Method and device for planning a movement path of a robot arm in a water chamber of an evaporator

Abstract

The application relates to a motion path planning method and device of a mechanical arm in an evaporator water chamber. The method comprises the following steps: under the condition that a robot does not collide with an inner wall of the water chamber and the minimum distance between the robot and the inner wall of the water chamber is greater than a preset safety distance, determining a neighborhood centering on a first node; for each adjacent node in the neighborhood, determining the path cost of the adjacent node according to the first distance corresponding to the node group of the path of the adjacent node, the second distance between the adjacent node and the first node, the minimum distance at each node on the path and the minimum distance at the first node; and based on the path cost of each adjacent node, determining the parent node of the first node from the adjacent nodes to plan the motion path of the mechanical arm. The motion path not only tends to be the shortest, but also ensures that the distance between the robot and the inner wall of the water chamber is relatively large, so that the mechanical arm does not collide during the installation operation of the blocking plate in the evaporator water chamber.

Description

Technical Field

[0001] This application relates to the field of path planning technology, and in particular to a method and apparatus for planning the motion path of a robotic arm in an evaporator water chamber. Background Technology

[0002] With the continuous increase in demand for industrial automation, robotic arms are increasingly operating in narrow spaces (such as inside equipment cavities and pipes), which places higher demands on the obstacle avoidance accuracy and environmental adaptability of path planning.

[0003] Currently, when a robotic arm performs a plug installation operation in the evaporator water chamber, it first needs to grab the plug near the manhole and transport it to the coolant pipe inlet for installation. Due to the narrow space inside the water chamber, the robotic arm is prone to collisions with the chamber structure during the handling process. Therefore, planning the robotic arm's path during the automated plug installation and removal process to avoid collisions has become a pressing problem in this field. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and apparatus for planning the motion path of a robotic arm in an evaporator water chamber, which can plan the path of the robotic arm during the automatic disassembly and assembly of the blocking plate to avoid collisions, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a motion path planning method for a robotic arm inside an evaporator water chamber, including:

[0006] Under the condition that the robot does not collide with the water chamber wall at the first node and the minimum distance between the robot and the water chamber wall is greater than the preset safety distance, the neighborhood is determined with the first node as the center; the first node is the node determined based on the preset expansion step size along the line connecting the second node and the random point; the second node is the node on the expansion tree that is closest to the random point; the robot includes the robotic arm and the blockage plate grasped by the robotic arm; the random point is a random point generated in the joint configuration space containing the starting point and the destination node;

[0007] For each neighboring node in the neighborhood, the path cost of the neighboring node is determined based on the first distance of the node group under the path corresponding to the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node; the node group includes two adjacent nodes on the path.

[0008] Based on the path cost of each neighboring node, determine the parent node of the first node from among the neighboring nodes.

[0009] Based on the distance between the first node and the target node, and the first node and its parent node, plan the motion path of the robotic arm.

[0010] In one embodiment, the path cost of a neighboring node is determined based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node, including:

[0011] Determine the first square of the minimum distance under each node on the path, and the second square of the minimum distance under the first node;

[0012] Determine the first reciprocal of each first square and the first summation result of each second square, and determine the product of the first summation result and the preset distance constraint coefficient;

[0013] The path cost of neighboring nodes is determined based on the product, each first distance, and the second distance.

[0014] In one embodiment, there are multiple node groups. The path cost of neighboring nodes is determined based on the product, each first distance, and a second distance, including:

[0015] Determine the second summation result between each of the first distances and the second distances;

[0016] The path cost of neighboring nodes is determined based on the second summation result and the product.

[0017] In one embodiment, determining the path cost of neighboring nodes based on the second summation result and the product includes:

[0018] Determine the third summation result between the second summation result and the product, and determine the third summation result as the path cost of the neighboring nodes.

[0019] In one embodiment, determining the parent node of the first node from among the neighboring nodes based on the path cost of each neighboring node includes:

[0020] The neighboring node corresponding to the minimum path cost is determined as the parent node of the first node.

[0021] In one embodiment, the method further includes:

[0022] Determine the quotient of the logarithm of the number of nodes in the expanded tree divided by the number of nodes;

[0023] Determine the second reciprocal of the degrees of freedom, and determine the target result of the second reciprocal power of the quotient;

[0024] The neighborhood radius is determined based on the product of the target result and the preset coefficients.

[0025] The neighborhood is formed by taking the first node as the center and determining the neighboring nodes whose distance from the first node is less than the neighborhood radius.

[0026] In one embodiment, the method further includes:

[0027] With the configuration of nodes on the path, determine the third distance between the robot's minimum convex hull and each convex polyhedron in the minimum convex hull of the water chamber space structure; the minimum convex hull of the water chamber space structure is the minimum convex hull corresponding to the water chamber space structure of the evaporator water chamber, and the minimum convex hull of the robot is the minimum convex hull surrounding the robot.

[0028] The minimum value among all third distances is determined as the minimum distance under the node.

[0029] In one embodiment, the motion path of the robotic arm is planned based on the distance between the first node and the target node, the first node, and the parent node of the first node, including:

[0030] If the distance between the first node and the target node is less than the preset expansion step size, determine the current path of the robotic arm, the parent node of the first node, and the planned motion path of the robotic arm with the first node as the target node.

[0031] If the distance between the first node and the destination node is not less than the preset expansion step size, a new first node is determined, and the process of determining the neighborhood centered on the new first node is repeated until the distance between the most recently determined first node and the destination node is less than the preset expansion step size. The current path of the robotic arm, the parent node of the new first node, and the planned motion path of the robotic arm with the new first node as the center are determined.

[0032] In one embodiment, determining the new first node includes:

[0033] For each neighboring node in the neighborhood, if the path cost from the first node to the neighboring node is less than the path cost from the parent node of the neighboring node to the neighboring node, the parent node of the neighboring node is updated to the first node, so as to update the extended tree and obtain a new extended tree.

[0034] Along the line connecting the second node and the new random point, determine the new first node based on the preset expansion step size; the second node is the point in the new expanded tree with the smallest distance from the new random point.

[0035] Secondly, this application also provides a motion path planning device for a robotic arm inside an evaporator water chamber, comprising:

[0036] The first determining module is used to determine the neighborhood centered on the first node when it is determined that the robot has not collided with the water chamber wall and the minimum distance between the robot and the water chamber wall is greater than a preset safety distance. The first node is a node determined based on a preset expansion step size along the line connecting the second node and the random point. The second node is the node on the expansion tree that is closest to the random point. The robot includes a robotic arm and a blockage plate grasped by the robotic arm. The random point is a random point generated in the joint configuration space containing the starting point and the destination node.

[0037] The second determining module is used to determine the path cost of each neighboring node in the neighborhood based on the first distance of the node group under the path corresponding to the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node; the node group includes two adjacent nodes on the path.

[0038] The third determining module is used to determine the parent node of the first node from among the neighboring nodes based on the path cost of each neighboring node.

[0039] The path planning module is used to plan the motion path of the robotic arm based on the distance between the first node and the destination node, the first node and its parent node.

[0040] The aforementioned method and apparatus for planning the motion path of the robotic arm inside the evaporator water chamber, after determining that the robot does not collide with the inner wall of the water chamber at a first node and that the minimum distance between the robot and the inner wall of the water chamber is greater than a preset safety distance, determines a neighborhood centered on the first node. For each neighboring node within the neighborhood, the path cost of the neighboring node is determined based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node. Based on the path cost of each neighboring node, the parent node of the first node is determined from among the neighboring nodes. The motion path of the robotic arm is planned based on the distance between the first node and the destination node, the first node, and the parent node of the first node. Because the path cost of the neighboring node is determined based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node, the final determined motion path of the robotic arm not only has the shortest distance but also ensures a large distance between the robot and the inner wall of the water chamber, thereby avoiding collisions during the installation of the plug plate inside the evaporator water chamber. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the structure of a robotic arm provided in an embodiment of this application;

[0043] Figure 2 This is a flowchart illustrating a motion path planning method for a robotic arm inside an evaporator water chamber, as provided in an embodiment of this application.

[0044] Figure 3 This is a flowchart illustrating a path cost determination method provided in an embodiment of this application;

[0045] Figure 4 This is a flowchart illustrating another path cost determination method provided in an embodiment of this application;

[0046] Figure 5 This is a flowchart illustrating a neighborhood determination method provided in an embodiment of this application;

[0047] Figure 6 This is a flowchart illustrating a method for determining the minimum distance provided in an embodiment of this application;

[0048] Figure 7 This is a flowchart illustrating a method for determining a first node provided in an embodiment of this application;

[0049] Figure 8 This is a schematic diagram of the overall process of a motion path planning method provided in an embodiment of this application;

[0050] Figure 9 This is a schematic diagram of the motion path planning device for a robotic arm inside an evaporator water chamber, provided in an embodiment of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0053] The robotic arm in the embodiments of this application is as follows: Figure 1 As shown, Figure 1 This is a schematic diagram of a robotic arm provided in an embodiment of this application. The internal shape of the primary water chamber of the evaporator is approximately a quarter sphere, with a manhole and a coolant inlet / outlet pipe at each end. After the plug plate is inserted into the water chamber through the manhole, the robotic arm grabs the plug plate and transports it to the pipe opening for installation.

[0054] The blockage removal and installation robot system consists of a mobile chassis, a conveying mechanism, and a robotic arm. The robotic arm base is fixed to the conveying mechanism and enters the water chamber through a manhole. The conveying mechanism serves as a redundant translational joint for the robotic arm. The robotic arm's end effector is equipped with a quick-change disc, which works in conjunction with the quick-change disc on the blockage plate to lock and release, enabling the picking up and placement of the blockage plate.

[0055] The robotic arm has a repeatability of 0.2 mm and can achieve precise pose control in joint space and Cartesian space.

[0056] To facilitate the description of the embodiments of this application, the relevant technologies involved in the embodiments of this application will be introduced first:

[0057] 1) Calculation of the state space and forward kinematics of the robotic arm joint configuration:

[0058] The state vector in the joint configuration state space of a serially connected robotic arm with n free joints is defined as follows: ,in Indicates the first The position (angle or displacement) of each joint. Two states. and The Euclidean distance between them is defined as: .

[0059] The DH method is used to establish the link coordinate system of the robotic arm, and four DH parameters between two adjacent joint coordinate systems are determined: link length. Linkage angle Linkage offset Joint angle For joints If it is a rotational joint, then the joint variable is the joint angle ( If it is a translational joint, then the joint variable is the displacement. Adjacent joint coordinate system arrive The homogeneous transformation matrix is:

[0060]

[0061] For those with For a robot with 1 joint, the transformation matrix from the base coordinate system to the end effector is:

[0062]

[0063] Therefore, the overall pose configuration of the robotic arm can be determined by the state vectors of its joint configurations. The only certainty.

[0064] 2) Convex decomposition of the water chamber model and convex hull of the robotic arm:

[0065] The water chamber structure, which encloses the water chamber space, can be divided into three parts: a side partition, a top tube plate, and an approximately quarter-spherical wall. To meet the collision detection algorithm's requirement for convex polyhedral input, the geometry of the water chamber can be simplified. The partition and tube plate can be cuboids. Instead, the spherical wall portion is a non-convex curved shell. Using the V-HACD convex decomposition algorithm, the spherical wall is decomposed into... A set of non-intersecting convex polyhedra The environmental input for collision detection is the set of these decomposed convex polyhedra. The set of convex polyhedra after decomposition The minimum convex hull that makes up the water chamber spatial structure.

[0066] For ease of collision detection calculations, the robotic arm and the blocking plate are considered as a single unit, with the blocking plate being regarded as an extension of the robotic arm's end effector. Hereinafter, this unit will be referred to as the "robot." Based on the robot's 3D model, the minimum convex hull surrounding the robot can be generated, serving as the robot-end input for collision detection. .

[0067] 3) Minimum distance calculation:

[0068] The robot is configured at a certain joint. Below, the GJK collision detection and minimum distance calculation algorithm is used to calculate the minimum convex hull of the robot. and Collision detection is performed on each convex polyhedron, and the set of minimum distances between the robot's minimum convex hull and each convex polyhedron is output. Ultimately, the robot was configured with joints. The minimum distance between the ground and the environment can be expressed as That is, the robot's joint configuration. The minimum distance between the lower part and the inner wall of the water chamber space structure can be expressed as: Joint configuration refers to the robot's state vector. The robot's state vector... The minimum distance between the lower part and the inner wall of the water chamber space structure can be expressed as: .

[0069] In one exemplary embodiment, such as Figure 2 As shown, Figure 2 This is a flowchart illustrating a motion path planning method for a robotic arm inside an evaporator water chamber, as provided in an embodiment of this application. The method includes the following steps S201 to S204. Wherein:

[0070] S201, under the condition that the minimum distance between the robot and the water chamber wall is greater than the preset safety distance under the first node, the neighborhood is determined with the first node as the center; the first node is the node determined based on the preset expansion step size along the line direction connecting the second node and the random point, the second node is the node on the expansion tree that is closest to the random point, the robot includes a robotic arm and a blockage plate grasped by the robotic arm, and the random point is a random point generated in the joint configuration space containing the starting point and the destination node.

[0071] Random points can be generated in the joint configuration space that includes the start point and the destination node. And find the node in the extended tree that is closest to the random point. .node This is the second node.

[0072] Along the node With random points The direction of the line connecting from Expand by a step length Acquire new nodes ,node This is the first node.

[0073] ;

[0074] in, This represents the preset expansion step size.

[0075] Collision detection: In The system detects whether the robot collides with the inner wall of the water chamber. If no collision occurs, it determines whether the minimum distance between the robot and the inner wall of the water chamber is greater than the preset safe distance. If the minimum distance between the robot and the inner wall of the water chamber is greater than the preset safe distance, the neighborhood is determined with the first node as the center.

[0076] by Centered on, select with The Euclidean distance between them is less than All tree nodes form a neighborhood, and the neighborhood radius is... The selection is as follows:

[0077] ;

[0078] in, It is the number of nodes in the current tree. It is the dimension of the configuration space (similar to the robot's degrees of freedom). Calculate experience points.

[0079] Get selection and Euclidean distance between The neighborhood is formed by all tree nodes whose difference is less than the preset difference.

[0080] It should be noted that this embodiment mentions random points. ,node ,node These all represent state vectors in the joint configuration state space of the robotic arm, not actual physical positions.

[0081] The minimum distance between the robot and the inner wall of the water chamber is the distance between the minimum convex hull of the water chamber space structure and the minimum convex hull of the robot. The distance between the minimum convex hull of the water chamber space structure and the minimum convex hull of the robot can characterize the minimum distance between the robot and the inner wall of the water chamber.

[0082] S202, for each neighboring node in the neighborhood, determine the path cost of the neighboring node based on the first distance of the node group under the path corresponding to the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node; the node group includes two adjacent nodes on the path.

[0083] The path cost function can be calculated based on the following formula. Each tree node in the neighborhood Path cost :

[0084]

[0085] As can be seen, the formula for the path cost function above consists of two parts. The first part is... The second part is The first part aims to minimize the planned motion path, while the second part aims to maximize the minimum distance between the robot and the water chamber wall, thereby avoiding collisions. The second part could also be... , It can be equal to 3 or other values, as long as it maximizes the minimum distance between the robot and the inner wall of the water chamber.

[0086] in, (i=1,2,...,p-1) represents the distance from the starting point to the node. The sequence of path nodes on the path, It is the number of path nodes. Right now , Right now , It is a preset distance constraint coefficient, which can be adjusted to determine the safe distance of the planned path. It is the robot's joint configuration The minimum distance between the ground and the environment. Configure the poses of two joints in the joint configuration space. and The Euclidean distance between them.

[0087] In this embodiment, the starting point to the node The path is the path corresponding to the neighboring nodes.

[0088] It should be noted that the path cost can also be determined based on a modified formula of the path cost function described above. For example, the path cost can be obtained by multiplying the result on the right side of the above formula by a preset coefficient.

[0089] S203, Based on the path cost of each neighboring node, determine the parent node of the first node from among the neighboring nodes.

[0090] The neighboring node with the lowest path cost can be determined as the parent node of the first node. Alternatively, any neighboring node whose path cost is less than a preset path cost can be determined as the parent node of the first node.

[0091] The node with the minimum path cost in the neighborhood can be selected. As The parent node, not As. Connection and The environment here refers to the convex hull of the water chamber spatial structure. Node If we denote it as point A, and the extended tree in S201 is the initially constructed extended tree, If point B is the starting point, then the planned movement path is: starting point → point A → (Point B).

[0092] S204: Based on the distance between the first node and the target node, and the first node and its parent node, plan the motion path of the robotic arm.

[0093] If the distance between the first node and the destination node is less than the preset expansion step size, determine the current path of the robotic arm, the parent node of the first node, and the planned motion path of the robotic arm with the first node as the reference point. The current path of the robotic arm refers to the portion of the motion path planned before the current time.

[0094] For example, if the distance between point B and the destination node is less than the preset expansion step size, then the starting point → point A → is determined. Point B represents the planned motion path of the robotic arm. In this case, the current path of the robotic arm includes the starting point.

[0095] If the distance between the first node and the destination node is not less than the preset expansion step size, a new first node is determined, and the process of determining the neighborhood centered on the new first node is repeated until the distance between the most recently determined first node and the destination node is less than the preset expansion step size. The current path of the robotic arm, the parent node of the new first node, and the planned motion path of the robotic arm with the new first node as the center are determined.

[0096] For example, referring to the above example, if the distance between point B and the target node is not less than the preset expansion step size, the planned partial motion path is: starting point → point A → (Point B), that is, the current path of the robotic arm is the starting point → point A → (Point B). In this case, the expanded tree in S201 can be updated to obtain a new expanded tree. Along the line connecting the second node and the new random point, a new first node is determined based on a preset expansion step size. The second node is the point in the new expanded tree with the smallest distance to the new random point. The nodes after point B are determined by determining the new first node. This continues until the distance between the most recently determined new first node and the target node is less than the preset expansion step size. The current path of the robotic arm, the parent node of the new first node, and the planned motion path of the robotic arm are then determined. For example, if the new first node is point C and its parent node is point D, and the distance between point C and the target node is less than the preset expansion step size, then the planned motion path of the robotic arm is: starting point → point A → (Point B) → Point D → Point C. The current path of the robotic arm is: starting point → point A → (Point B).

[0097] In this embodiment, if it is determined at the first node that the robot does not collide with the inner wall of the water chamber and the minimum distance between the robot and the inner wall of the water chamber is greater than a preset safety distance, a neighborhood is determined centered on the first node. For each neighboring node within the neighborhood, the path cost of the neighboring node is determined based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node. Based on the path cost of each neighboring node, the parent node of the first node is determined from each neighboring node. Based on the distance between the first node and the target node, the first node and the parent node of the first node, the motion path of the robotic arm is planned. Since the path cost of the neighboring node is determined based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node, the final determined motion path of the robotic arm not only has the shortest distance but also ensures a large distance between the robot and the inner wall of the water chamber, thereby avoiding collisions during the installation of the plug plate in the evaporator water chamber.

[0098] In one exemplary embodiment, such as Figure 3 As shown, Figure 3 This is a flowchart illustrating a path cost determination method provided in an embodiment of this application. The above-described S202 includes steps S301 to S303:

[0099] S301, determine the first square of the minimum distance under each node on the path, and the second square of the minimum distance under the first node.

[0100] S302, determine the first reciprocal of each first square and the first summation result of the second reciprocal of each second square, and determine the product of the first summation result and the preset distance constraint coefficient.

[0101] As shown in the formula for the path cost function above, the first summation result is equal to The product of the first summation result and the preset distance constraint coefficient is: .

[0102] S303, determine the path cost of neighboring nodes based on the product, each first distance, and the second distance.

[0103] The path cost of a neighboring node can be determined by the product, the sum of each first distance, and the second distance. Alternatively, the second summation result between each first distance and the second distance can be determined, the product of the second summation result and a preset value can be determined, and the sum of this product and the product determined in S302 can be used as the path cost of the neighboring node.

[0104] In this embodiment, by determining the first square of the minimum distance under each node on the path and the second square of the minimum distance under the first node, the first reciprocal of each first square and the second reciprocal of the second square are determined, and the product of the first summation result and the preset distance constraint coefficient is determined. Based on the product, each first distance, and the second distance, the path cost of the neighboring node is determined. Since not only the first distance and the second distance are taken into account, but also the minimum distance under each node on the path and the minimum distance under the first node, that is, the second part of the formula in the above-mentioned path cost function is taken into account, the path cost of the neighboring node is determined more accurately. This makes the final determined motion path not only the shortest distance, but also the distance between the robot and the water chamber structure maximized, thereby avoiding collisions between the robot and the water chamber structure.

[0105] In one exemplary embodiment, such as Figure 4 As shown, Figure 4 This is a flowchart illustrating another path cost determination method provided in this application embodiment. In this application embodiment, there are multiple node groups. The above-mentioned S303 includes steps S401 to S402:

[0106] S401, determine the second summation result between each first distance and the second distance.

[0107] S402, determine the path cost of neighboring nodes based on the second summation result and the product.

[0108] In this embodiment, by determining the second summation result between each first distance and the second distance, and determining the path cost of the neighboring node based on the second summation result and the product, the obtained path cost can more comprehensively measure the distance of the motion path and the distance between the robot and the water chamber structure.

[0109] In an exemplary embodiment, the above-described S402, which determines the path cost of neighboring nodes based on the second summation result and the product, can be implemented in the following way:

[0110] Determine the third summation result between the second summation result and the product, and determine the third summation result as the path cost of the neighboring nodes.

[0111] In this embodiment, by determining the third summation result between the second summation result and the product, and determining the third summation result as the path cost of the neighboring nodes, the obtained path cost can more comprehensively measure the distance of the motion path and the distance between the robot and the water chamber structure.

[0112] In an exemplary embodiment, S203 described above, determining the parent node of the first node from among the neighboring nodes based on the path cost of each neighboring node, can be achieved in the following way:

[0113] The neighboring node corresponding to the minimum path cost is determined as the parent node of the first node.

[0114] By determining the neighboring node with the minimum path cost as the parent node of the first node, the minimum distance between the robot and the inner wall of the water chamber can be maximized, thereby reducing the probability of collision between the robot and the inner wall of the water chamber.

[0115] In one exemplary embodiment, such as Figure 5 As shown, Figure 5 This is a flowchart illustrating a neighborhood determination method provided in an embodiment of this application. The method includes:

[0116] S501, determine the quotient of the logarithm of the number of nodes in the expanded tree divided by the number of nodes.

[0117] S502, determine the second reciprocal of the degrees of freedom, and determine the target result of the second reciprocal power of the quotient.

[0118] S503 determines the neighborhood radius based on the product of the target result and the preset coefficients.

[0119] S504, take the first node as the center, and determine the neighboring nodes whose distance from the first node is less than the neighborhood radius to form a neighborhood.

[0120] The above In the formula, The quotient of the logarithm of the number of nodes in the expanded tree divided by the number of nodes. The target result is... . For preset coefficients, is the neighborhood radius.

[0121] In this embodiment, the second reciprocal of the degrees of freedom is determined by dividing the logarithm of the number of nodes in the extended tree by the quotient of the number of nodes, and the target result of the second reciprocal of the quotient is determined. Based on the product of the target result and the preset coefficient, the neighborhood radius is determined. The neighborhood is formed by taking the first node as the center and determining the neighboring nodes whose distance from the first node is less than the neighborhood radius, thus laying the foundation for determining the parent node of the first node.

[0122] In one exemplary embodiment, such as Figure 6 As shown, Figure 6 This is a flowchart illustrating a minimum distance determination method provided in an embodiment of this application. The method includes:

[0123] S601, under the configuration of nodes on the path, determine the third distance between the robot's minimum convex hull and each convex polyhedron in the minimum convex hull of the water chamber space structure; the minimum convex hull of the water chamber space structure is the minimum convex hull corresponding to the water chamber space structure of the evaporator water chamber, and the minimum convex hull of the robot is the minimum convex hull surrounding the robot.

[0124] S602, determine the minimum value among the third distances as the minimum distance under the node.

[0125] Right now .

[0126] In this embodiment, by configuring the nodes on the path, the third distance between the robot's minimum convex hull and each convex polyhedron in the minimum convex hull of the water chamber space structure is determined, and the minimum value among the third distances is determined as the minimum distance under the node.

[0127] In one exemplary embodiment, the motion path planning method for the robotic arm inside the evaporator water chamber can be implemented as follows:

[0128] If the distance between the first node and the target node is less than the preset expansion step size, determine the current path of the robotic arm, the parent node of the first node, and the planned motion path of the robotic arm with the first node as the target node.

[0129] If the distance between the first node and the destination node is not less than the preset expansion step size, a new first node is determined, and the process of determining the neighborhood centered on the new first node is repeated until the distance between the most recently determined first node and the destination node is less than the preset expansion step size. The current path of the robotic arm, the parent node of the new first node, and the planned motion path of the robotic arm with the new first node as the center are determined.

[0130] In this embodiment, the planned movement path of the robotic arm is determined by the relationship between the distance between the first node and the target node and the preset expansion step size. This enables a more accurate determination of the path cost of neighboring nodes, so that the final determined movement path not only has the shortest distance, but also maximizes the distance between the robot and the water chamber structure, thereby avoiding collisions between the robot and the water chamber structure.

[0131] In one exemplary embodiment, such as Figure 7 As shown, Figure 7 This is a flowchart illustrating a method for determining a first node according to an embodiment of this application. The method includes:

[0132] S701: For each neighboring node in the neighborhood, if the path cost from the first node to the neighboring node is less than the path cost from the parent node of the neighboring node to the neighboring node, the parent node of the neighboring node is updated to the first node, so as to update the extended tree and obtain a new extended tree.

[0133] For example, if the path cost from the starting point to the parent node of a neighboring node to the neighboring node is greater than the path cost from the starting point to the first node to the neighboring node, then the first node can be used as the parent node of the neighboring node, thereby updating the extended tree to obtain a new extended tree.

[0134] S702, along the line connecting the second node and the new random point, determine the new first node based on the preset expansion step size; the second node is the point in the new expanded tree with the smallest distance from the new random point.

[0135] The method for determining the new first node is similar to the method for determining the first node described above, and will not be repeated here.

[0136] In this embodiment, for each neighboring node in the neighborhood, if the path cost from the first node to the neighboring node is less than the path cost from the parent node of the neighboring node to the neighboring node, the parent node of the neighboring node is updated to the first node to update the extended tree and obtain a new extended tree. Along the line connecting the second node and the new random point, a new first node is determined based on a preset extension step size, which lays the foundation for determining the parent node of the new first node based on the new first node.

[0137] In one exemplary embodiment, such as Figure 8 As shown, Figure 8 This is a schematic diagram of the overall process of a motion path planning method provided in an embodiment of this application. The method includes:

[0138] Set the following parameters: Initial joint configuration That is, the starting point configuration and the ending joint configuration. That is, the destination node configuration. Within the joint configuration space, planning is done by... arrive The path. Maximum number of iterations. Expand step size Minimum safe distance By setting the expansion step size Constraints can control the magnitude of changes in joint position between two path points.

[0139] Initialize the tree: starting from the beginning Build an extended tree .

[0140] Random sampling points: Generate random points in the joint configuration space that includes the start point and the target point. And find the node in the extended tree T that is closest to the random point. .

[0141] Node expansion: along the node With random points The direction of the line connecting from Expand by a step length Acquire new nodes .

[0142] Collision detection: In The system is configured to detect whether the robot collides with the water chamber wall. If a collision occurs, the system returns to the step corresponding to the random sampling point. If no collision occurs, the system proceeds to the step of determining the minimum distance.

[0143] Minimum distance determination: Calculation Minimum distance between the robot and the water chamber wall under configuration ,judge Is it greater than the preset minimum safe distance? If yes, proceed to the neighborhood selection step; otherwise, return to the step corresponding to the random sampling point.

[0144] Neighborhood selection: Centered on, select with The Euclidean distance between them is less than All tree nodes within the neighborhood, neighborhood radius The selection is as follows: .

[0145] in, It is the number of nodes in the current tree. It is the dimension of the configuration space (similar to the robot's degrees of freedom). Calculate experience points.

[0146] Select parent node: Calculate Each tree node in the neighborhood Path cost Select the node with the minimum path cost in the neighborhood. As The parent node, connected and .

[0147] Update the tree: Use a method similar to step 7 to traverse and check the neighborhood. Neighboring nodes If through arrive If the path cost can be reduced, then change it. The parent node is .

[0148] Determine whether a formation has been formed from arrive If the path is correct, the planning ends; otherwise, return to the step corresponding to the random sampling point.

[0149] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0150] Based on the same inventive concept, this application also provides a motion path planning device for an evaporator water chamber robotic arm to implement the motion path planning method for the robotic arm in the evaporator water chamber described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the motion path planning device for the robotic arm in the evaporator water chamber provided below can be found in the limitations of the motion path planning method for the robotic arm in the evaporator water chamber described above, and will not be repeated here.

[0151] In one exemplary embodiment, such as Figure 9 As shown, Figure 9 This is a schematic diagram of the motion path planning device 900 for a robotic arm inside an evaporator water chamber, provided in an embodiment of this application. The motion path planning device 900 includes:

[0152] The first determining module 901 is used to determine a neighborhood centered on the first node when it is determined at the first node that the robot has not collided with the water chamber wall and the minimum distance between the robot and the water chamber wall is greater than a preset safety distance; the first node is a node determined based on a preset expansion step size along the line connecting the second node and the random point; the second node is the node on the expansion tree that is closest to the random point; the robot includes a robotic arm and a blocking plate grasped by the robotic arm; and the random point is a random point generated in the joint configuration space containing the starting point and the destination node.

[0153] The second determining module 902 is used to determine the path cost of each neighboring node in the neighborhood based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node; the node group includes two adjacent nodes on the path.

[0154] The third determining module 903 is used to determine the parent node of the first node from among the neighboring nodes based on the path cost of each of the neighboring nodes.

[0155] The path planning module 904 is used to plan the motion path of the robotic arm based on the distance between the first node and the destination node, the first node and the parent node of the first node.

[0156] In an exemplary embodiment, the second determining module 902 includes:

[0157] The first determining submodule is used to determine the first square of the minimum distance under each node on the path, and the second square of the minimum distance under the first node;

[0158] The second determining submodule is used to determine the first summation result of the first reciprocal of each of the first squares and the second reciprocal of each of the second squares, and to determine the product of the first summation result and the preset distance constraint coefficient;

[0159] The third determining submodule is used to determine the path cost of the neighboring node based on the product, each of the first distances, and the second distance.

[0160] In one exemplary embodiment, the number of node groups is multiple, and the third determining submodule includes:

[0161] The first determining unit is configured to determine the second summation result between each of the first distances and the second distances;

[0162] The second determining unit is used to determine the path cost of the neighboring node based on the second summation result and the product.

[0163] In an exemplary embodiment, the second determining unit is specifically configured to determine a third summation result between the second summation result and the product, and to determine the third summation result as the path cost of the neighboring node.

[0164] In an exemplary embodiment, the third determining module 903 is specifically used to determine the neighboring node corresponding to the minimum path cost as the parent node of the first node.

[0165] In one exemplary embodiment, the motion path planning device 900 further includes:

[0166] The fourth determining module is used to determine the quotient of the logarithm of the number of nodes in the expanded tree divided by the number of nodes.

[0167] The fifth determining module is used to determine the second reciprocal of the degree of freedom and to determine the target result of the second reciprocal power of the quotient;

[0168] The sixth determining module is used to determine the neighborhood radius based on the product of the target result and a preset coefficient;

[0169] The seventh determining module is used to determine, with the first node as the center, neighboring nodes whose distance from the first node is less than the neighborhood radius to form the neighborhood.

[0170] In one exemplary embodiment, the motion path planning device 900 further includes:

[0171] The eighth determining module is used to determine, under the configuration of nodes on the path, the third distance between the robot's minimum convex hull and each convex polyhedron in the minimum convex hull of the water chamber space structure; the minimum convex hull of the water chamber space structure is the minimum convex hull corresponding to the water chamber space structure of the evaporator water chamber, and the robot's minimum convex hull is the minimum convex hull surrounding the robot; the minimum value among the third distances is determined as the minimum distance under the node.

[0172] In one exemplary embodiment, the path planning module 904 includes:

[0173] The third determining unit is used to determine the current path of the robotic arm, the parent node of the first node, and the planned motion path of the robotic arm when the distance between the first node and the target node is less than the preset expansion step size.

[0174] The fourth determining unit is used to determine a new first node when the distance between the first node and the target node is not less than the preset expansion step size, and return to execute the step of determining the neighborhood with the new first node as the center until the distance between the most recently determined first node and the target node is less than the preset expansion step size, and determine the current path of the robotic arm, the parent node of the new first node, and the planned motion path of the robotic arm with the new first node as the center.

[0175] In an exemplary embodiment, the fourth determining unit is specifically configured to, for each neighboring node in the neighborhood, update the parent node of the neighboring node to the first node if the path cost from the first node to the neighboring node is less than the path cost from the parent node of the neighboring node to the neighboring node, thereby updating the extended tree to obtain a new extended tree; determine the new first node based on a preset expansion step size along the line direction connecting the second node and the new random point; the second node is the point in the new extended tree with the smallest distance to the new random point.

[0176] Each module in the motion path planning device for the robotic arm inside the evaporator water chamber described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0177] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0178] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0179] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A motion path planning method for a robotic arm inside an evaporator water chamber, characterized in that, The method includes: If, at the first node, it is determined that the robot does not collide with the inner wall of the water chamber, and the minimum distance between the robot and the inner wall of the water chamber is greater than a preset safety distance, a neighborhood is determined with the first node as the center. The first node is a node determined based on a preset expansion step size along the line connecting the second node and the random point. The second node is the node on the expansion tree that is closest to the random point. The robot includes a robotic arm and a blockage plate grasped by the robotic arm. The random point is a random point generated in the joint configuration space containing the starting point and the destination node. For each neighboring node in the neighborhood, the path cost of the neighboring node is determined based on the first distance of the node group under the path corresponding to the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node; the node group includes two adjacent nodes on the path. Based on the path cost of each of the neighboring nodes, the parent node of the first node is determined from the neighboring nodes. Based on the distance between the first node and the target node, the first node and its parent node, the motion path of the robotic arm is planned; The step of determining the path cost of a neighboring node based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node includes: Determine the first square of the minimum distance under each node on the path, and the second square of the minimum distance under the first node; Determine the first summation result of the first reciprocal of each of the first squares and the second reciprocal of each of the second squares, and determine the product of the first summation result and the preset distance constraint coefficient; The path cost of the neighboring node is determined based on the product, each of the first distances, and the second distance; The step of planning the motion path of the robotic arm based on the distance between the first node and the target node, the first node, and the parent node of the first node includes: If the distance between the first node and the target node is less than the preset expansion step size, determine the current path of the robotic arm, the parent node of the first node, and the planned motion path of the robotic arm with the first node as the first node. If the distance between the first node and the target node is not less than the preset expansion step size, a new first node is determined, and the process of determining the neighborhood centered on the new first node is repeated until the distance between the most recently determined first node and the target node is less than the preset expansion step size. The current path of the robotic arm, the parent node of the new first node, and the planned motion path of the robotic arm based on the new first node are determined. The determination of the new first node includes: For each neighboring node in the neighborhood, if the path cost from the first node to the neighboring node is less than the path cost from the parent node of the neighboring node to the neighboring node, the parent node of the neighboring node is updated to the first node, so as to update the extended tree and obtain a new extended tree. Along the line connecting the second node and the new random point, the new first node is determined based on a preset expansion step size; the second node is the point in the new expanded tree with the smallest distance from the new random point.

2. The method according to claim 1, characterized in that, The number of node groups is multiple, and determining the path cost of the neighboring nodes based on the product, each of the first distances, and the second distance includes: Determine the second summation result between each of the first distances and the second distances; The path cost of the neighboring node is determined based on the second summation result and the product.

3. The method according to claim 2, characterized in that, Determining the path cost of the neighboring node based on the second summation result and the product includes: A third summation result is determined between the second summation result and the product, and the third summation result is determined as the path cost of the neighboring node.

4. The method according to any one of claims 1-3, characterized in that, Determining the parent node of the first node from among the neighboring nodes based on the path cost of each of the neighboring nodes includes: The neighboring node corresponding to the minimum path cost is determined as the parent node of the first node.

5. The method according to any one of claims 1-3, characterized in that, The method further includes: Determine the quotient by dividing the logarithm of the number of nodes in the expanded tree by the number of nodes; Determine the second reciprocal of the degrees of freedom, and determine the target result of the second reciprocal power of the quotient; The neighborhood radius is determined based on the product of the target result and the preset coefficient. The neighborhood is formed by taking the first node as the center and determining neighboring nodes whose distance from the first node is less than the neighborhood radius.

6. The method according to any one of claims 1-3, characterized in that, The method further includes: With the nodes configured on the path, the third distance between the robot's minimum convex hull and each convex polyhedron in the minimum convex hull of the water chamber space structure is determined; the minimum convex hull of the water chamber space structure is the minimum convex hull corresponding to the water chamber space structure of the evaporator water chamber, and the minimum convex hull of the robot is the minimum convex hull surrounding the robot. The minimum value among the aforementioned third distances is determined as the minimum distance under the node.

7. A motion path planning device for a robotic arm inside an evaporator water chamber, characterized in that, The device includes: The first determining module is used to determine a neighborhood centered on the first node when it is determined at the first node that the robot has not collided with the water chamber wall and the minimum distance between the robot and the water chamber wall is greater than a preset safety distance; the first node is a node determined based on a preset expansion step size along the line connecting the second node and the random point; the second node is the node on the expansion tree that is closest to the random point; the robot includes a robotic arm and a blocking plate grasped by the robotic arm; and the random point is a random point generated in the joint configuration space containing the starting point and the destination node. The second determining module is used to determine the path cost of each neighboring node within the neighborhood based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node; the node group includes two adjacent nodes on the path; The third determining module is used to determine the parent node of the first node from among the neighboring nodes based on the path cost of each of the neighboring nodes; The path planning module is used to plan the motion path of the robotic arm based on the distance between the first node and the destination node, the first node and the parent node of the first node; The step of determining the path cost of a neighboring node based on the first distance corresponding to the node group under the path of the neighboring node, the second distance between the neighboring node and the first node, the minimum distance under each node on the path, and the minimum distance under the first node includes: Determine the first square of the minimum distance under each node on the path, and the second square of the minimum distance under the first node; Determine the first summation result of the first reciprocal of each of the first squares and the second reciprocal of each of the second squares, and determine the product of the first summation result and the preset distance constraint coefficient; The path cost of the neighboring node is determined based on the product, each of the first distances, and the second distance; The step of planning the motion path of the robotic arm based on the distance between the first node and the target node, the first node, and the parent node of the first node includes: If the distance between the first node and the target node is less than the preset expansion step size, determine the current path of the robotic arm, the parent node of the first node, and the planned motion path of the robotic arm with the first node as the first node. If the distance between the first node and the target node is not less than the preset expansion step size, a new first node is determined, and the process of determining the neighborhood centered on the new first node is repeated until the distance between the most recently determined first node and the target node is less than the preset expansion step size. The current path of the robotic arm, the parent node of the new first node, and the planned motion path of the robotic arm based on the new first node are determined. The determination of the new first node includes: For each neighboring node in the neighborhood, if the path cost from the first node to the neighboring node is less than the path cost from the parent node of the neighboring node to the neighboring node, the parent node of the neighboring node is updated to the first node, so as to update the extended tree and obtain a new extended tree. Along the line connecting the second node and the new random point, the new first node is determined based on a preset expansion step size; the second node is the point in the new expanded tree with the smallest distance from the new random point.

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