WO2023066201A1 - 机械臂运动路径规划方法及装置、设备、程序、介质 - Google Patents

机械臂运动路径规划方法及装置、设备、程序、介质 Download PDF

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Publication number
WO2023066201A1
WO2023066201A1 PCT/CN2022/125688 CN2022125688W WO2023066201A1 WO 2023066201 A1 WO2023066201 A1 WO 2023066201A1 CN 2022125688 W CN2022125688 W CN 2022125688W WO 2023066201 A1 WO2023066201 A1 WO 2023066201A1
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Prior art keywords
data
target object
target
planning
motion path
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PCT/CN2022/125688
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English (en)
French (fr)
Inventor
祝丰年
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达闼机器人股份有限公司
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Publication of WO2023066201A1 publication Critical patent/WO2023066201A1/zh

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
    • B25J9/1697—Vision controlled systems

Definitions

  • the embodiments of the present application relate to the technical field of automation, and in particular to a method, device, device, program, and medium for planning a motion path of a robot arm.
  • the purpose of the embodiments of the present application is to provide a method, device, device, program, and medium for planning a movement path of a robotic arm, so as to reduce costs while avoiding impacts on the operation of the robotic arm.
  • an embodiment of the present application provides a method for planning a movement path of a robotic arm, which is used to plan a movement path of a robotic arm when grabbing a target object, including: collecting weight data of the target object; The weight data is used to calculate the target torque when the robotic arm grabs the target object; and path planning is performed according to the target torque.
  • the embodiment of the present application also provides a robot arm movement path planning device, which is used to plan the movement path of the robot arm when grabbing the target object, including: a data collection component, used to collect the weight data of the target object; a torque calculation component, It is connected with the data acquisition component, and calculates the target torque when the mechanical arm grabs the target object according to the weight data; the path planning component is connected with the torque calculation component, and performs path planning according to the target torque.
  • An embodiment of the present application also provides an electronic device, including: at least one processor; and a memory connected in communication with the at least one processor; wherein, the memory stores information that can be executed by the at least one processor. instructions, the instructions are executed by the at least one processor, so that the at least one processor can execute the above-mentioned method for planning the motion path of the mechanical arm.
  • An embodiment of the present application further provides a computer program, which implements the above-mentioned method for planning a motion path of a robotic arm when the computer program is executed by a processor.
  • An embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned method for planning a motion path of a robotic arm is implemented.
  • the embodiment of the present application collects the weight data of the target object to be grasped through the data acquisition component, and calculates the optimal torque when the manipulator grasps the target object according to the weight data as the target torque, and then performs the grasping of the manipulator according to the target torque.
  • the motion path of the object is planned; there is no need to install a torque sensor on the mechanical arm, so as to avoid the impact of the torque sensor on the mechanical arm on the movement of the mechanical arm; in addition, the need to set an expensive torque sensor can also effectively reduce costs.
  • the collecting the weight data of the target object specifically includes: collecting the density data of the target object and the volume data of the target object; and calculating the weight data according to the density data and the volume data.
  • the target object after the collection of the density data of the target object, it also includes: calculating the position of the center of gravity of the target object according to the density data;
  • the target moment specifically includes: calculating and obtaining the target moment according to the position of the center of gravity and the weight data.
  • it also includes: acquiring distance data between the robotic arm and the target object; calculating the target moment when the robotic arm grabs the target object according to the weight data, specifically including: according to the weight data and the distance data to calculate the target torque.
  • it also includes: collecting the image data of the target object; obtaining grasping position data according to the image data; calculating the target moment when the mechanical arm grasps the target object according to the weight data, specifically including: The target torque is calculated according to the weight data and the grasping position data.
  • the data acquisition component includes a density acquisition device and a volume acquisition device; the density acquisition device is used to collect the density data of the target object, and the volume acquisition device is used to collect the volume data of the target object; the Weight data is calculated from the volume data and the density data.
  • a center-of-gravity calculation component connected to the density acquisition device; the center-of-gravity calculation component calculates the position of the center of gravity of the target object according to the density data; The center of gravity position is calculated to obtain the target moment.
  • it also includes: a distance acquisition component connected to the moment calculation component; the distance acquisition component is used to obtain distance data between the mechanical arm and the target object; the torque calculation component obtains the distance data , and calculate the target torque according to the weight data and the distance data.
  • the image acquisition component connected to the moment calculation component, the image acquisition component is used to collect the image data of the target object, and obtain grasping position data according to the image data; the torque calculation component The target moment is calculated based on the grasping position data and the weight data.
  • Fig. 1 is a structural schematic diagram 1 of a device for planning a movement path of a robotic arm provided in the first embodiment of the present application;
  • Fig. 2 is the second structural schematic diagram of the robot arm motion path planning device provided by the first embodiment shown in Fig. 1 of the present application;
  • Fig. 3 is a schematic structural diagram of a device for planning a motion path of a robotic arm provided in a second embodiment of the present application;
  • Fig. 4 is a schematic structural diagram of a device for planning a motion path of a robot arm provided in a third embodiment of the present application;
  • Fig. 5 is a schematic structural diagram of a robot arm movement path planning device provided by the fourth embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of a robotic arm for motion path planning provided by an embodiment of the present application.
  • FIG. 7 is a schematic flowchart of a method for planning a motion path of a robotic arm provided in a fifth embodiment of the present application.
  • FIG. 8 is a schematic flowchart of a method for planning a motion path of a robotic arm provided in the sixth embodiment of the present application.
  • FIG. 9 is a schematic flowchart of a method for planning a motion path of a robotic arm provided in the seventh embodiment of the present application.
  • FIG. 10 is a schematic flowchart of a method for planning a motion path of a robotic arm provided in the eighth embodiment of the present application.
  • FIG. 11 is a schematic structural diagram of an electronic device provided by a ninth embodiment of the present application.
  • the first embodiment of the present application relates to a robot arm movement path planning device, which is used to plan the movement path of the robot arm when grabbing a target object.
  • the specific structure is shown in Figure 1, including: a data acquisition component 10, and a data acquisition component 10 is connected to the torque calculation component 20, and the path planning component 30 is connected to the torque calculation component 20.
  • the data collection component 10 is used to collect the weight data of the target object
  • the torque calculation component 20 calculates the target torque when the mechanical arm grabs the target object according to the weight data collected by the data collection component 10, and the path planning component 30 controls the movement of the mechanical arm according to the target torque.
  • path for path planning the target torque is the optimal torque when the manipulator grabs the target object; and the optimal torque is the minimum torque during the dynamic calculation process.
  • the data acquisition component 10 collects the weight data of the target object to be grasped, and the torque calculation component 20 calculates the optimal time when the mechanical arm grasps the target object according to the weight data.
  • the torque is used as the target torque, and then the path planning component 30 performs path planning for the motion path of the mechanical arm to grab the target object according to the target torque; there is no need to set a torque sensor on the mechanical arm, thereby avoiding setting a torque sensor on the mechanical arm.
  • the cost can be effectively reduced without setting up an expensive torque sensor.
  • the data acquisition assembly 10 includes a density acquisition device 11 and a volume acquisition device 12 .
  • the density collecting device 11 is used for collecting the density data of the target object
  • the volume collecting device 12 is used for collecting the volume data of the target object
  • the weight data is obtained by calculating according to the volume data and the density data.
  • the density acquisition device 11 may be an ultrasonic densitometer
  • the volume acquisition device 12 may include a depth camera and an image recognizer. Among them, the ultrasonic densitometer directly measures the density of the target object, the depth camera captures the target image of the target object, and the image recognizer performs image recognition on the target image to obtain the volume data of the target object.
  • the aforementioned density acquisition device 11 is an ultrasonic densitometer
  • the volume acquisition device 12 includes a depth camera and an image recognizer is only a specific illustration in this embodiment and does not constitute a limitation.
  • the density acquisition device 11 and the volume acquisition device 12 can also be other devices, as long as the density and volume of the target object can be measured.
  • the data collection component 10 may further include a depth camera 13 and an image recognizer 14 .
  • the depth camera 13 shoots the target image of the target object
  • the image recognizer 14 performs image recognition on the target image, and judges the type data of the target object according to the recognition result, for example, recognizing that the target object is iron, plastic or glass, etc., according to the target object The type of to get the density data of the target object.
  • the image recognizer 14 performs image recognition on the target image, it can also recognize the volume data of the target object, so as to calculate the weight data of the target object according to the density data and the volume data.
  • the second embodiment of the present application relates to a device for planning a motion path of a robot arm.
  • the second embodiment is substantially the same as the first embodiment, the main difference is that: as shown in Figure 3, in the second embodiment of the present application, it also includes: a center of gravity calculation component 40 connected to the density acquisition device 11, the center of gravity calculation The component 40 is connected to the moment calculation component 20 .
  • the center of gravity calculation component 40 can calculate the center of gravity position of the target object according to the density data collected by the density acquisition device 11, and the moment calculation component 20 can calculate the target torque according to the weight data collected by the data acquisition component 10 and the center of gravity position calculated by the center of gravity calculation component 40 .
  • the robot arm motion path planning device provided in the second embodiment of the present application is additionally equipped with a center of gravity calculation component 40 on the basis of the first embodiment, so while retaining all the technical effects of the first embodiment, the target object can be obtained through calculation
  • the position of the center of gravity of the target moment is calculated by the moment calculation component 20, which makes the calculation result of the target moment more accurate and optimizes the result of path planning.
  • the third embodiment of the present application relates to a motion path planning device for a mechanical arm.
  • the third embodiment is substantially the same as the first embodiment, the main difference is that: as shown in FIG. 4 , in the third embodiment of the present application, a distance acquisition component 50 connected to the moment calculation component 20 is also included.
  • the distance collection component 50 is used to obtain the distance data between the mechanical arm and the target object, and the moment calculation component 20 obtains the distance data collected by the distance collection component 50, and according to the weight data collected by the data collection component 10 and the distance collected by the distance collection component 50 The data is calculated to obtain the target torque.
  • the robot arm movement path planning device provided by the third embodiment of the present application is additionally provided with a distance acquisition component 50 on the basis of the first embodiment, therefore, while retaining all the technical effects of the first embodiment
  • the distance data between the mechanical arm and the target object is collected by the distance collection component 50
  • the torque calculation component 20 additionally adds the distance data between the mechanical arm and the target object when calculating the target torque, so that the calculation result of the target torque is more accurate , which optimizes the result of path planning.
  • the distance acquisition component 50 is a laser range finder. It can be understood that the foregoing distance acquisition component 50 is a laser range finder, which is only a specific illustration in this embodiment and does not constitute a limitation. In other examples of the application, the distance acquisition component 50 can also be an ultrasonic Other structures such as a rangefinder can be flexibly set according to actual needs.
  • the fourth embodiment of the present application relates to a motion path planning device for a robot arm.
  • the fourth embodiment is substantially the same as the first embodiment, the main difference is that, as shown in FIG. 5 , in the fourth embodiment of the present application, an image acquisition component 60 connected to the moment calculation component 20 is also included.
  • the image acquisition component 60 is used to collect the image data of the target object, and obtain the grasping position data according to the image data, that is, the image acquisition component 60 performs image recognition on the image data of the collected target object, and obtains the position that is easy to grasp in the target object as
  • the grasping position is to obtain the position data of the grasping position, and the torque calculation component 20 calculates the target torque according to the weight data and the grasping position data collected by the data acquisition component 10 .
  • the robot arm motion path planning device provided by the fourth embodiment of the present application is additionally provided with an image acquisition component 60 on the basis of the first embodiment, therefore, while retaining all the technical effects of the first embodiment, the image acquisition component 60
  • the image data of the target object is collected, and the grasping position data is obtained according to the image data.
  • the torque calculation component 20 additionally adds the grasping position data when calculating the target torque, so that the calculation result of the target torque is more accurate, and the result of path planning is optimized.
  • M( ⁇ ) is the mass matrix
  • M( ⁇ ) ⁇ ′′ represents the inertial force term.
  • the main diagonal elements in M( ⁇ ) represent the effective inertia of each connecting rod itself, representing the given Determine the relationship between the torque on the joint and the resulting angular acceleration
  • the off-diagonal elements represent the coupling inertia between the connecting rods, which is the measure of the coupling torque generated by the accelerated motion of a connecting rod on another joint
  • C ( ⁇ , ⁇ ′) is the n ⁇ 1 order centripetal force and Coriolis force term
  • G( ⁇ ) is the n ⁇ 1 order gravity term, which is related to the shape and position ⁇ of the robot, that is, ⁇ 1 and ⁇ 2 in Figure 6.
  • ⁇ , ⁇ ', ⁇ " are the rotation angle, angular velocity and angular acceleration, respectively, and these three values can be detected during the movement of the manipulator.
  • the movement of the robotic arm can be achieved by controlling the torque of the joints.
  • the control joint m1 in Figure 6 is taken as an example to illustrate the control of torque on motion:
  • the fifth embodiment of the present application relates to a method for planning a movement path of a robotic arm, which is used to plan the movement path of the robotic arm when grabbing a target object.
  • the specific steps are shown in FIG. 7 , including:
  • Step S701 Collect weight data of the target object.
  • the density data and the volume data of the target object are firstly collected, and the weight data of the target object is obtained by calculating the density data and the volume data.
  • the density data of the target object can be collected by the density collection device, and the volume data of the target object can be collected by the volume collection device.
  • the density collection device can be an ultrasonic densitometer, and the volume collection device can be Includes depth camera and image recognizer. The ultrasonic densitometer directly measures the density of the target object, the depth camera captures the target image of the target object, and the image recognizer performs image recognition on the target image to obtain the volume data of the target object.
  • the aforementioned density acquisition device is an ultrasonic densitometer
  • the volume acquisition device includes a depth camera and an image recognizer is only a specific illustration in this embodiment and does not constitute a limitation.
  • Other devices may be used as long as the density and volume of the target object can be measured.
  • the target image of the target object is captured by the depth camera, and the image recognizer performs image recognition on the target image, and judges the type data of the target object according to the recognition result, such as identifying whether the target object is iron, plastic or glass, etc., according to the target object.
  • Type Gets the density data of the target object.
  • the image recognizer performs image recognition on the target image, it can also recognize the volume data of the target object, so as to calculate the weight data of the target object according to the density data and the volume data.
  • Step S702 Calculate and obtain the target moment when the robot arm grasps the target object according to the weight data.
  • Step S703 Perform path planning according to the target moment.
  • this embodiment is an embodiment of the method for planning a movement path of a robotic arm corresponding to the first embodiment, and this embodiment can be implemented in cooperation with the first embodiment.
  • the relevant technical details and technical effects mentioned in the first embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition.
  • the relevant technical details mentioned in this embodiment can also be applied in the first embodiment.
  • the sixth embodiment of the present application relates to a method for planning a movement path of a robotic arm, which is used to plan the movement path of the robotic arm when grabbing a target object.
  • the specific steps are shown in FIG. 8 , including:
  • Step S801 Collect weight data of the target object.
  • the density data and the volume data of the target object are firstly collected, and the weight data of the target object is obtained by calculating the density data and the volume data.
  • Step S802 Calculate the center of gravity of the target object according to the density data.
  • Step S803 Calculate and obtain the target torque when the robotic arm grabs the target object according to the weight data and the position of the center of gravity.
  • Step S804 Perform path planning according to the target moment.
  • this embodiment is an embodiment of the method for planning a movement path of a robotic arm corresponding to the second embodiment, and this embodiment can be implemented in cooperation with the second embodiment.
  • the relevant technical details and technical effects mentioned in the second embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition.
  • the relevant technical details mentioned in this implementation manner can also be applied in the second embodiment.
  • the seventh embodiment of the present application relates to a method for planning a movement path of a robotic arm, which is used to plan the movement path of the robotic arm when grabbing a target object.
  • the specific steps are shown in FIG. 9 , including:
  • Step S901 Collect weight data of the target object.
  • Step S902 Obtain distance data between the robotic arm and the target object.
  • Step S903 Calculate the target moment when the robotic arm grabs the target object according to the weight data and the distance data.
  • Step S904 Perform path planning according to the target moment.
  • this embodiment is an embodiment of the method for planning a movement path of a robotic arm corresponding to the third embodiment, and this embodiment can be implemented in cooperation with the third embodiment.
  • the relevant technical details and technical effects mentioned in the third embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition.
  • the relevant technical details mentioned in this implementation manner can also be applied in the third embodiment.
  • the eighth embodiment of the present application relates to a method for planning a movement path of a robotic arm, which is used to plan the movement path of the robotic arm when grabbing a target object.
  • the specific steps are shown in FIG. 10 , including:
  • Step S1001 Collect weight data of the target object.
  • Step S1002 Collect image data of the target object.
  • Step S1003 Obtain grabbing position data according to the image data.
  • Step S1004 Calculate the target moment when the robot arm grabs the target object according to the weight data and the grab position data.
  • Step S1005 Perform path planning according to the target moment.
  • this embodiment is an embodiment of the method for planning a movement path of a robotic arm corresponding to the fourth embodiment, and this embodiment can be implemented in cooperation with the fourth embodiment.
  • the relevant technical details and technical effects mentioned in the fourth embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition.
  • the relevant technical details mentioned in this embodiment can also be applied in the fourth embodiment.
  • the ninth embodiment of the present application relates to an electronic device, as shown in FIG. 11 , including: at least one processor 1101; and a memory 1102 communicatively connected to at least one processor 1101; Instructions executed by the processor 1101, the instructions are executed by at least one processor 1101, so that at least one processor 1101 can execute the grasping control method described in any method embodiment above.
  • the memory 1102 and the processor 1101 are connected by a bus, and the bus may include any number of interconnected buses and bridges, and the bus connects one or more processors 1101 and various circuits of the memory 1102 together.
  • the bus may also connect together various other circuits such as peripherals, voltage regulators, and power management circuits, all of which are well known in the art and therefore will not be further described herein.
  • the bus interface provides an interface between the bus and the transceivers.
  • a transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing means for communicating with various other devices over a transmission medium.
  • the data processed by the processor 1101 is transmitted on the wireless medium through the antenna, and further, the antenna also receives the data and transmits the data to the processor 1101 .
  • the processor 1101 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management and other control functions. And the memory 1102 may be used to store data used by the processor 1101 when performing operations.
  • the tenth embodiment of the present application relates to a computer program.
  • the computer program is executed by a processor, the grasping control method described in any of the above method embodiments is implemented.
  • the eleventh embodiment of the present application relates to a computer-readable storage medium storing a computer program.
  • the computer program is executed by the processor, the grasping control method described in any of the above method embodiments is implemented.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and other media that can store program codes. .

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Abstract

一种机械臂运动路径规划方法及装置、设备、程序、介质，其中，机械臂运动路径规划方法，用于规划机械臂抓取目标物件时的运动路径，包括：采集目标物件的重量数据；根据重量数据计算得到机械臂抓取目标物件时的目标力矩；根据目标力矩进行路径规划。

Description

机械臂运动路径规划方法及装置、设备、程序、介质
本申请基于申请号为“202111210874.6”、申请日为2021年10月18日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此以引入方式并入本申请。
技术领域
本申请的实施例涉及自动化技术领域，特别涉及一种机械臂运动路径规划方法及装置、设备、程序、介质。
 背景技术
目前，工业机器人已被广泛应用于电子、物流、化工等各个领域。当机器人在执行上下料任务过程中，由于工件摆放的位置偏差，采用传统的位置控制方法取件时必然会使得机械臂承受额外的附加外力，如果工件偏移量较大并强行取件，相当于与机械臂发生碰撞，导致作业失败。为了解决上述问题，工业机器人一种简单易行的做法是在机械臂末端法兰上加装力传感器/力矩传感器以测量外部作用力，然后采用基于位置的导纳控制策略实现柔顺控制。
然而，在机械臂关节处加装力矩传感器，虽然可以实时获取相关力信息/力矩信息，帮助机器人系统实现力反馈控制，但是增加力矩传感器往往会导致机械臂过于笨重，而且力矩传感器的高昂价格也会使得整体的成本大幅增加。
 技术解决方案
本申请实施例的目的在于提供一种机械臂运动路径规划方法及装置、设备、程序、介质，避免对机械臂工作产生影响的同时降低成本。
为解决上述技术问题，本申请的实施例提供了一种机械臂运动路径规划方法，用于规划机械臂抓取目标物件时的运动路径，包括：采集所述目标物件的重量数据；根据所述重量数据计算得到机械臂抓取所述目标物件时的目标力矩；根据所述目标力矩进行路径规划。
本申请的实施例还提供了一种机械臂运动路径规划装置，用于规划机械臂抓取目标物件时的运动路径，包括：数据采集组件，用于采集目标物件的重量数据；力矩计算组件，与所述数据采集组件相连，并根据所述重量数据计算机械臂抓取所述目标物件时的目标力矩；路径规划组件，与力矩计算组件相连，根据所述目标力矩进行路径规划。
本申请的实施例还提供了一种电子设备，包括：至少一个处理器；以及，与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如上所述的机械臂运动路径规划方法。
本申请的实施例还提供了一种计算机程序，所述计算机程序被处理器执行时实现如上所述的机械臂运动路径规划方法。
本申请的实施例还提供了一种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如上所述的机械臂运动路径规划方法。
本申请实施方式通过数据采集组件采集待抓取的目标物件的重量数据，根据重量数据计算得到机械臂抓取目标物件时最优的力矩作为目标力矩，然后根据目标力矩进行对机械臂抓取目标物件的运动路径进行路径规划；无需在机械臂上设置力矩传感器，从而避免在机械臂上设置力矩传感器对机械臂的运动产生影响；此外，无需设置价格昂贵的力矩传感器还可以有效的降低成本。
另外，所述采集所述目标物件的重量数据，具体包括：采集所述目标物件的密度数据和所述目标物件的体积数据；根据所述密度数据和所述体积数据计算得到所述重量数据。
另外，所述采集所述目标物件的密度数据后，还包括：根据所述密度数据计算得到所述目标物件的重心位置；所述根据所述重量数据计算得到机械臂抓取所述目标物件时的目标力矩，具体包括：根据所述重心位置和所述重量数据计算得到所述目标力矩。
另外，还包括：获取所述机械臂与所述目标物件之间的距离数据；所述根据所述重量数据计算得到机械臂抓取所述目标物件时的目标力矩，具体包括：根据所述重量数据和所述距离数据计算得到所述目标力矩。
另外，还包括：采集所述目标物件的图像数据；根据所述图像数据获取抓取位置数据；所述根据所述重量数据计算得到机械臂抓取所述目标物件时的目标力矩，具体包括：根据所述重量数据和所述抓取位置数据计算得到所述目标力矩。
另外，所述数据采集组件包括密度采集装置和体积采集装置；所述密度采集装置用于采集所述目标物件的密度数据，所述体积采集装置用于采集所述目标物件的体积数据；所述重量数据根据所述体积数据和所述密度数据计算得到。
另外，还包括：与所述密度采集装置连接的重心计算组件；所述重心计算组件根据所述密度数据计算得到所述目标物件的重心位置；所述力矩计算组件根据所述重量数据和所述重心位置计算得到所述目标力矩。
另外，还包括：与所述力矩计算组件连接的距离采集组件；所述距离采集组件用于获取所述机械臂与所述目标物件之间的距离数据；所述力矩计算组件获取所述距离数据、并根据所述重量数据和所述距离数据计算得到所述目标力矩。
另外，还包括：与所述力矩计算组件连接的图像采集组件，所述图像采集组件用于采集所述目标物件的图像数据、并根据所述图像数据获取抓取位置数据；所述力矩计算组件根据所述抓取位置数据和所述重量数据计算所述目标力矩。
 附图说明
图1是本申请第一实施例所提供的机械臂运动路径规划装置的结构示意图一；
图2是本申请图1所示的第一实施例所提供的机械臂运动路径规划装置的结构示意图二；
图3是本申请第二实施例所提供的机械臂运动路径规划装置的结构示意图；
图4是本申请第三实施例所提供的机械臂运动路径规划装置的结构示意图；
图5是本申请第四实施例所提供的机械臂运动路径规划装置的结构示意图；
图6是本申请实施例所提供的进行运动路径规划的机械臂的结构示意图；
图7是本申请第五实施例所提供的机械臂运动路径规划方法的流程示意图；
图8是本申请第六实施例所提供的机械臂运动路径规划方法的流程示意图；
图9是本申请第七实施例所提供的机械臂运动路径规划方法的流程示意图；
图10是本申请第八实施例所提供的机械臂运动路径规划方法的流程示意图；
图11是本申请第九实施例所提供的电子设备的结构示意图。
 本发明的实施方式
为使本申请的目的、技术方案和优点更加清楚，下面将结合附图对本申请的各实施方式进行详细的阐述。然而，本领域的普通技术人员可以理解，在本申请各实施方式中，为了使读者更好地理解本申请而提出了许多技术细节。但是，即使没有这些技术细节和基于以下各实施方式的种种变化和修改，也可以实现本申请所要求保护的技术方案。
本申请的第一实施例涉及一种机械臂运动路径规划装置，用于规划机械臂抓取目标物件时的运动路径，具体结构如图1所示，包括：数据采集组件10、与数据采集组件10相连的力矩计算组件20、以及与力矩计算组件20连接的路径规划组件30。数据采集组件10用于采集目标物件的重量数据，力矩计算组件20根据数据采集组件10采集的重量数据计算机械臂抓取目标物件时的目标力矩，路径规划组件30根据目标力矩对机械臂的运动路径进行路径规划。其中，目标力矩为机械臂抓取目标物件时的最优力矩；其中，最优力矩为动态计算过程中的最小力矩。
本申请第一实施例所提供的机械臂运动路径规划装置中通过数据采集组件10采集待抓取的目标物件的重量数据，力矩计算组件20根据重量数据计算得到机械臂抓取目标物件时最优的力矩作为目标力矩，然后路径规划组件30根据目标力矩进行对机械臂抓取目标物件的运动路径进行路径规划；无需在机械臂上设置力矩传感器，从而避免在机械臂上设置力矩传感器对机械臂的运动产生影响；此外，无需设置价格昂贵的力矩传感器还可以有效的降低成本。
在一些实施例中，数据采集组件10包括密度采集装置11和体积采集装置12。其中，密度采集装置11用于采集目标物件的密度数据，体积采集装置12用于采集目标物件的体积数据；根据体积数据和密度数据计算得到重量数据。在一些例子中，密度采集装置11可以为超声波密度计，体积采集装置12可以包括深度相机和图像识别器。其中，超声波密度计直接测量目标物件的密度，深度相机拍摄目标物件的目标图像，图像识别器对目标图像进行图像识别、获取目标物件的体积数据。可以理解的是，前述密度采集装置11为超声波密度计，体积采集装置12包括深度相机和图像识别器仅为本实施例中的一种具体的举例说明，并不构成限定，在其它例子中，密度采集装置11和体积采集装置12还可以是其它装置，只要可以测量目标物件的密度和体积即可。
可以理解的是，前述数据采集组件10包括密度采集装置11和体积采集装置12仅为第一实施例中数据采集组件10的一种具体结构的举例说明，并不构成限定，在本申请的另一些实施例中，如图2所示，数据采集组件10还可以是包括深度相机13和图像识别器14。其中，深度相机13拍摄目标物件的目标图像，图像识别器14对目标图像进行图像识别，根据识别结果判断目标物件的种类数据，例如识别目标物体为铁器、塑料件或玻璃件等，根据目标物件的种类获取目标物件的密度数据。此外，图像识别器14在对目标图像进行图像识别时，还可以识别到目标物件的体积数据，从而根据密度数据和体积数据计算得到目标物件的重量数据。
本申请的第二实施例涉及一种机械臂运动路径规划装置。第二实施例与第一实施例大致相同，主要区别之处在于：如图3所示，在本申请第二实施例中，还包括：与密度采集装置11连接的重心计算组件40，重心计算组件40与力矩计算组件20连接。重心计算组件40可以根据密度采集装置11采集的密度数据计算得到目标物件的重心位置，力矩计算组件20可以根据数据采集组件10采集的重量数据和重心计算组件40计算得到的重心位置计算得到目标力矩。
本申请第二实施例所提供的机械臂运动路径规划装置在第一实施例的基础上额外设置重心计算组件40，因此，在保留第一实施例的全部技术效果的同时，通过计算获取目标物件的重心位置，力矩计算组件20在计算目标力矩时额外加入目标物件的重心位置，使得目标力矩的计算结果更为精准，优化了路径规划的结果。
本申请的第三实施例涉及一种机械臂运动路径规划装置。第三实施例与第一实施例大致相同，主要区别之处在于：如图4所示，在本申请第三实施例中，还包括：与力矩计算组件20连接的距离采集组件50。距离采集组件50用于获取机械臂与目标物件之间的距离数据，力矩计算组件20获取距离采集组件50采集的距离数据、并根据数据采集组件10采集的重量数据和距离采集组件50采集的距离数据计算得到目标力矩。
与现有技术相比，本申请第三实施例所提供的机械臂运动路径规划装置在第一实施例的基础上额外设置距离采集组件50，因此，在保留第一实施例的全部技术效果的同时，通过距离采集组件50采集机械臂与目标物件之间的距离数据，力矩计算组件20在计算目标力矩时额外加入机械臂与目标物件之间的距离数据，使得目标力矩的计算结果更为精准，优化了路径规划的结果。
在一些例子中，距离采集组件50为激光测距仪。可以理解的是，前述距离采集组件50为激光测距仪仅为本实施例中的一种具体的举例说明，并不构成限定，在本申请的其它例子中，距离采集组件50还可以为超声波测距仪等其它结构，具体可以根据实际需要进行灵活的设置。
本申请的第四实施例涉及一种机械臂运动路径规划装置。第四实施例与第一实施例大致相同，主要区别之处在于：如图5所示，在本申请第四实施例中，还包括：与力矩计算组件20连接的图像采集组件60。图像采集组件60用于采集目标物件的图像数据、并根据图像数据获取抓取位置数据，即图像采集组件60对采集的目标物件的图像数据进行图像识别，获取目标物件中易于抓取的位置作为抓取位置，获取抓取位置的位置数据，力矩计算组件20根据数据采集组件10采集的重量数据和抓取位置数据计算得到目标力矩。
本申请第四实施例所提供的机械臂运动路径规划装置在第一实施例的基础上额外设置图像采集组件60，因此，在保留第一实施例的全部技术效果的同时，通过图像采集组件60采集目标物件的图像数据、并根据图像数据获取抓取位置数据，力矩计算组件20在计算目标力矩时额外加入抓取位置数据，使得目标力矩的计算结果更为精准，优化了路径规划的结果。
下面，将以2个自由度的机械臂为例，举例说明力矩计算原理。如图6所示，C为机械臂基座为固定点，x方向为水平方向，g方向为重力方向，假定连杆质量集中在连杆末端，控制末端质量m2(其中m2为被抓取物体和控制端机械臂的总质量)和控制关节质量m1。其中L2和L1分别为控制末端机械臂的长度，以及控制关节机械臂的长度。其中θ1和θ2分别为两个机械臂的旋转角度。
通常可将根据动量矩定理或牛顿-欧拉法推导出的力矩计算公式为如下形式：
τ=M（θ）θ″+C(θ，θ′)+G(θ)；
其中，机械臂自由度为n，M（θ）为质量矩阵，M（θ）θ″代表惯性力项。M（θ）中的主对角线元素表示各连杆本身的有效惯量，代表给定关节上的力矩与产生的角加速度之间的关系，非对角线元素表示连杆之间的耦合惯量，即是某连杆的加速运动对另一关节产生的耦合作用力矩的度量；C(θ，θ′)为n×1阶向心力和科氏力项；G(θ)为n×1阶的重力项，与机器人的形位θ有关，即图6中的θ1和θ2。其中θ，θ′，θ″分别为旋转角、角速度和角加速度，在机械臂的运动中可以检测得到该三个值。
机械臂的运动可以通过控制关节的力矩来实现。本例中以控制关节图6中m1为例进行说明力矩对运动的控制：
根据上面所述的计算公式，在给定一个抓取的时间间隔，得到等间隔时间序列，根据机械臂当前位置，被抓物体的位置计算轨迹插值，得到与时间序列对应的关节位置，速度与加速度值，进一步调用上述函数得到对应的关节力矩值，最终得到一个与时间、位置相关的运动轨迹。
例如，以图6中自由度为2的机械臂为例进一步说明，当控制终端m2从虚线运动至实线位置的时候，在水平方向上产生了位置的变化，角度旋转为θ2，假定运动耗时为T，通过上述的公式可以计算得到对应的力矩。将该运动过程进一步细分，假定该过程分为N个相等的时间段组成,每段时间为t=T/N。当控制终端m2从起始虚线位置运行了时间t，角度旋转为θ，根据上述公式计算得到力矩，依次类推可以得到N个不同时间点的角度，速度，加速度。通过在运行的过程中实时计算，就会得到一个与时间，位置相关的运动轨迹，从而实现实时柔顺的控制机械臂的运动。
本申请第五实施例涉及一种机械臂运动路径规划方法，用于规划机械臂抓取目标物件时的运动路径，具体步骤如图7所示，包括：
步骤S701：采集目标物件的重量数据。
在本步骤中，首先采集目标物件的密度数据和体积数据，通过密度数据和体积数据计算得到目标物件的重量数据。在一些实施例中，可以通过密度采集装置采集目标物件的密度数据，通过体积采集装置采集目标物件的体积数据，在实际应用过程中，例如，密度采集装置可以为超声波密度计，体积采集装置可以包括深度相机和图像识别器。超声波密度计直接测量目标物件的密度，深度相机拍摄目标物件的目标图像，图像识别器对目标图像进行图像识别、获取目标物件的体积数据。可以理解的是，前述密度采集装置为超声波密度计，体积采集装置包括深度相机和图像识别器仅为本实施例中的一种具体的举例说明，并不构成限定，在其它实施例中，还可以是其它装置，只要可以测量目标物件的密度和体积即可。例如，通过深度相机拍摄目标物件的目标图像，图像识别器对目标图像进行图像识别，根据识别结果判断目标物件的种类数据，例如识别目标物体为铁器、塑料件或玻璃件等，根据目标物件的种类获取目标物件的密度数据。此外，图像识别器在对目标图像进行图像识别时，还可以识别到目标物件的体积数据，从而根据密度数据和体积数据计算得到目标物件的重量数据。
步骤S702：根据重量数据计算得到机械臂抓取目标物件时的目标力矩。
步骤S703：根据目标力矩进行路径规划。
不难发现，本实施例为与第一实施例相对应的机械臂运动路径规划方法的实施例，本实施例可与第一实施例互相配合实施。第一实施例中提到的相关技术细节和技术效果在本实施例中依然有效，为了减少重复，这里不再赘述。相应地，本实施例中提到的相关技术细节也可应用在第一实施例中。
本申请第六实施例涉及一种机械臂运动路径规划方法，用于规划机械臂抓取目标物件时的运动路径，具体步骤如图8所示，包括：
步骤S801：采集目标物件的重量数据。
在本步骤中，首先采集目标物件的密度数据和体积数据，通过密度数据和体积数据计算得到目标物件的重量数据。
步骤S802：根据密度数据计算得到目标物件的重心位置。
步骤S803：根据重量数据和重心位置计算得到机械臂抓取目标物件时的目标力矩。
步骤S804：根据目标力矩进行路径规划。
不难发现，本实施例为与第二实施例相对应的机械臂运动路径规划方法的实施例，本实施例可与第二实施例互相配合实施。第二实施例中提到的相关技术细节和技术效果在本实施例中依然有效，为了减少重复，这里不再赘述。相应地，本实施方式中提到的相关技术细节也可应用在第二实施例中。
本申请第七实施例涉及一种机械臂运动路径规划方法，用于规划机械臂抓取目标物件时的运动路径，具体步骤如图9所示，包括：
步骤S901：采集目标物件的重量数据。
步骤S902：获取机械臂与目标物件之间的距离数据。
步骤S903：根据重量数据和距离数据计算得到机械臂抓取目标物件时的目标力矩。
步骤S904：根据目标力矩进行路径规划。
不难发现，本实施例为与第三实施例相对应的机械臂运动路径规划方法的实施例，本实施例可与第三实施例互相配合实施。第三实施例中提到的相关技术细节和技术效果在本实施例中依然有效，为了减少重复，这里不再赘述。相应地，本实施方式中提到的相关技术细节也可应用在第三实施例中。
本申请第八实施例涉及一种机械臂运动路径规划方法，用于规划机械臂抓取目标物件时的运动路径，具体步骤如图10所示，包括：
步骤S1001：采集目标物件的重量数据。
步骤S1002：采集目标物件的图像数据。
步骤S1003：根据图像数据获取抓取位置数据。
步骤S1004：根据重量数据和抓取位置数据计算得到机械臂抓取目标物件时的目标力矩。
步骤S1005：根据目标力矩进行路径规划。
不难发现，本实施例为与第四实施例相对应的机械臂运动路径规划方法的实施例，本实施例可与第四实施例互相配合实施。第四实施例中提到的相关技术细节和技术效果在本实施例中依然有效，为了减少重复，这里不再赘述。相应地，本实施例中提到的相关技术细节也可应用在第四实施例中。
本申请第九实施例涉及一种电子设备，如图11所示，包括：至少一个处理器1101；以及，与至少一个处理器1101通信连接的存储器1102；其中，存储器1102存储有可被至少一个处理器1101执行的指令，指令被至少一个处理器1101执行，以使至少一个处理器1101能够执行上述任一方法实施例所描述的抓取的控制方法。
其中，存储器1102和处理器1101采用总线方式连接，总线可以包括任意数量的互联的总线和桥，总线将一个或多个处理器1101和存储器1102的各种电路连接在一起。总线还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路连接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口在总线和收发机之间提供接口。收发机可以是一个元件，也可以是多个元件，比如多个接收器和发送器，提供用于在传输介质上与各种其他装置通信的单元。经处理器1101处理的数据通过天线在无线介质上进行传输，进一步，天线还接收数据并将数据传输给处理器1101。
处理器1101负责管理总线和通常的处理，还可以提供各种功能，包括定时，外围接口，电压调节、电源管理以及其他控制功能。而存储器1102可以被用于存储处理器1101在执行操作时所使用的数据。
本申请第十实施例涉及一种计算机程序，该计算机程序被处理器执行时实现上述任一方法实施例所描述的抓取的控制方法。
本申请第十一实施例涉及一种计算机可读存储介质，存储有计算机程序。计算机程序被处理器执行时实现上述任一方法实施例所描述的抓取的控制方法。
本领域技术人员可以理解实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得一个设备（可以是单片机，芯片等）或处理器（processor）执行本申请各个实施例方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器（ROM，Read-Only Memory）、随机存取存储器（RAM，Random Access Memory）、磁碟或者光盘等各种可以存储程序代码的介质。
本领域的普通技术人员可以理解，上述各实施例是实现本申请的具体实施例，而在实际应用中，可以在形式上和细节上对其作各种改变，而不偏离本申请的精神和范围。

Claims (13)

  1. 一种机械臂运动路径规划方法，用于规划机械臂抓取目标物件时的运动路径，包括：
    采集所述目标物件的重量数据；
    根据所述重量数据计算得到机械臂抓取所述目标物件时的目标力矩；
    根据所述目标力矩进行路径规划。
  2. 根据权利要求1所述的机械臂运动路径规划方法，其中，所述采集所述目标物件的重量数据，具体包括：
    采集所述目标物件的密度数据和所述目标物件的体积数据；
    根据所述密度数据和所述体积数据计算得到所述重量数据。
  3. 根据权利要求2所述的机械臂运动路径规划方法，其中，所述采集所述目标物件的密度数据后，还包括：
    根据所述密度数据计算得到所述目标物件的重心位置；
    所述根据所述重量数据计算得到机械臂抓取所述目标物件时的目标力矩，具体包括：
    根据所述重心位置和所述重量数据计算得到所述目标力矩。
  4. 根据权利要求1至3中任一项所述的机械臂运动路径规划方法，其中，还包括：获取所述机械臂与所述目标物件之间的距离数据；
    所述根据所述重量数据计算得到机械臂抓取所述目标物件时的目标力矩，具体包括：
    根据所述重量数据和所述距离数据计算得到所述目标力矩。
  5. 根据权利要求1至4中任一项所述的机械臂运动路径规划方法，其中，还包括：采集所述目标物件的图像数据；
    根据所述图像数据获取抓取位置数据；
    所述根据所述重量数据计算得到机械臂抓取所述目标物件时的目标力矩，具体包括：
    根据所述重量数据和所述抓取位置数据计算得到所述目标力矩。
  6. 一种机械臂运动路径规划装置，用于规划机械臂抓取目标物件时的运动路径，包括：
    数据采集组件，用于采集目标物件的重量数据；
    力矩计算组件，与所述数据采集组件相连，并根据所述重量数据计算机械臂抓取所述目标物件时的目标力矩；
    路径规划组件，与力矩计算组件相连，根据所述目标力矩进行路径规划。
  7. 根据权利要求6所述的机械臂运动路径规划装置，其中，
    所述数据采集组件包括密度采集装置和体积采集装置；
    所述密度采集装置用于采集所述目标物件的密度数据，所述体积采集装置用于采集所述目标物件的体积数据；
    所述重量数据根据所述体积数据和所述密度数据计算得到。
  8. 根据权利要求7所述的机械臂运动路径规划装置，其中，还包括：与所述密度采集装置连接的重心计算组件；
    所述重心计算组件根据所述密度数据计算得到所述目标物件的重心位置；
    所述力矩计算组件根据所述重量数据和所述重心位置计算得到所述目标力矩。
  9. 根据权利要求6至8中任一项所述的机械臂运动路径规划装置，其中，还包括：与所述力矩计算组件连接的距离采集组件；
    所述距离采集组件用于获取所述机械臂与所述目标物件之间的距离数据；
    所述力矩计算组件获取所述距离数据、并根据所述重量数据和所述距离数据计算得到所述目标力矩。
  10. 根据权利要求6至8中任一项所述的机械臂运动路径规划装置，其中，还包括：与所述力矩计算组件连接的图像采集组件，所述图像采集组件用于采集所述目标物件的图像数据、并根据所述图像数据获取抓取位置数据；
    所述力矩计算组件根据所述抓取位置数据和所述重量数据计算所述目标力矩。
  11. 一种电子设备，包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如权利要求1至5中任一项所述的机械臂运动路径规划方法。
  12. 一种计算机程序，所述计算机程序被处理器执行时实现如权利要求1至5中任一项所述的机械臂运动路径规划方法。
  13. 一种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如权利要求1至5中任一项所述的机械臂运动路径规划方法。
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