic arm;

[0320] Clamping torque setting

[0321] The clamping torque of the power robot can be modified by adjusting the slider.

[0322] Operating mode

[0323] 4.2.3 Setting the operating mode:

[0324] Console operations;

[0325] Automatic pathfinding.

[0326] 4.2.4 Setting the Trajectory Teaching Panel

[0327] Displays the track record, showing the track points and detailed information for each track; allows selection of whether to loop the track and sets the number of loops; includes track operation buttons and an automatic pathfinding function button.

[0328] 4.3 Console Operations

[0329] 4.3.1 Multi-view display

[0330] In the robotic arm operation scenario, the top left, bottom left, and top right screens represent the images captured by three cameras on the powered robotic arm, respectively. The bottom right screen is a free-view screen, allowing the user to control the camera's perspective. Users can double-click on any screen to display it in full screen. Furthermore, the free-view screen contains a camera list where cameras in the scene can be added, and clicking on a camera allows switching between them.

[0331] 4.3.2 Operation of Power Machinery

[0332] Users can control the powered robotic arm via a control panel or keyboard.

[0333] 4.3.3 Trajectory Recording and Operation

[0334] Track recording

[0335] Users can create a new trajectory by clicking the "Create Trajectory" button, and then control the powered robotic arm through the console or keyboard. Each time the trajectory is run, the corresponding trajectory point information will be recorded in the trajectory point panel.

[0336] Users can modify and delete trajectory points, and click on a selected point to view detailed information in the trajectory point details panel.

[0337] trajectory running

[0338] After saving the trajectory, the user can click the "Run" button, and the powered robotic arm will run along the trajectory points. The user can set whether to run the trajectory repeatedly and set the number of cycles.

[0339] 4.4 Verification of Automatic Route Finding Function

[0340] 4.4.1 Determine the target point

[0341] The gripper of the powered robotic arm produces a small ball, which the user can drag to the desired target point and then click a button to confirm.

[0342] 4.4.2 Automatic Pathfinding

[0343] After the user selects the target point, clicking the button begins the motion hand to detect the path from the current position to the target position and the required pose changes for each joint. A slider will appear at the bottom of the scene to display the detection progress.

[0344] When the progress bar shows "Completed," it means that the power arm has detected a path that it can traverse. The power arm then begins to move to the target point according to the detected and recorded path and the posture changes required for each joint at each path point.

[0345] When the progress bar shows "Pathfinding Failed," it means that the power arm has not found a suitable path to reach the target point.

[0346] Examples 2, 3, and 4 are all interactive training systems based on three-dimensional space automatic pathfinding methods, which combine intelligent rigid bodies that form power manipulator models with physical control console operations.

[0347] Examples 2, 3, and 4 all include collision detection and reverse drive. When the running distance of the nuclear industry power robot is detected to be greater than a preset threshold, the nuclear industry power robot is controlled to stop its current drive operation and is driven to run in reverse for a certain distance. This certain distance is a preset safety distance, which ensures system safety.

[0348] Examples 2, 3, and 4 can more realistically reflect the interaction between nuclear industry powered robots and the impact of the external environment on nuclear industry powered robots. They can also be used to test, verify, and improve existing procedures and processes to reduce errors and improve products or industrial processes. Furthermore, they can enable trainees to better understand and experience relevant knowledge and skills, allowing them to practice effectively in a more realistic environment, thereby accumulating practical experience, improving operational skills, and enhancing their confidence in real-world applications.

[0349] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A three-dimensional automatic pathfinding method, characterized in that, The method is used for power robotic arms in the nuclear industry, and the method includes the following steps: Step S1: Determine the starting point S in three-dimensional space ), Target point D ), and the midpoint P The three-dimensional space includes the first plane where the starting point is located. The second plane where the midpoint is located ); The first plane is the XY plane, and the second plane is the YZ plane, perpendicular to the first plane; the starting point S ) and the intermediate point P The target point D has the same Z-axis distance. ) and the intermediate point P They have the same distance in the X direction and the same distance in the Y direction; Step S2: Locate the starting point S on the first plane. ) to the midpoint P The optimal path; Step S3: Find the midpoint P on the second plane ) to target point D The optimal path, based on the starting point S ) to the midpoint P The optimal path and intermediate point P) ) to target point D The optimal path is obtained from the starting point S. ) to target point D The optimal path is found, thus enabling automatic pathfinding in three-dimensional space. The three-dimensional space also includes the third plane where the target point is located. ), third plane ) is the XY plane, and is adjacent to the first plane )parallel, Step S3 is followed by step S4: Step S4: Confirm the target point D on the third plane. If there is no deviation, then the target point D... No correction is performed; if there is a deviation, then the target point D is recalibrated on the third plane. Automatic pathfinding deviation correction is performed. The target point D on the third plane The automatic pathfinding deviation correction includes the following steps: Step S41: Confirm the target point D ) and target point E The target point is the final destination point. Step S42: Based on target point D ) and target point E The third plane is divided into grids to obtain the third three-dimensional grid. Step S43: Move the target point D Add it to the third Close list and set the target point D. As the first node, find the starting point and target point D. The squares that can be reached in the third-dimensional grid; Step S44: Move the target point D The reachable squares in the third 2D grid are added to the third Open list, and the target point D is calculated. Total consumption , ； This represents the movement cost from the starting point s to the first grid. This indicates moving from the first grid to E. The estimated consumption is calculated, with the first grid cell representing the target point D. The total consumption in the reachable squares within the third-dimensional grid. The lowest square; S45: Add the first grid to the third Close list, and use the first grid as the second top node, and find the grids that the first grid can reach in the third 2D grid; Find the grids that the first grid can reach in the third 2D grid, and exclude the squares in the third Close list; Step S46: Add the grids reachable by the first grid in the first 2D grid to the third Open list, and calculate the total consumption of the first grid. , ； This represents the movement cost from the k-th grid to the (k+1)-th grid. This indicates moving from the (k+1)th grid to the midpoint P. The estimated consumption of the n+1 grid is the total consumption of the grid that the nth grid can reach in the first two-dimensional grid. The lowest grid; Step S47: Continue in this manner, repeating steps S45 and S46 until the target point E is reached. It has been added to the third Close list; Step S48: From the last point E added to the third Close list Starting from point D, the algorithm backtracks step by step to the first parent node by finding the parent node, in order to form a path on the third plane leading to the target point D. ) to target point E The correction path.

2. The three-dimensional automatic pathfinding method according to claim 1, characterized in that, Step S2 specifically includes: Step S21: Based on the starting point S and the midpoint P The first plane is divided into grids based on the distance of ), resulting in the first two-dimensional grid; Step S22: Set the starting point S Add it to the first Close list and set the starting point S. As the first parent node, find the starting point S. The squares that can be reached in the first two-dimensional grid; Step S23: Set the starting point S The squares reachable in the first two-dimensional grid are added to the first Open list, and the total consumption at the starting point is calculated. , ； This represents the movement cost from the starting point 's' to the first square. This indicates moving from the first square to the middle point P. The estimated consumption is calculated, with the first square being the starting point S. The total consumption in the reachable squares within the first two-dimensional grid. The lowest square; Step S24: Add the first square to the first Close list, and set the first square as the second parent node, and find the squares that the first square can reach in the first two-dimensional grid; exclude the squares that the first square can reach in the first two-dimensional grid from the first Close list. Step S25: Add the cells reachable from the first cell in the first two-dimensional grid to the first Open list, and calculate the total consumption of the first cell. , ； This represents the movement cost from the nth square to the (n+1)th square. This indicates moving from the (n+1)th square to the midpoint P. The estimated consumption of the n+1th square is the total consumption of the squares reachable from the nth square in the first two-dimensional grid. The lowest square; Step S26: Continue in this manner, repeating steps S24 and S25 until the intermediate point P is reached. It has been added to the first Close list; Step S27: From the midpoint P of the last item added to the first Close list Starting from point S, the process backtracks step by step to the first parent node by finding the parent node, in order to form a path on the first plane starting from the first parent node. ) to the midpoint P The optimal path.

3. The three-dimensional automatic pathfinding method according to claim 1, characterized in that, In step S3, the intermediate point P is found on the second plane. ) to target point D The optimal path, specifically including the following steps: Step S31: Based on the intermediate point P and target point D The second plane is divided into grids based on the distance of ), resulting in a second two-dimensional grid; Step S32: Set the midpoint P Add it to the second Close list, and set the middle point P. Using the first parent node as the starting point, find the intermediate point P. The squares that can be reached in the second two-dimensional grid; Step S33: Place the midpoint P The cells reachable in the second 2D grid are added to the second Open list, and the total consumption is calculated. , ； This represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. This indicates moving from the (m+1)th grid cell to the middle point P. The estimated consumption of the m+1th grid is the total consumption of the m-th grid within the grids reachable from the first two-dimensional grid. The lowest grid cell; Step S34: Add the first grid cell to the second Close list, and use the first grid cell as the second parent node. Find the grid cells that the first grid cell can reach in the second two-dimensional grid. Exclude the grid cells that the first grid cell can reach in the second two-dimensional grid from the second Close list. Step S35: Add the grids reachable from the first grid in the second two-dimensional grid to the second Open list, and calculate the total consumption of the first grid. , ； This represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. This indicates moving from the (m+1)th grid cell to the middle point P. The estimated consumption of the m+1th grid is the total consumption of the m-th grid within the grids reachable from the first two-dimensional grid. The lowest grid cell; Step S36: Continue in this manner, repeating steps S34 and S35 until the intermediate point P is reached. It has been added to the second Close list; Step S37: From the target point D that was last added to the second Close list By searching for the parent node, we backtrack step by step to the first parent node, in order to form a path on the second plane with the intermediate point P. ) to target point D The optimal path.

4. A hardware-in-the-loop simulation method for nuclear industry power robotic arms, characterized in that, The method includes the following steps: Step A1: Obtain monitoring information for monitoring the control box; and, according to any one of claims 1 to 3, obtain the motion path of the nuclear industry power robot during its movement. Step A2: Use the monitoring information and the motion path of the nuclear industry power robot as input control information to control the simulation operation of the nuclear industry power robot, so as to complete the semi-physical simulation of the nuclear industry power robot.

5. The hardware-in-the-loop simulation method according to claim 4, characterized in that, Step A2 specifically includes: Step A21: The monitoring information and the motion path of the nuclear industry power robot during its movement are transmitted to the DSP control board as input control information; Step A22: Based on the input control information, the DSP control board outputs corresponding control signals and control parameters to the control box; Step A23: The control box controls the simulation operation of the nuclear industry power robot according to the control signals and control parameters output by the DSP control board, so as to complete the semi-physical simulation of the nuclear industry power robot.

6. The hardware-in-the-loop simulation method according to claim 5, characterized in that, Step A23 further includes: When the simulation operation of the nuclear industry powered robot is running, if the running distance of the nuclear industry powered robot is detected to be greater than a preset threshold, the nuclear industry powered robot will stop its current driving operation and be driven to run in the opposite direction for a certain distance, which is a preset safety distance.

7. The hardware-in-the-loop simulation method according to claim 4, characterized in that, Step A0 is included before step A1. A0: A virtual-real hybrid platform is pre-constructed, which is a semi-physical simulation platform for nuclear industry power robotic arms; as well as, Predefine the parameters of the nuclear industry powered robot and configure its operability; Step A2 specifically involves transmitting the monitoring information and the motion path of the nuclear industry power robot as input control information to the DSP control board of the hardware-in-the-loop simulation platform of the nuclear industry power robot. as well as, Based on the input control information, the DSP control board outputs corresponding control signals and control parameters to the control box of the hardware-in-the-loop simulation platform for the nuclear industry power robot.

8. The hardware-in-the-loop simulation method according to claim 7, characterized in that, The predefined parameters for the nuclear industry powered robotic arm specifically include: The parameters of the operating unit of the nuclear industry power robot are customized. And / or, This allows for the addition, deletion, modification, and search operations of the operating units of nuclear industry power robotic arms. And / or, Establish the relationships between the operating units of the nuclear industry's powered robotic arm. And / or, Based on the kinematic model of the nuclear industry power manipulator, the operating unit of the nuclear industry power manipulator is configured with equipment parameters, motion parameters, motion sequence, constraint parameters, and spatial parameters. And / or, Determine the degrees of freedom for the movement of the nuclear industry's powered robotic arm. And / or, The operating unit of the nuclear industry power robot is set to move according to the three-dimensional space automatic path finding method; The operability of the nuclear industry powered robotic arm specifically includes: Configuration of operating unit, degree of freedom, clamp coordinate system, base coordinate system, link coordinate system, and absolute coordinate system.

9. A three-dimensional automatic pathfinding device, characterized in that, The apparatus is used in the three-dimensional automatic pathfinding method according to any one of claims 1-3, the apparatus comprising: Determining unit, used to determine the starting point S in three-dimensional space. ), Target point D ), and the midpoint P The three-dimensional space includes the first plane where the starting point is located. The second plane where the midpoint is located ); Wherein, the first plane is the XY plane, the second plane is the YZ plane and is perpendicular to the first plane; the starting point S ) and the intermediate point P The target point D has the same Z-axis distance. ) and the intermediate point P They have the same distance in the X direction and the same distance in the Y direction; The first search unit, connected to the determining unit, is used to search for the starting point S on the first plane. ) to the midpoint P The optimal path; The second search unit, connected to both the determining unit and the first search unit, is used to search for the intermediate point P on the second plane. ) to target point D The optimal path, based on the starting point S ) to the midpoint P The optimal path and intermediate point P) ) to target point D The optimal path is obtained from the starting point S. ) to target point D The optimal path is found, thus enabling automatic pathfinding in three-dimensional space.

10. The three-dimensional automatic pathfinding device according to claim 9, characterized in that, The first search unit includes: The first grid segmentation module, connected to the determining unit, is used to segment data based on the starting point S. and the midpoint P The first plane is divided into grids based on the distance of ), resulting in the first two-dimensional grid; A first storage module, connected to the first grid segmentation module, is used to store a first Close list; the first Close list includes a first plane. Points that have already been processed; The second storage module, connected to the first grid segmentation module, is used to store the first Open list; the first Open list includes a first plane. Unprocessed points on the surface; The first searching module is connected to the first grid segmentation module, the first storage module, and the second storage module, respectively, and is used to find the total consumption. The smallest parent node, where the parent node is the midpoint P that was last added to the first Close list. Starting from point S, the algorithm backtracks step by step to the first parent node to form a path on the first plane. ) to the midpoint P The optimal path, with the first parent node being the starting point S. The second parent node is the starting point S. The cell with the lowest total consumption among the cells reachable in the first two-dimensional grid is the next parent node, and so on. Total consumption of squares The calculation formula is as follows: ； This represents the movement cost from the nth square to the (n+1)th square. This indicates moving from the (n+1)th square to the midpoint P. The estimated consumption of the n+1th square is the total consumption of the squares reachable from the nth square in the first two-dimensional grid. The lowest square; The second search unit includes: The second grid segmentation module, connected to the determining unit, is used to segment data based on the intermediate point P. and target point D The second plane is divided into grids based on the distance of ), resulting in a second two-dimensional grid; The third storage module, connected to the second grid segmentation module, is used to store the second Close list; the second Close list includes the second plane. Points that have already been processed; The fourth storage module, connected to the second grid segmentation module, is used to store the second Open list; the first Open list includes the second plane. Unprocessed points on the surface; The second searching module, connected to the second grid segmentation module, the third storage module, and the fourth storage module respectively, is used to find the total consumption. The smallest of all parent nodes, the parent node is the target point D that was last added to the second Close list. Starting from point P, the algorithm backtracks step by step to the first parent node to form a path on the second plane that ends at the intermediate point P. ) to target point D The optimal path is where the first mother node is the intermediate point P. The second mother node is the intermediate point P. The next parent node is the cell with the lowest total consumption among the cells reachable from the previous parent node in the second two-dimensional grid. Total consumption of the grid The calculation formula is as follows: ； This represents the movement cost from the m-th grid cell to the (m+1)-th grid cell. This indicates moving from the (m+1)th grid cell to the middle point P. The estimated consumption of the m+1th grid is the total consumption of the m-th grid within the grids reachable from the first two-dimensional grid. The lowest grid.

11. A hardware-in-the-loop simulation platform for nuclear industry power robotic arms, characterized in that, include: The host PC includes the three-dimensional automatic pathfinding device as described in claim 9 or 10 and a monitoring unit, wherein the three-dimensional automatic pathfinding device includes a determining unit, a first searching unit, and a second searching unit. The determining unit is used to determine the starting point S in three-dimensional space. ), Target point D ), and the midpoint P The three-dimensional space includes the first plane where the starting point is located. The second plane where the midpoint is located ); Wherein, the first plane is the XY plane, the second plane is the YZ plane and is perpendicular to the first plane; the starting point S ) and the intermediate point P The target point D has the same Z-axis distance. ) and the intermediate point P They have the same distance in the X direction and the same distance in the Y direction; The first search unit is connected to the determining unit and is used to search for the starting point S on the first plane. ) to the midpoint P The optimal path; The second search unit is connected to both the determining unit and the first search unit, and is used to find the midpoint P on the second plane. ) to target point D The optimal path, based on the starting point S ) to the midpoint P The optimal path and intermediate point P) ) to target point D The optimal path is obtained from the starting point S. ) to target point D The optimal path; The monitoring unit is used to monitor the operation of the control box; The DSP control board is connected to the host PC via a CAN bus and is used to output corresponding control signals and control parameters to the control box based on the input control information from the second lookup unit and the monitoring unit. The control box, connected to the host PC via a CAN bus, is used to simulate the set actions of the nuclear industry power robot based on the control signals and control parameters output by the DSP control board, thereby completing the semi-physical simulation of the nuclear industry power robot.

12. The hardware-in-the-loop simulation platform according to claim 11, characterized in that, The platform also includes: A construction unit is used to pre-build a virtual-real hybrid platform, which is a semi-physical simulation platform for nuclear industry power robotic arms. The hardware-in-the-loop simulation platform for nuclear industry power robots includes a virtual control unit and a field operation unit. The virtual control unit, based on a PC, includes an algorithm module, a DSP control module, a monitoring module, a virtual button module, and a display interface module. The algorithm module is used to obtain the motion path of the nuclear industry power robot based on a three-dimensional automatic pathfinding method. The monitoring module is used to monitor the control box. The virtual button module is used to simulate the operation of the nuclear industry power robot. The display interface module is used to display the control information of the DSP control module, the monitoring information of the monitoring module, and the virtual buttons of the virtual button module. The field operation unit includes physical buttons on the control box. There is a one-to-one mapping relationship between the physical buttons and the virtual buttons. When a physical button is pressed, the working state of the corresponding virtual button is configured to be active. When the physical button is not pressed, the working state of the corresponding virtual button is configured to be inactive.

13. The hardware-in-the-loop simulation platform according to claim 11, characterized in that, The control box includes: The data acquisition box and the operating component are interconnected. The data acquisition box is used to collect user input information from the operating component so that the DSP control board can output corresponding control signals and control parameters. The operating components include a universal drive joystick, a software emergency stop button, a buzzer, a power indicator light, a fault indicator light, and an enable indicator light; A universal drive joystick is used to precisely control the movement of nuclear industry power robotic arms; The software emergency stop button is used to stop the software from running in case of an malfunction in the host PC. Buzzers are often used to emit alert or alarm sounds; The power indicator light is used to indicate the power supply status. When the power is on, the power indicator light is on; when the power is not on, the power indicator light is off. Fault indicator lights are used to indicate faults or abnormalities; The enable indicator light is used to indicate that the operation or function of the nuclear industry's powered robotic arm is normal.

Images