WO2022198994A1

WO2022198994A1 - Robot arm motion planning method and apparatus, and readable storage medium and robot arm

Info

Publication number: WO2022198994A1
Application number: PCT/CN2021/124620
Authority: WO
Inventors: 陈金亮; 刘益彰; 熊友军
Original assignee: 深圳市优必选科技股份有限公司
Priority date: 2021-03-22
Filing date: 2021-10-19
Publication date: 2022-09-29
Also published as: CN113119116A; CN113119116B

Abstract

The present application belongs to the technical field of robots, and particularly relates to a robot arm motion planning method and apparatus, and a computer-readable storage medium and a robot arm. The method comprises: determining a virtual optimal motion planning path of a robot arm moving from a preset initial pose to a preset target pose; performing random sampling in a free space of the robot arm, so as to obtain a first random pose of the robot arm; performing iterative optimization on the first random pose according to the virtual optimal motion planning path, so as to obtain an optimized second random pose; and taking the second random pose as a random sampling result in a preset robot arm motion planning algorithm to perform motion planning on the robot arm. By means of the present application, a virtual optimal motion planning path is taken as a guidance basis during random sampling, and iterative optimization is performed on an original random pose obtained by means of the random sampling, such that a random sampling result has an obvious guidance quality, and a large number of invalid searches can be reduced, thereby greatly improving the planning efficiency.

Description

一种机械臂运动规划方法、装置、可读存储介质及机械臂A robotic arm motion planning method, device, readable storage medium, and robotic arm

本申请要求于2021年03月22日在中国专利局提交的、申请号为202110301669.4的中国专利申请的优先权，其全部内容通过引用结合在本申请中。This application claims the priority of the Chinese Patent Application No. 202110301669.4 filed with the Chinese Patent Office on March 22, 2021, the entire contents of which are incorporated herein by reference.

技术领域technical field

本申请属于机器人技术领域，尤其涉及一种机械臂运动规划方法、装置、计算机可读存储介质及机械臂。The present application belongs to the field of robotics technology, and in particular relates to a method, device, computer-readable storage medium and a robotic arm for motion planning of a robotic arm.

背景技术Background technique

在现有技术中，一般是使用快速扩展随机树(Rapidly-exploring Random Trees，RRT)或者双向快速扩展随机树(Bi-directional RRT，BiRRT)等规划算法对机械臂进行运动规划。但这些算法是在整个自由空间中随机均匀采样，缺乏导向性，存在大量的无效搜索，效率较低。In the prior art, planning algorithms such as Rapidly-exploring Random Trees (RRT) or Bi-directional Rapidly Expanding Random Trees (Bi-directional RRT, BiRRT) are generally used to plan the motion of the robotic arm. However, these algorithms are random and uniform sampling in the entire free space, lack of guidance, there are a large number of invalid searches, and the efficiency is low.

技术问题technical problem

有鉴于此，本申请实施例提供了一种机械臂运动规划方法、装置、计算机可读存储介质及机械臂，以解决现有的机械臂运动规划方法缺乏导向性、效率较低的问题。In view of this, the embodiments of the present application provide a robotic arm motion planning method, device, computer-readable storage medium, and robotic arm to solve the problems of lack of guidance and low efficiency in the existing robotic arm motion planning methods.

技术解决方案technical solutions

本申请实施例的第一方面提供了一种机械臂运动规划方法，可以包括：A first aspect of the embodiments of the present application provides a method for planning a motion of a robotic arm, which may include:

确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径；Determine the virtual optimal motion planning path for the robotic arm to move from the preset initial pose to the preset target pose;

在所述机械臂的自由空间中进行随机采样，得到所述机械臂的第一随机位姿；Perform random sampling in the free space of the robotic arm to obtain the first random pose of the robotic arm;

根据所述虚拟最优运动规划路径对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿；Iteratively optimizes the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose;

将所述第二随机位姿作为预设的机械臂运动规划算法中的随机采样结果对所述机械臂进行运动规划。The second random pose is used as a random sampling result in a preset robotic arm motion planning algorithm to perform motion planning for the robotic arm.

进一步地，所述根据所述虚拟最优运动规划路径对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿，可以包括：Further, the iterative optimization of the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose may include:

将所述第一随机位姿映射至所述虚拟最优运动规划路径上，得到与所述第一随机位姿对应的虚拟最优位姿；mapping the first random pose to the virtual optimal motion planning path to obtain a virtual optimal pose corresponding to the first random pose;

建立与所述虚拟最优位姿对应的引力场函数；establishing a gravitational field function corresponding to the virtual optimal pose;

根据所述引力场函数对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿。The first random pose is iteratively optimized according to the gravitational field function to obtain an optimized second random pose.

进一步地，所述根据所述引力场函数对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿，可以包括：Further, the iterative optimization of the first random pose according to the gravitational field function to obtain an optimized second random pose may include:

将所述第一随机位姿作为当前随机位姿；taking the first random pose as the current random pose;

根据所述引力场函数计算所述当前随机位姿的梯度；Calculate the gradient of the current random pose according to the gravitational field function;

根据当前随机位姿的梯度对所述当前随机位姿进行更新计算，得到更新随机位姿；The current random pose is updated and calculated according to the gradient of the current random pose to obtain an updated random pose;

若所述当前随机位姿的梯度不满足预设的迭代终止条件，则将所述更新随机位姿作为新的当前随机位姿，并返回执行所述根据所述引力场函数计算所述当前随机位姿的梯度的步骤及其后续步骤；If the gradient of the current random pose does not meet the preset iteration termination condition, the updated random pose is taken as the new current random pose, and the execution of the calculation of the current random pose according to the gravitational field function is returned to. The steps of the gradient of the pose and its subsequent steps;

若所述当前随机位姿的梯度满足所述迭代终止条件，则将所述更新随机位姿确定为优化后的第二随机位姿。If the gradient of the current random pose satisfies the iteration termination condition, the updated random pose is determined as the optimized second random pose.

进一步地，所述建立与所述虚拟最优位姿对应的引力场函数，可以包括：Further, the establishing the gravitational field function corresponding to the virtual optimal pose may include:

根据下式建立所述引力场函数：The gravitational field function is established according to the following formula:

其中，q _goal为所述虚拟最优位姿，q为所述当前随机位姿，

为预设的尺度因子，d(q,q _goal)为q与q _goal之间的距离，

为预设的距离阈值，U _att(q)为所述引力场函数。 Wherein, q _goal is the virtual optimal pose, q is the current random pose,

is the preset scale factor, d(q,q _goal ) is the distance between q and q _goal ,

is the preset distance threshold, and U _att (q) is the gravitational field function.

进一步地，所述根据所述引力场函数计算所述当前随机位姿的梯度，可以包括：Further, calculating the gradient of the current random pose according to the gravitational field function may include:

根据下式计算所述当前随机位姿的梯度：The gradient of the current random pose is calculated according to the following formula:

其中，

为所述当前随机位姿的梯度。 in,

is the gradient of the current random pose.

进一步地，所述根据当前随机位姿的梯度对所述当前随机位姿进行更新计算，得到更新随机位姿，可以包括：Further, the update calculation of the current random pose according to the gradient of the current random pose to obtain an updated random pose may include:

根据下式对所述当前随机位姿进行更新计算：The current random pose is updated and calculated according to the following formula:

其中，q为所述当前随机位姿，

为所述当前随机位姿的梯度，η为预设的更 Among them, q is the current random pose,

is the gradient of the current random pose, η is the preset more

新步长，q′为所述更新随机位姿。The new step size, q' is the updated random pose.

进一步地，所述确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径，可以包括：Further, the determining the virtual optimal motion planning path of the robotic arm moving from the preset initial pose to the preset target pose may include:

将连接所述初始位姿和所述目标位姿的直线路径确定为所述虚拟最优运动规划路径。A straight path connecting the initial pose and the target pose is determined as the virtual optimal motion planning path.

本申请实施例的第二方面提供了一种机械臂运动规划装置，可以包括：A second aspect of the embodiments of the present application provides a robotic arm motion planning device, which may include:

虚拟路径确定模块，用于确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径；The virtual path determination module is used to determine the virtual optimal motion planning path of the robot arm moving from the preset initial pose to the preset target pose;

随机采样模块，用于在所述机械臂的自由空间中进行随机采样，得到所述机械臂的第一随机位姿；a random sampling module for performing random sampling in the free space of the robotic arm to obtain the first random pose of the robotic arm;

迭代优化模块，用于根据所述虚拟最优运动规划路径对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿；an iterative optimization module, configured to iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose;

运动规划模块，用于将所述第二随机位姿作为预设的机械臂运动规划算法中的随机采样结果对所述机械臂进行运动规划。A motion planning module, configured to use the second random pose as a random sampling result in a preset motion planning algorithm of the manipulator to perform motion planning for the manipulator.

进一步地，所述迭代优化模块可以包括：Further, the iterative optimization module may include:

位姿映射单元，用于将所述第一随机位姿映射至所述虚拟最优运动规划路径上，得到与所述第一随机位姿对应的虚拟最优位姿；a pose mapping unit, configured to map the first random pose onto the virtual optimal motion planning path to obtain a virtual optimal pose corresponding to the first random pose;

引力场函数建立单元，用于建立与所述虚拟最优位姿对应的引力场函数；a gravitational field function establishment unit, configured to establish a gravitational field function corresponding to the virtual optimal pose;

迭代优化单元，用于根据所述引力场函数对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿。An iterative optimization unit, configured to iteratively optimize the first random pose according to the gravitational field function to obtain an optimized second random pose.

进一步地，所述迭代优化单元可以包括：Further, the iterative optimization unit may include:

当前随机位姿确定子单元，用于将所述第一随机位姿作为当前随机位姿；The current random pose determination subunit is used to use the first random pose as the current random pose;

梯度计算子单元，用于根据所述引力场函数计算所述当前随机位姿的梯度；a gradient calculation subunit, configured to calculate the gradient of the current random pose according to the gravitational field function;

更新计算子单元，用于根据当前随机位姿的梯度对所述当前随机位姿进行更新计算，得到更新随机位姿；an update calculation subunit, configured to update and calculate the current random pose according to the gradient of the current random pose to obtain an updated random pose;

当前随机位姿更新子单元，用于若所述当前随机位姿的梯度不满足预设的迭代终止条件，则将所述更新随机位姿作为新的当前随机位姿；The current random pose update subunit is configured to use the updated random pose as a new current random pose if the gradient of the current random pose does not meet the preset iteration termination condition;

第二随机位姿确定子单元，用于若所述当前随机位姿的梯度满足所述迭代终止条件，则将所述更新随机位姿确定为优化后的第二随机位姿。The second random pose determination subunit is configured to determine the updated random pose as the optimized second random pose if the gradient of the current random pose satisfies the iteration termination condition.

进一步地，所述引力场函数建立单元具体用于根据下式建立所述引力场函数：Further, the gravitational field function establishment unit is specifically configured to establish the gravitational field function according to the following formula:

其中，q _goal为所述虚拟最优位姿，q为所述当前随机位姿，

为预设的尺度因子，d(q,q _goal)为q与q _goal之间的距离，

进一步地，所述梯度计算子单元具体用于根据下式计算所述当前随机位姿的梯度：Further, the gradient calculation subunit is specifically configured to calculate the gradient of the current random pose according to the following formula:

其中，

为所述当前随机位姿的梯度。 in,

is the gradient of the current random pose.

进一步地，所述更新计算子单元具体用于根据下式对所述当前随机位姿进行更新计算：Further, the update calculation subunit is specifically configured to update and calculate the current random pose according to the following formula:

其中，q为所述当前随机位姿，

为所述当前随机位姿的梯度，η为预设的更新步长，q′为所述更新随机位姿。 Among them, q is the current random pose,

is the gradient of the current random pose, η is the preset update step size, and q′ is the updated random pose.

进一步地，所述虚拟路径确定模块具体用于将连接所述初始位姿和所述目标位姿的直线路径确定为所述虚拟最优运动规划路径。Further, the virtual path determination module is specifically configured to determine a straight line path connecting the initial pose and the target pose as the virtual optimal motion planning path.

本申请实施例的第三方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述任一种机械臂运动规划方法的步骤。A third aspect of the embodiments of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any one of the above-mentioned robotic arm motion planning methods is implemented. step.

本申请实施例的第四方面提供了一种机械臂，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述任一种机械臂运动规划方法的步骤。A fourth aspect of the embodiments of the present application provides a robotic arm, including a memory, a processor, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program The steps of implementing any one of the above-mentioned robotic arm motion planning methods.

本申请实施例的第五方面提供了一种计算机程序产品，当计算机程序产品在机械臂上运行时，使得机械臂执行上述任一种机械臂运动规划方法的步骤。A fifth aspect of the embodiments of the present application provides a computer program product, which, when the computer program product runs on a robotic arm, causes the robotic arm to execute the steps of any of the above-mentioned methods for motion planning of a robotic arm.

有益效果beneficial effect

本申请实施例与现有技术相比存在的有益效果是：本申请实施例确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径；在所述机械臂的自由空间中进行随机采样，得到所述机械臂的第一随机位姿；根据所述虚拟最优运动规划路径对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿；将所述第二随机位姿作为预设的机械臂运动规划算法中的随机采样结果对所述机械臂进行运动规划。通过本申请实施例，以理想状态的虚拟最优运动规划路径作为随机采样时的导向依据，对随机采样得到的原始随机位姿(即第一随机位姿)进行迭代优化，从而将其向虚拟最优运动规划路径的方向上牵引，得到优化后的随机位姿(即第二随机位姿)，这样得到的随机采样结果具有明显的导向性，基于这样的随机采样结果对机械臂进行运动规划，能够减少大量的无效搜索，极大提升了规划效率。The beneficial effects of the embodiments of the present application compared with the prior art are: the embodiments of the present application determine a virtual optimal motion planning path for the robotic arm to move from a preset initial pose to a preset target pose; Perform random sampling in the free space of the arm to obtain the first random pose of the robotic arm; iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose pose; use the second random pose as a random sampling result in a preset robotic arm motion planning algorithm to perform motion planning for the robotic arm. Through the embodiment of the present application, the virtual optimal motion planning path in the ideal state is used as the guiding basis for random sampling, and the original random pose (ie the first random pose) obtained by random sampling is iteratively optimized, so that it is transferred to the virtual Traction in the direction of the optimal motion planning path to obtain the optimized random pose (ie, the second random pose), and the random sampling result obtained in this way has obvious orientation. Based on such random sampling results, the motion planning of the robotic arm is carried out. , which can reduce a large number of invalid searches and greatly improve the planning efficiency.

附图说明Description of drawings

为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only for the present application. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

图1为本申请实施例中一种机械臂运动规划方法的一个实施例流程图；1 is a flowchart of an embodiment of a method for planning a motion of a robotic arm in an embodiment of the present application;

图2为根据虚拟最优运动规划路径对第一随机位姿进行迭代优化的示意流程图；2 is a schematic flowchart of iterative optimization of a first random pose according to a virtual optimal motion planning path;

图3为根据引力场函数对第一随机位姿进行迭代优化的示意流程图；FIG. 3 is a schematic flowchart of iterative optimization of the first random pose according to the gravitational field function;

图4为本申请实施例中一种机械臂运动规划装置的一个实施例结构图；4 is a structural diagram of an embodiment of a robotic arm motion planning device in an embodiment of the application;

图5为本申请实施例中一种机械臂的示意框图。FIG. 5 is a schematic block diagram of a robotic arm in an embodiment of the present application.

本发明的实施方式Embodiments of the present invention

为使得本申请的发明目的、特征、优点能够更加的明显和易懂，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，下面所描述的实施例仅仅是本申请一部分实施例，而非全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本申请保护的范围。In order to make the purpose, features and advantages of the invention of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the following The described embodiments are only some, but not all, embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

应当理解，当在本说明书和所附权利要求书中使用时，术语“包括”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。It is to be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described feature, integer, step, operation, element and/or component, but does not exclude one or more other features , whole, step, operation, element, component and/or the presence or addition of a collection thereof.

还应当理解，在此本申请说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本申请。如在本申请说明书和所附权利要求书中所使用的那样，除非上下文清楚地指明其它情况，否则单数形式的“一”、“一个”及“该”意在包括复数形式。It should also be understood that the terminology used in the specification of the application herein is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural unless the context clearly dictates otherwise.

还应当进一步理解，在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合。It should also be further understood that, as used in this specification and the appended claims, the term "and/or" refers to and including any and all possible combinations of one or more of the associated listed items .

如在本说明书和所附权利要求书中所使用的那样，术语“如果”可以依据上下文被解释为“当...时”或“一旦”或“响应于确定”或“响应于检测到”。类似地，短语“如果确定”或“如果检测到[所描述条件或事件]”可以依据上下文被解释为意指“一旦确定”或“响应于确定”或“一旦检测到[所描述条件或事件]”或“响应于检测到[所描述条件或事件]”。As used in this specification and the appended claims, the term "if" may be contextually interpreted as "when" or "once" or "in response to determining" or "in response to detecting" . Similarly, the phrases "if it is determined" or "if the [described condition or event] is detected" may be interpreted, depending on the context, to mean "once it is determined" or "in response to the determination" or "once the [described condition or event] is detected. ]" or "in response to detection of the [described condition or event]".

另外，在本申请的描述中，术语“第一”、“第二”、“第三”等仅用于区分描述，而不能理解为指示或暗示相对重要性。In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

请参阅图1，本申请实施例中一种机械臂运动规划方法的一个实施例可以包括：Referring to FIG. 1, an embodiment of a robotic arm motion planning method in the embodiment of the present application may include:

步骤S101、确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径。Step S101 , determining a virtual optimal motion planning path for the robotic arm to move from a preset initial pose to a preset target pose.

在本申请实施例中，可以通过位姿空间(configuration space)描述机械臂所有关节的位姿构成的集合，即C空间，而当考虑机械臂和障碍物时，C空间会分为两个空间，即障碍物空间C _Obs和自由空间C _free。 In this embodiment of the present application, the set composed of the poses of all joints of the manipulator can be described by the configuration space, that is, the C space, and when the manipulator and obstacles are considered, the C space is divided into two spaces , namely the obstacle space C _Obs and the free space C _free .

其中，C _Obs可以用下式进行描述： Among them, C _Obs can be described by the following formula:

即C _Obs为满足以下条件的位姿构成的集合：该位姿(即q)属于C空间且在该位姿下的机械臂(即Robot(q))与障碍物(即Obs)相交不为空，两者发生碰撞。 That is, C _Obs is a set of poses that satisfy the following conditions: the pose (ie q) belongs to the C space and the robot arm (ie Robot(q)) under this pose and the obstacle (ie Obs) do not intersect empty, the two collide.

相应地，C _free可以用下式进行描述： Correspondingly, C _free can be described by the following formula:

C _free＝C-C _Obs C _free = CC _Obs

即C _free为C _Obs的补集。 That is, C _free is the complement of C _Obs .

C _free是机械臂能够在其中运动并发现轨迹最优、时间最短的可选区域。机械臂运动规划算法的基本思想是基于C _free建立用于解决机械臂运动规划问题的路径图，其具体流程可以包括： C _free is an optional area in which the robotic arm can move and find the optimal trajectory and the shortest time. The basic idea of the manipulator motion planning algorithm is to establish a path diagram for solving the manipulator motion planning problem based on C _free . The specific process can include:

(1)确定机械臂的工作空间W；(1) Determine the workspace W of the robotic arm;

(2)确定工作空间W中的障碍物Obs以及机械臂Robot；(2) Determine the obstacles Obs in the workspace W and the robotic arm Robot;

(3)确定机械臂对应的C空间，以及障碍物空间C _Obs和自由空间C _free； (3) Determine the C space corresponding to the robotic arm, as well as the obstacle space C _Obs and the free space C _free ;

(4)确定机械臂的初始位姿q _init和目标位姿q _end； (4) Determine the initial pose q _init and the target pose q _end of the robotic arm;

(5)在自由空间C _free中根据机械臂运动规划算法采样获得机械臂的无碰撞路径τ，该路径需满足τ[0,1]→C _free，其中，0表示运动规划的起始时间点，1表示运动规划的终止时间点，该路径在从起始时间点至终止时间点之间的任意一个规划时间点时的机械臂位姿均处在自由空间C _free的范围内，且该路径在起始时间点时的机械臂位姿为初始位姿，即τ[0]＝q _init，该路径在终止时间点时的机械臂位姿为目标位姿，即τ[1]＝q _end。 (5) The collision-free path τ of the manipulator is obtained by sampling according to the motion planning algorithm of the manipulator in the free space C _free , and the path must satisfy τ[0,1]→C _free , where 0 represents the starting time point of the motion planning , 1 indicates the termination time point of the motion planning, the robot arm pose at any planning time point between the start time point and the termination time point of the path is within the range of the free space C _free , and the path The pose of the robotic arm at the start time point is the initial pose, that is, τ[0]=q _init , and the pose of the robotic arm at the end time point of the path is the target pose, that is, τ[1]=q _end .

在本申请实施例中，可以将连接初始位姿和目标位姿的直线路径确定为虚拟最优运动规划路径。需要注意的是，这条虚拟最优运动规划路径仅仅只是一条理想状态下的最优路径，因为这条路径可能会通过障碍物空间，因此在实际中往往并不可行。但是该路径可以作为实际的运动规划的导向依据，减少运动规划过程中无方向的随机搜索。In this embodiment of the present application, a straight path connecting the initial pose and the target pose may be determined as the virtual optimal motion planning path. It should be noted that this virtual optimal motion planning path is only an optimal path in an ideal state, because this path may pass through the obstacle space, so it is often not feasible in practice. However, the path can be used as the guiding basis for the actual motion planning, reducing the random search without direction in the process of motion planning.

步骤S102、在机械臂的自由空间中进行随机采样，得到机械臂的第一随机位姿。In step S102, random sampling is performed in the free space of the manipulator to obtain a first random pose of the manipulator.

在本申请实施例中，可以采用现有技术中的任意一种方式在自由空间中进行随机采样。为了便于区分，此处将采用现有技术进行随机采样得到的结果记为第一随机位姿。In the embodiments of the present application, random sampling in free space may be performed in any manner in the prior art. In order to facilitate the distinction, the result obtained by random sampling using the prior art is recorded as the first random pose.

步骤S103、根据虚拟最优运动规划路径对第一随机位姿进行迭代优化，得到优化后的第二随机位姿。Step S103 , iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose.

如图2所示，步骤S103具体可以包括如下过程：As shown in FIG. 2, step S103 may specifically include the following process:

步骤S1031、将第一随机位姿映射至虚拟最优运动规划路径上，得到与第一随机位姿对应的虚拟最优位姿。Step S1031 , mapping the first random pose to the virtual optimal motion planning path to obtain a virtual optimal pose corresponding to the first random pose.

在本申请实施例中，可以将第一随机位姿在虚拟最优运动规划路径上的投影作为虚拟最优位姿。In this embodiment of the present application, the projection of the first random pose on the virtual optimal motion planning path may be used as the virtual optimal pose.

步骤S1032、建立与虚拟最优位姿对应的引力场函数。Step S1032, establishing a gravitational field function corresponding to the virtual optimal pose.

具体地，可以根据下式建立引力场函数：Specifically, the gravitational field function can be established according to the following formula:

其中，q _goal为虚拟最优位姿，q为当前随机位姿，

为预设的尺度因子，其具体取值可以根据实际情况进行设置，d(q,q _goal)为q与q _goal之间的距离，

为预设的距离阈值，其具体取值可以根据实际情况进行设置，U _att(q)为引力场函数。从上式中可以看出，引力场函数是个分段函数，当

时，引力势能的大小与当前随机位姿到虚拟最优位姿的距离的平方成正比；而当

时，降低引力计算函数的取值，从而避免当前随机位姿距离虚拟最优位姿较远时引力过大的问题。 Among them, q _goal is the virtual optimal pose, q is the current random pose,

is a preset scale factor, and its specific value can be set according to the actual situation, d(q,q _goal ) is the distance between q and q _goal ,

is the preset distance threshold, and its specific value can be set according to the actual situation, U _att (q) is the gravitational field function. It can be seen from the above formula that the gravitational field function is a piecewise function, when

When , the magnitude of gravitational potential energy is proportional to the square of the distance from the current random pose to the virtual optimal pose; and when

When , reduce the value of the gravitational calculation function, so as to avoid the problem of excessive gravitational force when the current random pose is far from the virtual optimal pose.

步骤S1033、根据引力场函数对第一随机位姿进行迭代优化，得到优化后的第二随机位姿。Step S1033: Perform iterative optimization on the first random pose according to the gravitational field function to obtain an optimized second random pose.

如图3所示，步骤S1033具体可以包括如下过程：As shown in FIG. 3, step S1033 may specifically include the following process:

步骤S10331、将第一随机位姿作为当前随机位姿。Step S10331, taking the first random pose as the current random pose.

步骤S10332、根据引力场函数计算当前随机位姿的梯度。Step S10332: Calculate the gradient of the current random pose according to the gravitational field function.

具体地，可以将引力场函数对距离求导，从而得到如下式所示的梯度计算公式：Specifically, the gravitational field function can be derived from the distance to obtain the gradient calculation formula shown in the following formula:

其中，

为当前随机位姿的梯度。 in,

is the gradient of the current random pose.

步骤S10333、根据当前随机位姿的梯度对当前随机位姿进行更新计算，得到更新随机位姿。Step S10333: Perform update calculation on the current random pose according to the gradient of the current random pose to obtain an updated random pose.

具体地，可以根据下式对当前随机位姿进行更新计算：Specifically, the current random pose can be updated and calculated according to the following formula:

其中，q为当前随机位姿，

为当前随机位姿的梯度，η为预设的更新步长，其具体取值可以根据实际情况进行设置，q′为更新随机位姿。由于梯度方向是函数上升最快的方向，通过梯度迭代可以获取局部最优解。 Among them, q is the current random pose,

is the gradient of the current random pose, η is the preset update step size, and its specific value can be set according to the actual situation, and q' is the update random pose. Since the gradient direction is the fastest rising direction of the function, the local optimal solution can be obtained through gradient iteration.

步骤S10334、判断当前随机位姿的梯度是否满足预设的迭代终止条件。Step S10334: Determine whether the gradient of the current random pose satisfies a preset iteration termination condition.

在本申请实施例中，迭代终止条件可以为：当前规划路径的梯度小于预设的梯度阈值，或迭代优化时长大于预设的时长阈值。其中，梯度阈值和时长阈值的具体取值均可以根据实际情况进行设置。通过这样的方式，可以对迭代优化时长进行控制，从而保证在向引力场方向扩展的同时不至于因此耗费过多的时间而影响整体的采样效率。In this embodiment of the present application, the iteration termination condition may be: the gradient of the current planned path is less than a preset gradient threshold, or the iterative optimization duration is greater than a preset duration threshold. The specific values of the gradient threshold and the duration threshold can be set according to actual conditions. In this way, the duration of iterative optimization can be controlled, so as to ensure that the overall sampling efficiency will not be affected due to excessive time consumption while expanding in the direction of the gravitational field.

若当前随机位姿的梯度不满足预设的迭代终止条件，则执行步骤S10335；若当前随机位姿的梯度满足迭代终止条件，则执行步骤S10336。If the gradient of the current random pose does not meet the preset iteration termination condition, step S10335 is performed; if the gradient of the current random pose meets the iteration termination condition, step S10336 is performed.

步骤S10335、将更新随机位姿作为新的当前随机位姿。Step S10335, taking the updated random pose as the new current random pose.

在执行步骤S10335之后，返回执行步骤S10332及其后续步骤。After executing step S10335, return to executing step S10332 and its subsequent steps.

步骤S10336、将更新随机位姿确定为优化后的随机位姿。Step S10336: Determine the updated random pose as the optimized random pose.

为了便于区分，此处将优化后的随机位姿记为第二随机位姿。For the convenience of distinction, the optimized random pose is recorded as the second random pose here.

步骤S104、将第二随机位姿作为预设的机械臂运动规划算法中的随机采样结果对机械臂进行运动规划。Step S104 , using the second random pose as a random sampling result in the preset robotic arm motion planning algorithm to perform motion planning for the robotic arm.

在本申请实施例中，可以使用现有技术中的任意一种机械臂运动规划算法来对机械臂进行运动规划，包括但不限于RRT或BiRRT等具体的规划算法，具体的规划过程可以参见现有技术内容，此处对其不再赘述。但需要注意的是，本申请实施例在现有的机械臂运动规划算法的基础上，对其中的随机采样的过程进行了优化，对于每一次的随机采样，均以优化后的第二随机位姿来代替原始的第一随机位姿作为随机采样结果，并基于这一随机采样结果对机械臂进行运动规划。In this embodiment of the present application, any manipulator motion planning algorithm in the prior art can be used to plan the motion of the manipulator, including but not limited to specific planning algorithms such as RRT or BiRRT. For the specific planning process, please refer to the present There are technical contents, which will not be repeated here. However, it should be noted that the embodiment of the present application optimizes the random sampling process based on the existing robotic arm motion planning algorithm. For each random sampling, the optimized second random bit is used. The original first random pose is used as the random sampling result, and the motion planning of the robotic arm is performed based on this random sampling result.

综上，本申请实施例以理想状态的虚拟最优运动规划路径作为随机采样时的导向依据，对随机采样得到的原始随机位姿(即第一随机位姿)进行迭代优化，从而将其向虚拟最优运动规划路径的方向上牵引，得到优化后的随机位姿(即第二随机位姿)，这样得到的随机采样结果具有明显的导向性，基于这样的随机采样结果对机械臂进行运动规划，能够减少大量的无效搜索，极大提升了规划效率。To sum up, the embodiment of the present application uses the ideal virtual optimal motion planning path as the guiding basis for random sampling, and performs iterative optimization on the original random pose (ie, the first random pose) obtained by random sampling, so as to make it a Traction in the direction of the virtual optimal motion planning path to obtain the optimized random pose (ie the second random pose), the random sampling result obtained in this way has obvious orientation, and the robotic arm is moved based on the random sampling result. Planning can reduce a large number of invalid searches and greatly improve the planning efficiency.

应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

对应于上文实施例所述的一种机械臂运动规划方法，图4示出了本申请实施例提供的一种机械臂运动规划装置的一个实施例结构图。Corresponding to the robotic arm motion planning method described in the above embodiment, FIG. 4 shows a structural diagram of an embodiment of a robotic arm motion planning apparatus provided in an embodiment of the present application.

本实施例中，一种机械臂运动规划装置可以包括：In this embodiment, a robotic arm motion planning device may include:

虚拟路径确定模块401，用于确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径；A virtual path determination module 401, configured to determine a virtual optimal motion planning path for the robotic arm to move from a preset initial pose to a preset target pose;

随机采样模块402，用于在所述机械臂的自由空间中进行随机采样，得到所述机械臂的第一随机位姿；A random sampling module 402, configured to perform random sampling in the free space of the robotic arm to obtain a first random pose of the robotic arm;

迭代优化模块403，用于根据所述虚拟最优运动规划路径对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿；an iterative optimization module 403, configured to iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose;

运动规划模块404，用于将所述第二随机位姿作为预设的机械臂运动规划算法中的随机采样结果对所述机械臂进行运动规划。The motion planning module 404 is configured to use the second random pose as a random sampling result in a preset motion planning algorithm of the manipulator to perform motion planning for the manipulator.

其中，q _goal为所述虚拟最优位姿，q为所述当前随机位姿，

为预设的尺度因子，d(q,q _goal)为q与q _goal之间的距离，

其中，

为所述当前随机位姿的梯度。 in,

is the gradient of the current random pose.

其中，q为所述当前随机位姿，

所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的装置，模块和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described devices, modules and units can be referred to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

图5示出了本申请实施例提供的一种机械臂的示意框图，为了便于说明，仅示出了与本申请实施例相关的部分。FIG. 5 shows a schematic block diagram of a robotic arm provided by an embodiment of the present application. For the convenience of description, only parts related to the embodiment of the present application are shown.

如图5所示，该实施例的机械臂5包括：处理器50、存储器51以及存储在所述存储器51中并可在所述处理器50上运行的计算机程序52。所述处理器50执行所述计算机程序52时实现上述各个机械臂运动规划方法实施例中的步骤，例如图1所示的步骤S101至步骤S104。或者，所述处理器50执行所述计算机程序52时实现上述各装置实施例中各模块/单元的功能，例如图4所示模块401至模块404的功能。As shown in FIG. 5 , the robotic arm 5 of this embodiment includes: a processor 50 , a memory 51 , and a computer program 52 stored in the memory 51 and executable on the processor 50 . When the processor 50 executes the computer program 52 , the steps in each of the above embodiments of the robotic arm motion planning method are implemented, for example, steps S101 to S104 shown in FIG. 1 . Alternatively, when the processor 50 executes the computer program 52, the functions of the modules/units in each of the foregoing apparatus embodiments, such as the functions of the modules 401 to 404 shown in FIG. 4, are implemented.

示例性的，所述计算机程序52可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器51中，并由所述处理器50执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序52在所述机械臂5中的执行过程。Exemplarily, the computer program 52 can be divided into one or more modules/units, and the one or more modules/units are stored in the memory 51 and executed by the processor 50 to complete the this application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 52 in the robotic arm 5 .

本领域技术人员可以理解，图5仅仅是机械臂5的示例，并不构成对机械臂5的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述机械臂5还可以包括输入输出设备、网络接入设备、总线等。Those skilled in the art can understand that FIG. 5 is only an example of the robot arm 5, and does not constitute a limitation to the robot arm 5. It may include more or less components than the one shown, or combine some components, or different components For example, the robotic arm 5 may further include an input and output device, a network access device, a bus, and the like.

所述处理器50可以是中央处理单元(Central Processing Unit，CPU)，还可以是其它通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列 (Field-Programmable Gate Array，FPGA)或者其它可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。The processor 50 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

所述存储器51可以是所述机械臂5的内部存储单元，例如机械臂5的硬盘或内存。所述存储器51也可以是所述机械臂5的外部存储设备，例如所述机械臂5上配备的插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。进一步地，所述存储器51还可以既包括所述机械臂5的内部存储单元也包括外部存储设备。所述存储器51用于存储所述计算机程序以及所述机械臂5所需的其它程序和数据。所述存储器51还可以用于暂时地存储已经输出或者将要输出的数据。The memory 51 may be an internal storage unit of the robotic arm 5 , such as a hard disk or a memory of the robotic arm 5 . The memory 51 may also be an external storage device of the robotic arm 5, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) equipped on the robotic arm 5 card, Flash Card, etc. Further, the memory 51 may also include both an internal storage unit of the robotic arm 5 and an external storage device. The memory 51 is used to store the computer program and other programs and data required by the robotic arm 5 . The memory 51 can also be used to temporarily store data that has been output or will be output.

所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述***中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

在本申请所提供的实施例中，应该理解到，所揭露的装置/机械臂和方法，可以通过其它的方式实现。例如，以上所描述的装置/机械臂实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个***，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。In the embodiments provided in this application, it should be understood that the disclosed apparatus/robot and method may be implemented in other ways. For example, the device/manipulator embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units. Or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读存储介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读存储介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读存储介质不包括电载波信号和电信信号。The integrated modules/units, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) ), random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer-readable Storage media exclude electrical carrier signals and telecommunications signals.

以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the above-mentioned embodiments, those of ordinary skill in the art should understand that: it can still be used for the above-mentioned implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the application, and should be included in the within the scope of protection of this application.

Claims

一种机械臂运动规划方法，其特征在于，包括：A method for motion planning of a robotic arm, comprising:

确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径；Determine the virtual optimal motion planning path for the robotic arm to move from the preset initial pose to the preset target pose;

在所述机械臂的自由空间中进行随机采样，得到所述机械臂的第一随机位姿；Perform random sampling in the free space of the robotic arm to obtain the first random pose of the robotic arm;

根据所述虚拟最优运动规划路径对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿；Iteratively optimizes the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose;

将所述第二随机位姿作为预设的机械臂运动规划算法中的随机采样结果对所述机械臂进行运动规划。The second random pose is used as a random sampling result in a preset robotic arm motion planning algorithm to perform motion planning for the robotic arm.
根据权利要求1所述的机械臂运动规划方法，其特征在于，所述根据所述虚拟最优运动规划路径对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿，包括：The robotic arm motion planning method according to claim 1, wherein the first random pose is iteratively optimized according to the virtual optimal motion planning path to obtain an optimized second random pose, include:

将所述第一随机位姿映射至所述虚拟最优运动规划路径上，得到与所述第一随机位姿对应的虚拟最优位姿；mapping the first random pose to the virtual optimal motion planning path to obtain a virtual optimal pose corresponding to the first random pose;

建立与所述虚拟最优位姿对应的引力场函数；establishing a gravitational field function corresponding to the virtual optimal pose;

根据所述引力场函数对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿。The first random pose is iteratively optimized according to the gravitational field function to obtain an optimized second random pose.
根据权利要求2所述的机械臂运动规划方法，其特征在于，所述根据所述引力场函数对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿，包括：The robotic arm motion planning method according to claim 2, wherein the iterative optimization of the first random pose according to the gravitational field function to obtain an optimized second random pose, comprising:

将所述第一随机位姿作为当前随机位姿；taking the first random pose as the current random pose;

根据所述引力场函数计算所述当前随机位姿的梯度；Calculate the gradient of the current random pose according to the gravitational field function;

根据当前随机位姿的梯度对所述当前随机位姿进行更新计算，得到更新随机位姿；The current random pose is updated and calculated according to the gradient of the current random pose to obtain an updated random pose;

若所述当前随机位姿的梯度不满足预设的迭代终止条件，则将所述更新随机位姿作为新的当前随机位姿，并返回执行所述根据所述引力场函数计算所述当前随机位姿的梯度的步骤及其后续步骤；If the gradient of the current random pose does not meet the preset iteration termination condition, the updated random pose is taken as the new current random pose, and the execution of the calculation of the current random pose according to the gravitational field function is returned to. The steps of the gradient of the pose and its subsequent steps;

若所述当前随机位姿的梯度满足所述迭代终止条件，则将所述更新随机位姿确定为优化后的第二随机位姿。If the gradient of the current random pose satisfies the iteration termination condition, the updated random pose is determined as the optimized second random pose.
根据权利要求3所述的机械臂运动规划方法，其特征在于，所述建立与所述虚拟最优位姿对应的引力场函数，包括：The robotic arm motion planning method according to claim 3, wherein the establishing a gravitational field function corresponding to the virtual optimal pose comprises:

根据下式建立所述引力场函数：The gravitational field function is established according to the following formula:

其中，q _goal为所述虚拟最优位姿，q为所述当前随机位姿，
为预设的尺度因子， d(q,q _goal)为q与q _goal之间的距离，
为预设的距离阈值，U _att(q)为所述引力场函数。 Wherein, q _goal is the virtual optimal pose, q is the current random pose,
is the preset scale factor, d(q,q _goal ) is the distance between q and q _goal ,
is the preset distance threshold, and U _att (q) is the gravitational field function.
根据权利要求4所述的机械臂运动规划方法，其特征在于，所述根据所述引力场函数计算所述当前随机位姿的梯度，包括：The robotic arm motion planning method according to claim 4, wherein the calculating the gradient of the current random pose according to the gravitational field function comprises:

根据下式计算所述当前随机位姿的梯度：The gradient of the current random pose is calculated according to the following formula:

其中，
为所述当前随机位姿的梯度。 in,
is the gradient of the current random pose.
根据权利要求3所述的机械臂运动规划方法，其特征在于，所述根据当前随机位姿的梯度对所述当前随机位姿进行更新计算，得到更新随机位姿，包括：The robotic arm motion planning method according to claim 3, wherein the updating and calculating the current random pose according to the gradient of the current random pose to obtain an updated random pose, comprising:

根据下式对所述当前随机位姿进行更新计算：The current random pose is updated and calculated according to the following formula:

其中，q为所述当前随机位姿，
为所述当前随机位姿的梯度，η为预设的更新步长，q′为所述更新随机位姿。 Among them, q is the current random pose,
is the gradient of the current random pose, η is the preset update step size, and q′ is the updated random pose.
根据权利要求1至6中任一项所述的机械臂运动规划方法，其特征在于，所述确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径，包括：The motion planning method for a robotic arm according to any one of claims 1 to 6, wherein the determining a virtual optimal motion planning path for the robotic arm to move from a preset initial pose to a preset target pose ,include:

将连接所述初始位姿和所述目标位姿的直线路径确定为所述虚拟最优运动规划路径。A straight path connecting the initial pose and the target pose is determined as the virtual optimal motion planning path.
一种机械臂运动规划装置，其特征在于，包括：A robotic arm motion planning device, characterized in that it includes:

虚拟路径确定模块，用于确定机械臂从预设的初始位姿运动至预设的目标位姿的虚拟最优运动规划路径；The virtual path determination module is used to determine the virtual optimal motion planning path of the robot arm moving from the preset initial pose to the preset target pose;

随机采样模块，用于在所述机械臂的自由空间中进行随机采样，得到所述机械臂的第一随机位姿；a random sampling module for performing random sampling in the free space of the robotic arm to obtain the first random pose of the robotic arm;

迭代优化模块，用于根据所述虚拟最优运动规划路径对所述第一随机位姿进行迭代优化，得到优化后的第二随机位姿；an iterative optimization module, configured to iteratively optimize the first random pose according to the virtual optimal motion planning path to obtain an optimized second random pose;

运动规划模块，用于将所述第二随机位姿作为预设的机械臂运动规划算法中的随机采样结果对所述机械臂进行运动规划。A motion planning module, configured to use the second random pose as a random sampling result in a preset motion planning algorithm of the manipulator to perform motion planning for the manipulator.
一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至7中任一项所述的机械臂运动规划方法的步骤。A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, the robotic arm motion according to any one of claims 1 to 7 is realized Steps in the planning method.
一种机械臂，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至 7中任一项所述的机械臂运动规划方法的步骤。A robotic arm, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, when the processor executes the computer program, the process according to claim 1 to Steps of the robotic arm motion planning method described in any one of 7.