An Optimal Path Planning and Scheduling System and Method for Robots

Abstract

The present invention discloses a robot optimal path planning and scheduling system and method. The method includes the steps of: S1, collecting three-dimensional coordinate parameters of the target node to be collected and detected currently and the state node of the current robotic arm through a camera; S2, traversing all paths from the state node of the current robotic arm to the target node through the rapidly-exploring random tree algorithm; S3, calculating the time values of each single path among all paths; S4, inputting the time values into a bubble program one by one to obtain the shortest time data value; S7, determining the shortest path of the robotic arm operation among multiple shortest time data values and outputting it as the optimal path required for the robotic arm to operate; S8, the robotic arm receiving the optimal path from the state node of the current robotic arm to the target node and controlling the corresponding joints to execute the operation parameters of the optimal path in sequence. The present invention realizes the function that the path planning of multiple target nodes corresponding to the work of multiple robotic arms cannot be achieved by the dynamic obstacle avoidance path planning of a single robotic arm.

Description

Technical Field

[0001] The present invention belongs to the technical field of robots. Specifically, it relates to a robot optimal path planning and scheduling system and method. Background Art

[0002] During the biological sample detection process, the collected sample solution needs to be diluted before being detected one by one. This process requires the cooperation of a robotic arm to reduce the insecurity and inaccuracy of manual detection. The robotic arm usually reaches the designated coordinate points through rotation, telescoping, and swinging during the entire movement process.

[0003] In the Chinese invention patent with the patent number CN201811398581.3, a dynamic obstacle avoidance path planning method for a seven-degree-of-freedom redundant robotic arm based on a rapidly-exploring random tree is disclosed, including the following steps: Step 1: According to the position of the target point, use the analytical solution of the redundant robotic arm inverse kinematics to determine the optimal target state of the robotic arm; Step 2: Use the optimal target state as the target node to construct a search tree in the joint space and plan a collision-free path; Step 3: Select the first k nodes from the path planned in Step 2 as the current path and input it to the robotic arm, and then update the k-th node as the root node; Step 4: According to the change in the position of the obstacle, update the environmental map in real time and rewire the search tree; Step 5: If the target node in the joint space is blocked by an obstacle, recalculate the optimal target state in the current environment as the new target node. The method of solving the redundant robotic arm inverse kinematics using the analytical solution and then determining the target node by optimizing the objective function avoids the problem of the uncertainty of the target state of the redundant robotic arm; divides the planning process into two processes: offline planning and online planning, and solves the problem that it cannot be used for real-time obstacle avoidance of redundant robotic arms due to the excessively high computational complexity of random search; in the online planning stage, uses the expansion and rewiring of the search tree to achieve the purpose of avoiding dynamic obstacles. When the original target node is blocked by a dynamic obstacle and the target becomes unreachable, it can switch the target node and search for a new path.

[0004] The defect of the existing patent is that the above method realizes the dynamic obstacle avoidance path planning of a single robotic arm. However, for the multi-sample detection process that needs to consider the obstacle avoidance path planning, the multi-sample detection of the assembly line also needs to consider the running time cost of the collection robotic arm reaching the target node. In the prior art, there is a lack of an optimal time path planning and scheduling method for multiple robotic arms and multiple target nodes. Summary of the Invention

[0005] Aiming at the problem that the existing robotic arm dynamic obstacle avoidance path planning scheme lacks an optimal time path planning and scheduling method for multiple robotic arms and multiple target nodes, the present invention provides a robot optimal path planning and scheduling system and method.

[0006] To achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0007] A robot optimal path planning and scheduling system, comprising a sample coordinate acquisition module, a robotic arm initial state acquisition module, a restricted barrier acquisition module, a path traversal module, a path movement times calculation module, a path time value calculation module, a module for determining the path with the shortest time, an optimal path storage module, and an optimal path execution module;

[0008] The sample coordinate acquisition module acquires the position of the target node that the current robotic arm needs to collect and detect through a camera; to achieve this action, it can be determined whether a test tube or other container storing the sample coordinates to be detected has fallen into the detection port through a proximity sensor, and then further verified whether there is a test tube or other container in the detection port through the camera. The coordinates of the detection port are preset during initialization. As long as the proximity sensor determines which detection port it is, the coordinates of this detection port in the database can be called to obtain the coordinate parameters of the target node position to be collected and detected; when collecting the corresponding multiple target node positions to be collected and detected for multiple robotic arms, only the target node to be collected and detected that is closest to the three-dimensional coordinates of the state node of the current robotic arm needs to be corresponding to each robotic arm.

[0009] The robotic arm initial state acquisition module acquires the three-dimensional coordinate parameters of the state node of the robotic arm through a camera; to achieve this process, a horizontally arranged proximity sensor or position sensor is also used to detect the sample collection point at the front end of the robotic arm, and then an inductive sensor is used to detect whether the camera is powered on to further ensure that the robotic arm is in the initial unpowered and tightened state; finally, the image collected by the camera is used to verify that the camera is in the initial non-operating state, and the three-dimensional coordinate parameters of the node are unified as the initial origin coordinate parameters.

[0010] The restricted barrier acquisition module is used to acquire the three-dimensional coordinate parameters of the running barrier nodes of the current robotic arm as the three-dimensional coordinates of the restricted traversal nodes for path traversal; if there are two robotic arms, the mid-axis plane of the two robotic arms is the three-dimensional plane of the restricted traversal nodes of the two robotic arms; by setting the three-dimensional coordinates of the restricted traversal nodes during the traversal process of the robotic arm, not only the possibility of collision between the robotic arms during operation is avoided, but also the computational complexity and data volume of path traversal are reduced.

[0011] The path traversal module traverses all accessible node coordinates and paths at the front end of the current robotic arm using the rapidly-exploring random tree algorithm, and transmits all accessible node coordinates and paths to the path movement times calculation module;

[0012] The path movement times calculation module decomposes all accessible node coordinates at the front end of the robotic arm for each single path to obtain the number of actions (rotation, telescoping, and swinging) of all joints of the robotic arm when completing the entire path;

[0013] The path time value calculation module multiplies the number of actions of each joint obtained by decomposition by the average time required for the robotic arm to complete the action, and then sums the times of all actions to obtain the total running time data value of a single path;

[0014] The module for judging the path with the shortest time judges the path with the shortest time among all paths through a bubble program, and takes it as the optimal path for the robotic arm to run;

[0015] The optimal path storage module is used to store the coordinates of all accessible nodes at the front end of the robotic arm of the optimal path, the number of actions of each joint, and the sequence of actions; it is convenient for the optimal path execution module to call the corresponding data to the robotic arm for execution; during the subsequent operation of the robotic arm, in order to prevent program errors, it is possible to verify whether each joint's action completely realizes the initially traversed optimal path through the coordinates of the accessible nodes. By using an image recognition algorithm to recognize whether the video frame image coordinates of the start and end time nodes of the robotic arm's action are consistent with the coordinates of all accessible nodes at the front end of the robotic arm of the optimal path when the robotic arm executes each action. If they are consistent, it means that there is no error in the process of the robotic arm executing the action. If they are inconsistent, a command for whether the robotic arm executes the action is issued.

[0016] The optimal path execution module is used to call the number of actions of each joint of the robotic arm and the sequence of actions in the optimal path storage module, and the robotic arm executes them in sequence.

[0017] Furthermore, the optimal path storage module also stores the name of the robotic arm, the position of the target node for acquisition and detection, the three-dimensional coordinate parameters of the state node of the robotic arm, the coordinates of all accessible nodes at the front end of the robotic arm, the number of actions of the joint, and the total running time data value of a single path. Through the name of the robotic arm and the position of the target node for acquisition and detection, the position of the robotic arm can be quickly located; the three-dimensional coordinate parameters of the state node of the robotic arm, the coordinates of all accessible nodes at the front end of the robotic arm, the number of actions of the joint, and the total running time data value of a single path can be used to mutually verify whether the optimal path planning of the robotic arm is reasonable.

[0018] Furthermore, it further includes a robotic arm operating system, and the robotic arm operating system includes a first conveyor mechanism, a first proximity sensor, a second conveyor mechanism, a second proximity sensor, multiple robotic arms, a biological sample detection port, a numerical converter, and a terminal controller;

[0019] The first conveyor mechanism is arranged directly below the robotic arm, a biological sample detection port is arranged above the first conveyor mechanism, and a first proximity sensor is arranged directly below the first conveyor mechanism;

[0020] The second conveyor mechanism is longitudinally arranged on the side of the robotic arm, and a second proximity sensor is arranged on the second conveyor mechanism;

[0021] The numerical converter receives the data of the optimal path execution module, converts it into electrical signals recognizable by the terminal controller, and the terminal controller connects to the joint points of multiple robotic arms through control lines and controls the movement of the joint points of the robotic arms through electrical signals.

[0022] Furthermore, the robotic arm includes at least one motion unit, which is successively connected to a rotating arm, a telescopic arm, and a swinging arm; the rotating arm controls the rotation angle of the entire robotic arm, the telescopic arm is connected to the rotating arm; the swinging arm is connected to the front end of the telescopic arm and can swing at multiple angles around the connection point. The total number of paths required for traversing the robotic arm can be further reduced by presetting the rotation angle of the rotating arm and the numerical value of each rotation angle. Similarly, the telescopic length of the telescopic arm and the swing angle of the swinging arm can also be preset to limit the data redundancy generated during traversal in the optimal path planning process. It also plays a certain role in preventing the collision and barrier between the actions of two robotic arms.

[0023] A method for optimal path planning and scheduling of a robot, including the steps of:

[0024] S1. Collect the three-dimensional coordinate parameters of the target node to be collected and detected currently and the state node of the current robotic arm through a camera;

[0025] S2. Traverse all paths from the state node of the current robotic arm to the target node through the rapidly-exploring random tree algorithm;

[0026] S3. Calculate the time values of each single path among all paths by multiplying the number of rotations, extensions, and swings of each action in a single path by the preset operation time of each action;

[0027] S4. Input the time values into the bubble program one by one to obtain the shortest time data value;

[0028] S5. Judge the number of shortest time data values. If it is 1, go to step S6; if it is greater than 1, go to step S7;

[0029] S6. Output the optimal path required for the operation of the robotic arm corresponding to the shortest time data value;

[0030] S7. Judge the shortest path of the robotic arm operation among multiple shortest time data values and output it as the optimal path required for the operation of the robotic arm;

[0031] S8. The robotic arm receives the optimal path from the state node of the current robotic arm to the target node and controls the corresponding joints to execute the operation parameters of the optimal path in sequence.

[0032] Furthermore, the detailed steps of step 2 include:

[0033] S201. First, initialize variables such as the starting point q init , the target point q goal , the path Path, the tree T, and the coordinates of the obstacle points (i.e., the evenly divided planes between the robotic arms and other planes in the space mapped by the bisecting plane, such as the four planes in the vertical direction corresponding to the robotic arms);

[0034] S202. Randomly sample to generate a node q new And perform a collision detection. If a collision occurs, enter the next iteration and resample; otherwise, proceed to the next step;

[0035] S203. Determine whether the generated q new node and q goal meet the pre - given constraints. If they do, go to S207; otherwise, go to S204;

[0036] S204. According to the generated q new situation, call the adaptive step - size strategy to adjust the step - size factor;

[0037] S205. According to the expansion - point selection strategy, expand a new node, and then perform a collision detection. If there is no collision, generate a new node q new Then go to S203 for judgment; otherwise, go to S206;

[0038] S206. At this time, determine whether the generated tree has fallen into a local minimum. If it has fallen into a local minimum, call the local escape algorithm to generate q new Then go to S203 for judgment. If it has not fallen into the minimum, go to S204;

[0039] S207. At this time, a random expansion tree that meets the system constraints has been generated. Call the Dijkstra algorithm to optimize the path. If the cost of the new path is smaller, update the path;

[0040] S208. Return the generated RRT tree and end.

[0041] The RRT algorithm plans an effective path for the end - effector of the robotic arm in the Cartesian space. However, when the end - effector of the robotic arm is moving along this effective path, the poses of other joint linkages also need to be considered.

[0042] Furthermore, by defining a weight coefficient for the rotation or swing angle, the telescopic amount of each joint of the robotic arm, and the numerical value of the rotatable or swingable angle, calculate the path cost based on this:

[0043]

[0044] Among them, ω i refers to the weight of each joint, Δθ iThe change amount of the finger joint; by substituting all the paths in step S208 into the above formula, the joint swing angle of the optimal target pose is obtained. i = 1 to 6 refers to 6 joint points of the swing angle between the telescopic arm and the rotating arm, that is, the robotic arm is composed of 6 motion units.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] By setting a unified path planning and scheduling system for multiple robotic arms and multiple target nodes, the path planning of multiple target nodes corresponding to the work of multiple robotic arms that cannot be achieved by the dynamic obstacle avoidance path planning of a single robotic arm is realized. At the same time, through the shortest time algorithm, the working efficiency of the robotic arm is accelerated to select the action path with the optimal working efficiency, and then the shortest path is selected. The working efficiency of multiple robotic arms is improved to adapt to the biological sample detection process with a large amount of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a structural block diagram of an optimal path planning and scheduling system for a robot according to an embodiment of the present invention;

[0048] Figure 2 It is a flowchart of an optimal path planning and scheduling method for a robot according to an embodiment of the present invention;

[0049] Figure 3 It is a detailed flowchart of step S2 according to an embodiment of the present invention;

[0050] Figure 4 It is a structural block diagram of a robotic arm operation system according to an embodiment of the present invention.

[0051] Explanation of the marks in the figure: DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the embodiments and the drawings. The content mentioned in the embodiments does not limit the present invention.

[0053] As Figure 1 shown, this embodiment provides an optimal path planning and scheduling system for a robot, including a sample coordinate acquisition module, a robotic arm initial state acquisition module, a restricted barrier acquisition module, a path traversal module, a path action times calculation module, a path time value calculation module, a module for judging the path with the shortest time, an optimal path storage module, and an optimal path execution module;

[0054] The sample coordinate acquisition module acquires the position of the target node that the current robotic arm needs to collect and detect through a camera. To achieve this action, a proximity sensor can be used to detect whether a test tube or other container storing sample coordinates to be detected has fallen into the detection port. Then, a camera is used to further verify whether there is a test tube or other container in the detection port. The coordinates of the detection port are preset during initialization. As long as the proximity sensor determines which detection port it is, the coordinates of this detection port in the database can be called to obtain the coordinate parameters of the target node position to be collected and detected. When multiple robotic arms collect the corresponding multiple target node positions to be collected and detected, each robotic arm only needs to correspond to the target node to be collected and detected that is closest to the three-dimensional coordinates of the state node of the current robotic arm.

[0055] The robotic arm initial state acquisition module acquires the three-dimensional coordinate parameters of the state node of the robotic arm through a camera. To achieve this process, a horizontally set proximity sensor or position sensor is also used to detect the sample collection point at the very front of the robotic arm. Then, an inductive sensor is used to detect whether the camera is powered on to further ensure that the robotic arm is in the initial unpowered and tightened state. Finally, the image collected by the camera is used to verify that the camera is in the initial non-operating state, and the three-dimensional coordinate parameters of the node are unified as the initial origin coordinate parameters.

[0056] The restricted barrier acquisition module is used to acquire the three-dimensional coordinate parameters of the running barrier nodes of the current robotic arm as the three-dimensional coordinates of the restricted traversal nodes for path traversal. If there are two robotic arms, the mid-axis plane of the two robotic arms is the three-dimensional plane of the restricted traversal nodes of the two robotic arms. By setting the three-dimensional coordinates of the restricted traversal nodes during the robotic arm traversal process, not only the possibility of collision between robotic arms during operation is avoided, but also the computational complexity and data volume of path traversal are reduced.

[0057] The path traversal module uses the rapidly-exploring random tree algorithm to traverse all reachable node coordinates and paths at the front end of the current robotic arm, and transmits all reachable node coordinates and paths to the path action count calculation module.

[0058] The path action count calculation module decomposes all reachable node coordinates at the front end of the robotic arm for each single path to obtain the number of actions (rotation, extension, and swing) of all joints when the robotic arm completes the entire path.

[0059] The path time value calculation module multiplies the number of actions of each joint obtained by decomposition by the average time required for the robotic arm to complete this action, and then sums the times of all actions to obtain the total operation time data value of a single path.

[0060] The module for determining the shortest-time path uses a bubble sort program to determine the path with the shortest time among all paths as the optimal path for the robotic arm to run.

[0061] The optimal path storage module is used to store the coordinates of all accessible nodes at the front end of the robotic arm for the optimal path, the number of actions of each joint, and the sequence of actions; it facilitates the optimal path execution module to call the corresponding data to the robotic arm for execution; during the subsequent operation of the robotic arm, to prevent program errors, the coordinates of the accessible nodes can be used to verify whether the actions of each joint fully implement the initially traversed optimal path. By using an image recognition algorithm to identify whether the video frame image coordinates of the start and end time nodes of the action are consistent with the coordinates of all accessible nodes at the front end of the robotic arm for the optimal path when the robotic arm executes each action. If they are consistent, it indicates that there are no errors in the process of the robotic arm executing the action. If they are inconsistent, a command indicating whether the robotic arm has executed the action is issued.

[0062] The optimal path execution module is used to call the number of actions of each joint of the robotic arm and the sequence of actions in the optimal path storage module, and the robotic arm executes them in sequence.

[0063] The optimal path storage module also stores the name of the robotic arm, the position of the target node for acquisition and detection, the three-dimensional coordinate parameters of the state nodes of the robotic arm, the coordinates of all accessible nodes at the front end of the robotic arm, the number of actions of the joints, and the data value of the total running time of a single path. The position of the robotic arm can be quickly located through the name of the robotic arm and the position of the target node for acquisition and detection; the three-dimensional coordinate parameters of the state nodes of the robotic arm, the coordinates of all accessible nodes at the front end of the robotic arm, the number of actions of the joints, and the data value of the total running time of a single path can be used to mutually verify whether the optimal path planning of the robotic arm is reasonable.

[0064] As Figure 4 shown, it further includes a robotic arm operating system. The robotic arm operating system includes a first conveyor mechanism, a first proximity sensor, a second conveyor mechanism, a second proximity sensor, multiple robotic arms, a biological sample detection port, a numerical converter, and a terminal controller;

[0065] The first conveyor mechanism is arranged directly below the robotic arm. There is a biological sample detection port above the first conveyor mechanism, and a first proximity sensor is arranged directly below the first conveyor mechanism;

[0066] The second conveyor mechanism is arranged longitudinally on the side of the robotic arm, and a second proximity sensor is arranged on the second conveyor mechanism;

[0067] The numerical converter receives the data from the optimal path execution module and converts it into an electrical signal recognizable by the terminal controller. The terminal controller is connected to the joint points of multiple robotic arms through control lines and controls the movement of the joint points of the robotic arms through electrical signals.

[0068] The robotic arm includes at least one motion unit, which is successively connected to a rotating arm, a telescopic arm, and a swinging arm; the rotating arm controls the rotatable angle of the entire robotic arm, and the telescopic arm is connected to the rotating arm; the swinging arm is connected to the front end of the telescopic arm and can swing at multiple angles around the connection point. The total number of paths required for traversing the robotic arm can be further reduced by presetting the rotatable angle of the rotating arm and the numerical value of each rotation angle. Similarly, the telescopic length of the telescopic arm and the swingable angle of the swinging arm can also be preset to limit the data redundancy generated during traversal in the optimal path planning process. It also plays a certain role in preventing the collision and barrier between the two robotic arms during operation.

[0069] As Figure 2 shown, a method for optimal path planning and scheduling of a robot includes the steps:

[0070] S1. Collect the three-dimensional coordinate parameters of the target node to be collected and detected currently and the state node of the current robotic arm through a camera;

[0071] S2. Traverse all paths from the state node of the current robotic arm to the target node through the rapidly exploring random tree algorithm;

[0072] S3. Calculate the time values of each single path among all paths by multiplying the number of rotations, extensions, and swings of each action in a single path by the preset operation time of each action;

[0073] S4. Input the time values into the bubble program one by one to obtain the shortest time data value;

[0074] S5. Judge the number of shortest time data values. If it is 1, go to step S6; if it is greater than 1, go to step S7;

[0075] S6. Output the optimal path required for the robotic arm corresponding to the shortest time data value;

[0076] S7. Judge the shortest path of the robotic arm operation among multiple shortest time data values and output it as the optimal path required for the robotic arm;

[0077] S8. The robotic arm receives the optimal path from the state node of the current robotic arm to the target node and controls the corresponding joints to execute the operation parameters of the optimal path in sequence.

[0078] As Figure 3 shown, the detailed steps of step 2 include:

[0079] S201. First, initialize variables, such as the starting point q init , the target point q goal , the path Path, and the tree T;

[0080] S202. Randomly sample to generate a node qnew Perform collision detection. If a collision occurs, enter the next iteration and resample; otherwise, proceed to the next step.

[0081] S203. Determine whether the generated q new node and q goal meet the pre-given constraints. If they do, go to S207; otherwise, go to S204.

[0082] S204. According to the generated q new situation, call the adaptive step size strategy to adjust the step size factor.

[0083] S205. According to the expansion point selection strategy, expand a new node, and then perform collision detection. If there is no collision, generate a new node q new Then go to S203 for judgment; otherwise, go to S206.

[0084] S206. At this time, determine whether the generated tree has fallen into a local minimum. If it has fallen into a local minimum, call the local escape algorithm to generate q new Then go to S203 for judgment. If it has not fallen into the minimum, go to S204.

[0085] S207. At this time, a random expansion tree that meets the system constraints has been generated. Call the Dijkstra algorithm to optimize the path. If the cost of the new path is smaller, update the path.

[0086] S208. Return the generated RRT tree and end.

[0087] The RRT algorithm plans an effective path for the end of the robotic arm in the Cartesian space. However, when the end of the robotic arm is moving along this effective path, the poses of other joint linkages also need to be considered. By defining a weight coefficient for the rotation or swing angle, telescopic amount of each joint of the robotic arm, and the angle value that can be rotated or swung, the path cost is calculated based on this:

[0088]

[0089] Among them, ω i refers to the weight of each joint, and Δθ i refers to the joint change amount. By substituting all the path solutions in step S208 into the above formula, the joint swing angles of the optimal target pose are obtained. i = 1 to 6 refers to the 6 joint points of the swing angle between the telescopic arm and the rotating arm, that is, the robotic arm is composed of 6 motion units.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] By setting up a unified path planning and scheduling system for multiple robotic arms and multiple target nodes, it is possible to achieve path planning for multiple target nodes corresponding to the work of multiple robotic arms, which cannot be achieved by the dynamic obstacle avoidance path planning of a single robotic arm. At the same time, through the time shortest algorithm, the working efficiency of the robotic arm is accelerated to select the action path with the optimal working efficiency, and then the shortest path is selected. The working efficiency of multiple robotic arms is improved to adapt to the biological sample detection process with a large amount of data.

[0092] The above provides a detailed introduction to an optimal path planning and scheduling system and method for a robot provided by the present application. The description of specific embodiments is only used to help understand the method and its core idea of the present application. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

Claims

1. An optimal path planning and scheduling system for a robot, characterized in that, It includes a sample coordinate acquisition module, a robotic arm initial state acquisition module, a restricted barrier acquisition module, a path traversal module, a path action count calculation module, a path time value calculation module, a module for determining the path with the shortest time, an optimal path storage module, and an optimal path execution module; The sample coordinate acquisition module acquires the position of the target node that the current robotic arm needs to collect and detect through a camera; The robotic arm initial state acquisition module acquires the three-dimensional coordinate parameters of the state nodes of the robotic arm through a camera; The restricted barrier acquisition module is used to acquire the three-dimensional coordinate parameters of the operation barrier nodes of the current robotic arm as the three-dimensional coordinates of the restricted traversal nodes for path traversal; The path traversal module traverses all the reachable node coordinates and paths at the front end of the current robotic arm using the rapidly-exploring random tree algorithm and transmits all the reachable node coordinates and paths to the path action count calculation module; The path action count calculation module decomposes all the reachable node coordinates at the front end of the robotic arm for each single path to obtain the action counts of all joints of the robotic arm when completing the entire path; The path time value calculation module multiplies the action count of each joint obtained by decomposition by the average time required for the robotic arm to complete the action, and then sums up the times of all actions to obtain the total operation time data value of a single path; The module for determining the path with the shortest time determines the path with the shortest time among all paths through a bubble sort program as the optimal path for the robotic arm to operate; The optimal path storage module is used to store all the reachable node coordinates at the front end of the robotic arm for the optimal path, the action counts of each joint, and the sequence of actions; The optimal path execution module is used to call the action counts of each joint of the robotic arm and the sequence of actions in the optimal path storage module, and the robotic arm executes them in sequence.

2. The optimal path planning and scheduling system for a robot according to claim 1, characterized in that, The optimal path storage module also stores the name of the robotic arm, the position of the target node to be collected and detected, the three-dimensional coordinate parameters of the state nodes of the robotic arm, all the reachable node coordinates at the front end of the robotic arm, the action counts of the joints, and the total operation time data value of a single path.

3. The optimal path planning and scheduling system for a robot according to claim 2, wherein It further includes a robotic arm operation system, and the robotic arm operation system includes a first conveyor mechanism, a first proximity sensor, a second conveyor mechanism, a second proximity sensor, multiple robotic arms, a biological sample detection port, a numerical converter, and a terminal controller; The first conveyor mechanism is arranged directly below the robotic arm. There is a biological sample detection port above the first conveyor mechanism, and a first proximity sensor is arranged directly below the first conveyor mechanism; The second conveyor mechanism is arranged longitudinally on the side of the robotic arm, and a second proximity sensor is arranged on the second conveyor mechanism; The numerical converter receives the data from the optimal path execution module and converts it into an electrical signal recognizable by the terminal controller. The terminal controller is connected to the joint points of multiple robotic arms through control lines and controls the movement of the joint points of the robotic arms through electrical signals.

4. The optimal path planning and scheduling system for a robot according to claim 3, wherein, The robotic arm includes at least one motion unit, and the motion unit is successively connected to a rotating arm, a telescopic arm, and a swinging arm; the rotating arm controls the rotation angle of the entire robotic arm, the telescopic arm is connected to the rotating arm; the swinging arm is connected to the front end of the telescopic arm and can swing at multiple angles around the connection point.

5. A robot optimal path planning and scheduling method for implementing the robot optimal path planning and scheduling system described in claim 4, characterized in that, It includes steps: S1. Collect the three-dimensional coordinate parameters of the target node to be collected and detected currently and the state node of the current robotic arm through a camera; S2. Traverse all paths from the state node of the current robotic arm to the target node through the Rapidly-exploring Random Tree (RRT) algorithm; S3. Calculate the time values of each single path among all paths by multiplying the number of rotations, extensions, and swings of each action in a single path by the preset operation time of each action; S4. Input the time values into the bubble sort program one by one to obtain the shortest time data value; S5. Judge the number of the shortest time data values. If it is 1, go to step S6; if it is greater than 1, go to step S7; S6. Output the optimal path required for the robotic arm corresponding to the shortest time data value; S7. Judge the shortest path of the robotic arm operation among multiple shortest time data values and output it as the optimal path required for the robotic arm to run; S8. The robotic arm receives the optimal path from the state node of the current robotic arm to the target node and controls the corresponding joints to execute the operation parameters of the optimal path in sequence.

6. A method for optimal path planning and scheduling of a robot according to claim 5, characterized in that, The detailed steps of step 2 include: S201. First, initialize variables, including the starting point q init , the target point q goal , the path Path, the tree T, and the coordinates of the obstacle points, which are the evenly divided planes between two robotic arms and other planes in the space mapped by this evenly divided plane, and the four planes in the vertical direction corresponding to the robotic arms; S202. Randomly sample to generate node q new And perform collision detection. If a collision occurs, enter the next iteration and resample; otherwise, proceed to the next step. S203. Determine whether the generated q new node and q goal meet the pre-given constraints. If so, go to S207; otherwise, go to S204. S204. According to the generated q new situation, call the adaptive step size strategy to adjust the step size factor; S205. According to the extension point selection strategy, expand a new node, and then perform collision detection. If there is no collision, generate a new node q new Then go to S203 for judgment, otherwise go to S206; S206. At this time, it is determined whether the spanning tree has fallen into a local minimum. If it has fallen into a local minimum, the local escape algorithm is called to generate q new Then, it is transferred to S203 for judgment. If it has not fallen into the minimum value, it is transferred to S204; S207. At this time, a random expansion tree that meets the system constraints has been generated. Call the Dijkstra algorithm to optimize the path. If the cost of the new path is smaller, update the path; S208. Return the generated RRT tree and end.

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