WO2022179180A1 - Real-time online pose compensation control method based on multi-agent cooperative transportation - Google Patents

Real-time online pose compensation control method based on multi-agent cooperative transportation Download PDF

Info

Publication number
WO2022179180A1
WO2022179180A1 PCT/CN2021/129479 CN2021129479W WO2022179180A1 WO 2022179180 A1 WO2022179180 A1 WO 2022179180A1 CN 2021129479 W CN2021129479 W CN 2021129479W WO 2022179180 A1 WO2022179180 A1 WO 2022179180A1
Authority
WO
WIPO (PCT)
Prior art keywords
agent
pose
real
slave
adjustment
Prior art date
Application number
PCT/CN2021/129479
Other languages
French (fr)
Chinese (zh)
Inventor
漆嘉林
董礼港
韩建超
张加波
陈涛
李永亮
张宁
代国宝
王超
Original Assignee
北京卫星制造厂有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 北京卫星制造厂有限公司 filed Critical 北京卫星制造厂有限公司
Publication of WO2022179180A1 publication Critical patent/WO2022179180A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/42—Simultaneous measurement of distance and other co-ordinates
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0219—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory ensuring the processing of the whole working surface
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0225—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving docking at a fixed facility, e.g. base station or loading bay
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291—Fleet control
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to a real-time online pose compensation control method based on multi-agent cooperative transport, and belongs to the technical field of control.
  • High-end equipment such as aerospace equipment, aviation equipment, and rail transit equipment are constantly being updated, and the demand for intelligent, flexible, and automated transfer and docking equipment and systems is more urgent.
  • High-end equipment often has the characteristics of large size, heavy weight, and difficult transportation. It exceeds the transfer capacity of existing conventional transfer equipment, and the transfer efficiency is low; the transfer docking process lacks flexibility, flexibility and integrated system solutions, which cannot meet high-quality requirements. , High-efficiency, flexible intelligent transport docking requirements.
  • the technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a real-time online pose compensation control method based on multi-agent cooperative transport.
  • S1. Determine each slave agent and the reference selected by the slave agent. The pose between the points is used as the initial pose; S2.
  • each slave agent acquires the pose between the slave agent and the reference point selected by the slave agent in real time as real-time pose; S3, determine the pose deviation of each slave agent; S4, use the pose deviation to calculate the pose deviation percentage; S5, select the maximum value among all the percentages, and then normalize it to determine each slave agent
  • a real-time online pose compensation control method based on multi-agent co-transportation The most front-end agent in the multi-agent is used as the main agent, and the others are used as slave agents. Specifically, the following steps are included:
  • Each slave agent selects a reference point on the rear face of the master agent; and determines the pose between each slave agent and the selected reference point of the slave agent as the initial pose;
  • each slave agent obtains the pose between the slave agent and the reference point selected by the slave agent in real time as the real-time pose;
  • the rotation speed of each wheel of each slave agent is determined by using the motion control amount described in S6.
  • the multi-agents are distributed in a glyph shape.
  • the above-mentioned real-time online pose compensation control method based on multi-agent cooperative transport preferably, in the process of multi-agent cooperative operation, communication is performed at a cycle of 100 Hz through a TCP/IP communication protocol, and laser scanning radar is used to measure slave agents in real time. The distance and angle between the respective reference points.
  • the above-mentioned real-time online pose compensation control method based on multi-agent cooperative transport preferably, in S6, for each slave agent, when performing coupling recalculation of the amplitude in each direction by using the adjustment amplitude, according to the adjustment amplitude in the height direction value, recalculate the pose adjustment deviations in other directions to obtain the recalculated adjustment amplitudes in each direction.
  • the recalculated adjustment amplitudes in each direction are first normalized. , and then establish the exponential reaching law in all directions.
  • the motion control amount gradually increases; when the interpolation interval does not exceed the control threshold, the motion control amount slowing shrieking.
  • the present invention has the following beneficial effects:
  • the present invention realizes the innovative application of flexible and efficient transfer docking process with diversified adaptive execution equipment and transfer mode, and provides better solutions for the shipping docking method of large-scale heavy-duty products.
  • the cooperative operation mode based on multi-agent has the characteristics of high transfer accuracy, self-adaptive combination, and convenient and fast operation.
  • the relative pose is periodically measured by the pose measurement system based on laser scanning radar contour recognition, and the deviation from the initial set pose is calculated; the motion commands, Fast wireless interaction of compensation parameters and state parameter data; through the mutual constraint analysis of the real-time pose deviation of each axis of the multi-agent, and establishing the corresponding pose compensation adjustment control strategy according to the analysis results, to realize the multi-agent cooperative transport online pose compensation adjustment.
  • the present invention adopts the real-time online pose compensation control method of multi-agent cooperative transport based on laser scanning radar, which ensures the real-time relative pose control accuracy of the multi-agent cooperative transport process.
  • the multi-agent autonomous heterogeneous formation collaborative control technology it can adapt to the flexible transfer and docking of diverse and heterogeneous high-end equipment products, and realize the high-efficiency and generalized transfer and docking operation of multi-agent coordination.
  • the inventive method the problems of high-efficiency transfer, docking operation and high-precision positioning of large-scale heterogeneous high-end equipment in a narrow space are solved.
  • Fig. 1 is the eight Mecanum wheel combination distribution diagram of the slave agent of the present invention
  • FIG. 2 is a schematic diagram of the multi-agent layout of the present invention.
  • FIG. 3 is a schematic diagram of relative pose measurement during the multi-agent cooperative transport process of the present invention.
  • a real-time online pose compensation control method based on multi-agent co-transportation The most front-end agent in the multi-agent is used as the main agent, and the others are used as slave agents. Specifically, the following steps are included:
  • Each slave agent selects a reference point on the rear face of the master agent; and determines the pose between each slave agent and the selected reference point of the slave agent as the initial pose;
  • each slave agent obtains the pose between the slave agent and the reference point selected by the slave agent in real time as the real-time pose;
  • the rotation speed of each wheel of each slave agent is determined by using the motion control quantity described in S6.
  • the multi-agents are distributed in a zigzag shape.
  • the communication is performed at a cycle of 100 Hz through the TCP/IP communication protocol, and the distance between the slave agents and the respective reference points is measured in real time by using laser scanning radar. and angle.
  • the multi-agent cooperative transport system consists of 3 agents.
  • the combination of Mecanum wheels realizes drive control, and the schematic diagram of the layout of 8 Mecanum wheels is shown in Figure 1.
  • the three agents of the multi-agent cooperative transport system are distributed in a zigzag shape and move with the geometric centroid O as the center.
  • the schematic diagram of the relative distribution is shown in Figure 2.
  • the agent composed of the first four Mecanum wheels is the main agent, and the agent composed of the last two eight Mecanum wheels is the slave.
  • the initial relative poses between the three agents can be flexibly adapted according to the actual needs, that is, Fig. A and b in 2 can be changed at will, where a is the distance between the rear face of the former master agent and the front face of the rear slave agent, and b is the distance between the geometric center points of the latter two agents.
  • the appropriate a and b parameters are selected according to the actual quality characteristics of the transported object, that is, the initial distribution design of the multi-agent is formed.
  • the initial pose of the center point O 1 of the slave agent 1 in the coordinate system X 0 O 0 Y 0 of the main agent and the center point O 1 of the slave agent 2 in the coordinate system X of the main agent according to the physical model of each agent
  • the theoretical initial pose of 0 O 0 Y 0 at the same time, in the process of multi-agent cooperative operation, the laser scanning radar is read in real time with a communication cycle of 100Hz through the TCP/IP communication protocol, and the slave agent relative to the main agent rear face A and B are measured in real time.
  • the coordinate system X 1 O 1 Y 1 is established from the center O 1 of the agent 1
  • the coordinate system X 2 O 2 Y 2 is established from the center O 2 of the agent 2
  • the center point O 0 of the rear face of the main agent is used.
  • the initial pose from the center point O 1 of agent 1 in the coordinate system X 0 O 0 Y 0 of the main agent is:
  • the initial pose from the center point O 2 of agent 2 in the coordinate system X 0 O 0 Y 0 of the main agent is:
  • the real-time measurement data of the center point of the front face of the agent 1 relative to the points A and B of the rear face of the main agent are ⁇ (d A1 ', ⁇ A1 '), ( d B1 ', ⁇ B1 ') ⁇ , the real-time pose (d x1 ',d y1 ',d z1 of the geometric center point O 1 of the agent 1 in the coordinate system X 0 O 0 Y 0 of the main agent can be obtained '):
  • the two agents coordinately adjust the control strategy.
  • the x, y, z three-axis pose deviation of the agent is recorded as ( ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 , ⁇ 5 , ⁇ 6 ), and is used as the input parameter of the adjustment control.
  • the percentages ( ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 ) of the three-axis pose deviation data of the two agents are calculated , ⁇ 5 , ⁇ 6 ), and the maximum deviation percentage ⁇ max is obtained according to the percentage order.
  • Coupling recalculation is performed according to the adjustment amplitude of each axis of the two agents:
  • the interpolation increment ⁇ i of each axis is adjusted to design the integral separation PID algorithm: when the interpolation increment ⁇ i is greater than the threshold value, the adjusted speed output should gradually increase, And when the error is small, the growth rate is small, and when the error is large, the growth rate is large; when the deviation value is less than or equal to the interpolation increment ⁇ i , the adjusted speed output should gradually decrease, namely:
  • ⁇ i K pi ( ⁇ i- ⁇ i')+K ii * ⁇ i+K di *( ⁇ i-2* ⁇ i'+ ⁇ i"))
  • the MECHATROLINK_II field motion bus is used to realize the topological connection of the multi-axis drive motors of the master and slave vehicles. Twenty-axis linkage interpolation control is performed according to the current speed V i of all motors of the master and slave cars and the target speed V i target to realize the synchronous planning control of the master and slave cars.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • General Physics & Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

A real-time online pose compensation control method based on multi-agent cooperative transportation, comprising the following steps: S1, determining a pose between each slave agent and a reference point selected by the slave agent as an initial pose; S2, during multi-agent cooperative transportation, each slave agent obtaining, in real time, a pose between the slave agent and the reference point selected by the slave agent as a real-time pose; S3, determining the pose deviation of each slave agent; S4, calculating a pose deviation percentage by using the pose deviation; S5, selecting a maximum value from all the percentages, then performing normalization, and determining an adjustment amplitude value of each slave agent; and S6, performing coupling re-calculation on an amplitude value in each direction by using the adjustment amplitude value, establishing a control law in each direction by using a coupling re-calculation result, then determining an interpolation increment in each direction, and finally setting a control threshold and determining a motion control amount by using the interpolation increment.

Description

一种基于多智能体协同转运实时在线位姿补偿控制方法A real-time online pose compensation control method based on multi-agent cooperative transport
本申请要求于2021年02月26日提交中国专利局、申请号为202110220770.7、申请名称为“一种基于多智能体协同转运实时在线位姿补偿控制方法”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 filed with the China Patent Office on February 26, 2021, the application number is 202110220770.7, and the application name is "a real-time online pose compensation control method based on multi-agent cooperative transport", all of which The contents are incorporated herein by reference.
技术领域technical field
本发明涉及一种基于多智能体协同转运实时在线位姿补偿控制方法，属于控制技术领域。The invention relates to a real-time online pose compensation control method based on multi-agent cooperative transport, and belongs to the technical field of control.
背景技术Background technique
随着工业技术的急速发展，如航天装备、航空装备、轨道交通装备等高端装备在不断更新，对智能化、柔性化、自动化的转运对接装备与系统需求更为迫切。高端装备往往具有尺寸大、重量大、运输困难等特点，超过现有常规转运设备转运能力，转运效率较低；转运对接过程缺乏灵活性、柔性化和集成化的系统解决方案，无法满足高质量、高效率、柔性化的智能转运对接要求。With the rapid development of industrial technology, high-end equipment such as aerospace equipment, aviation equipment, and rail transit equipment are constantly being updated, and the demand for intelligent, flexible, and automated transfer and docking equipment and systems is more urgent. High-end equipment often has the characteristics of large size, heavy weight, and difficult transportation. It exceeds the transfer capacity of existing conventional transfer equipment, and the transfer efficiency is low; the transfer docking process lacks flexibility, flexibility and integrated system solutions, which cannot meet high-quality requirements. , High-efficiency, flexible intelligent transport docking requirements.
发明内容SUMMARY OF THE INVENTION
本发明要解决的技术问题是：克服现有技术的不足，提供了一种基于多智能体协同转运实时在线位姿补偿控制方法，S1、确定每个从智能体与该从智能体所选参考点之间的位姿作为初始位姿；S2、在多智能体协同转运过程中，每个从智能体实时获取该从智能体与该从智能体所选参考点之间的位姿作作为实时位姿；S3、确定每个从智能体的位姿偏差；S4、利用位姿偏差计算位姿偏差百分比；S5、选取所有百分比中的最大值，然后进行归一化后，确定每个从智能体的调整幅值；S6、利用调整幅值进行每个方向幅值的耦合重计 算；利用耦合重计算结果建立各方向的控制律；然后确定各方向的插补增量；最后设定控制阈值，利用插补增量确定的运动控制量。The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a real-time online pose compensation control method based on multi-agent cooperative transport. S1. Determine each slave agent and the reference selected by the slave agent. The pose between the points is used as the initial pose; S2. In the process of multi-agent co-transportation, each slave agent acquires the pose between the slave agent and the reference point selected by the slave agent in real time as real-time pose; S3, determine the pose deviation of each slave agent; S4, use the pose deviation to calculate the pose deviation percentage; S5, select the maximum value among all the percentages, and then normalize it to determine each slave agent The adjustment amplitude of the body; S6, use the adjustment amplitude to carry out the coupling recalculation of the amplitude in each direction; use the coupling recalculation result to establish the control law in each direction; then determine the interpolation increment in each direction; finally, set the control threshold , the motion control amount determined by the interpolation increment.
本发明目的通过以下技术方案予以实现：The object of the present invention is achieved through the following technical solutions:
一种基于多智能体协同转运实时在线位姿补偿控制方法，将多智能体中最前端的智能体作为主智能体，其他作为从智能体，具体包括如下步骤：A real-time online pose compensation control method based on multi-agent co-transportation. The most front-end agent in the multi-agent is used as the main agent, and the others are used as slave agents. Specifically, the following steps are included:
S1、每个从智能体在主智能体的后端面选取一个参考点；并确定每个从智能体与该从智能体所选参考点之间的位姿作为初始位姿；S1. Each slave agent selects a reference point on the rear face of the master agent; and determines the pose between each slave agent and the selected reference point of the slave agent as the initial pose;
S2、在多智能体协同转运过程中，每个从智能体实时获取该从智能体与该从智能体所选参考点之间的位姿作作为实时位姿；S2. During the multi-agent cooperative transport process, each slave agent obtains the pose between the slave agent and the reference point selected by the slave agent in real time as the real-time pose;
S3、根据所述初始位姿和实时位姿，确定每个从智能体的位姿偏差；S3, according to the initial pose and the real-time pose, determine the pose deviation of each slave agent;
S4、对每个从智能体，设定位姿调整阈值，然后利用位姿偏差计算位姿偏差百分比；S4. For each slave agent, set the pose adjustment threshold, and then use the pose deviation to calculate the pose deviation percentage;
S5、选取所有百分比中的最大值，然后进行归一化后，确定每个从智能体的调整幅值；S5. Select the maximum value among all percentages, and then normalize to determine the adjustment amplitude of each slave agent;
S6、对每个从智能体，利用调整幅值进行每个方向幅值的耦合重计算；利用耦合重计算结果建立各方向的控制律；然后设定插补间隔，利用耦合重计算结果、控制律确定各方向的插补增量；最后设定控制阈值，利用插补增量确定的运动控制量。S6. For each slave agent, use the adjusted amplitude to perform the coupling recalculation of the amplitude in each direction; use the coupling recalculation results to establish the control law in each direction; then set the interpolation interval, use the coupling recalculation results, control The interpolation increment in each direction is determined by the law; finally, the control threshold is set, and the motion control amount determined by the interpolation increment is used.
上述基于多智能体协同转运实时在线位姿补偿控制方法，优选的，利用S6中所述的运动控制量，确定每个从智能体的各轮转速。In the above-mentioned real-time online pose compensation control method based on multi-agent cooperative transport, preferably, the rotation speed of each wheel of each slave agent is determined by using the motion control amount described in S6.
上述基于多智能体协同转运实时在线位姿补偿控制方法，优选的，所述多智能体呈品字形分布。In the above-mentioned real-time online pose compensation control method based on multi-agent cooperative transport, preferably, the multi-agents are distributed in a glyph shape.
上述基于多智能体协同转运实时在线位姿补偿控制方法，优选的，所述多智能体协同作业过程中通过TCP/IP通信协议以100Hz的周期进行通信，且利用激光扫描雷达实时测量从智能体相对于各自参考点之间的距离和角度。The above-mentioned real-time online pose compensation control method based on multi-agent cooperative transport, preferably, in the process of multi-agent cooperative operation, communication is performed at a cycle of 100 Hz through a TCP/IP communication protocol, and laser scanning radar is used to measure slave agents in real time. The distance and angle between the respective reference points.
上述基于多智能体协同转运实时在线位姿补偿控制方法，优选的，S6中， 对每个从智能体，利用调整幅值进行每个方向幅值的耦合重计算时，根据高度方向的调整幅值，对其他方向的位姿调整偏差进行重计算，获得重计算后的各方向调整幅值。The above-mentioned real-time online pose compensation control method based on multi-agent cooperative transport, preferably, in S6, for each slave agent, when performing coupling recalculation of the amplitude in each direction by using the adjustment amplitude, according to the adjustment amplitude in the height direction value, recalculate the pose adjustment deviations in other directions to obtain the recalculated adjustment amplitudes in each direction.
上述基于多智能体协同转运实时在线位姿补偿控制方法，优选的，S6中，利用重计算后的各方向调整幅值建立各方向的控制律。In the above-mentioned real-time online pose compensation control method based on multi-agent cooperative transport, preferably, in S6, a control law for each direction is established by using the recalculated amplitude adjustment in each direction.
上述基于多智能体协同转运实时在线位姿补偿控制方法，优选的，利用重计算后的各方向调整幅值建立各方向的控制律时，首先对重计算后的各方向调整幅值进行归一化，然后建立各方向的指数趋近律。In the above-mentioned real-time online pose compensation control method based on multi-agent cooperative transport, preferably, when the control law of each direction is established by using the recalculated adjustment amplitudes in each direction, the recalculated adjustment amplitudes in each direction are first normalized. , and then establish the exponential reaching law in all directions.
上述基于多智能体协同转运实时在线位姿补偿控制方法，优选的，S6中，当插补间隔大于控制阈值时，运动控制量逐渐增大；当插补间隔不超过控制阈值时，运动控制量逐渐减小。In the above real-time online pose compensation control method based on multi-agent cooperative transport, preferably, in S6, when the interpolation interval is greater than the control threshold, the motion control amount gradually increases; when the interpolation interval does not exceed the control threshold, the motion control amount slowing shrieking.
本发明相比于现有技术具有如下有益效果：Compared with the prior art, the present invention has the following beneficial effects:
(1)通过多智能体协同作业实现高端装备高效转运对接，以适应高端装备转运对接过程中产品的多样性和异构性需求，提升装备柔性化和适应程度，减少已往产品转运对接过程中的人力劳动，缩短产品转运时间，实现智能装备协同作业在精准转运对接与装配制造环节高效应用。(1) To achieve efficient transfer and docking of high-end equipment through multi-agent collaborative operation, to meet the needs of product diversity and heterogeneity in the process of high-end equipment transfer and docking, improve equipment flexibility and adaptability, and reduce previous product transfer and docking processes. Human labor, shorten the product transfer time, and realize the efficient application of intelligent equipment collaborative operation in the precise transfer docking and assembly manufacturing links.
(2)本发明以多元化自适应执行装备和转运模式实现柔性化、高效化转运对接过程创新应用，为大型重载产品的装运对接方式提供更好的解决措施。(2) The present invention realizes the innovative application of flexible and efficient transfer docking process with diversified adaptive execution equipment and transfer mode, and provides better solutions for the shipping docking method of large-scale heavy-duty products.
(3)基于多智能体协同作业方式具有转运精度高、自适应组合、作业方便快捷等特点。(3) The cooperative operation mode based on multi-agent has the characteristics of high transfer accuracy, self-adaptive combination, and convenient and fast operation.
(4)多智能体协同作业时通过基于激光扫描雷达轮廓识别位姿测量系统周期性测量相对位姿，并与初始设定位姿进行偏差计算；多智能体之间通过5G网络实现运动指令、补偿参数和状态参数数据的快速无线交互；通过对多智能体的各轴实时位姿偏差进行互约束性分析，并根据分析结果建立相应的位姿补偿调节控制策略，实现多智能体协同转运时的在线位姿补偿调节。(4) When multi-agents work together, the relative pose is periodically measured by the pose measurement system based on laser scanning radar contour recognition, and the deviation from the initial set pose is calculated; the motion commands, Fast wireless interaction of compensation parameters and state parameter data; through the mutual constraint analysis of the real-time pose deviation of each axis of the multi-agent, and establishing the corresponding pose compensation adjustment control strategy according to the analysis results, to realize the multi-agent cooperative transport online pose compensation adjustment.
(5)本发明采用基于激光扫描雷达的多智能体协同转运实时在线位姿补 偿控制方法，保证了多智能体协同转运过程的实时相对位姿控制精度。结合多智能体的自主异型编队协同控制技术，可以自适应多样性和异构型高端装备产品的柔性化转运对接，实现多智能体协同的高效率、通用化转运对接作业。采用该发明方法，解决了大型异构高端装备在狭小空间内的高效率转运、对接作业以及高精度定位问题。(5) The present invention adopts the real-time online pose compensation control method of multi-agent cooperative transport based on laser scanning radar, which ensures the real-time relative pose control accuracy of the multi-agent cooperative transport process. Combined with the multi-agent autonomous heterogeneous formation collaborative control technology, it can adapt to the flexible transfer and docking of diverse and heterogeneous high-end equipment products, and realize the high-efficiency and generalized transfer and docking operation of multi-agent coordination. By adopting the inventive method, the problems of high-efficiency transfer, docking operation and high-precision positioning of large-scale heterogeneous high-end equipment in a narrow space are solved.
附图说明Description of drawings
图1为本发明从智能体的八麦克纳姆轮组合分布图；Fig. 1 is the eight Mecanum wheel combination distribution diagram of the slave agent of the present invention;
图2为本发明多智能体布局示意图；FIG. 2 is a schematic diagram of the multi-agent layout of the present invention;
图3为本发明多智能体协同转运过程中的相对位姿测量示意图。FIG. 3 is a schematic diagram of relative pose measurement during the multi-agent cooperative transport process of the present invention.
具体实施方式Detailed ways
为使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明的实施方式作进一步详细描述。In order to make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
一种基于多智能体协同转运实时在线位姿补偿控制方法，将多智能体中最前端的智能体作为主智能体，其他作为从智能体，具体包括如下步骤：A real-time online pose compensation control method based on multi-agent co-transportation. The most front-end agent in the multi-agent is used as the main agent, and the others are used as slave agents. Specifically, the following steps are included:
S1、每个从智能体在主智能体的后端面选取一个参考点；并确定每个从智能体与该从智能体所选参考点之间的位姿作为初始位姿；S1. Each slave agent selects a reference point on the rear face of the master agent; and determines the pose between each slave agent and the selected reference point of the slave agent as the initial pose;
S2、在多智能体协同转运过程中，每个从智能体实时获取该从智能体与该从智能体所选参考点之间的位姿作作为实时位姿；S2. During the multi-agent cooperative transport process, each slave agent obtains the pose between the slave agent and the reference point selected by the slave agent in real time as the real-time pose;
S3、根据所述初始位姿和实时位姿，确定每个从智能体的位姿偏差；S3, according to the initial pose and the real-time pose, determine the pose deviation of each slave agent;
S4、对每个从智能体，设定位姿调整阈值，然后利用位姿偏差计算位姿偏差百分比；S4. For each slave agent, set the pose adjustment threshold, and then use the pose deviation to calculate the pose deviation percentage;
S5、选取所有百分比中的最大值，然后进行归一化后，确定每个从智能体的调整幅值；S5. Select the maximum value among all percentages, and then normalize to determine the adjustment amplitude of each slave agent;
S6、对每个从智能体，利用调整幅值进行每个方向幅值的耦合重计算； 利用耦合重计算结果建立各方向的控制律；然后设定插补间隔，利用耦合重计算结果、控制律确定各方向的插补增量；最后设定控制阈值，利用插补增量确定的运动控制量。S6. For each slave agent, use the adjusted amplitude to perform coupling recalculation of the amplitude in each direction; use the results of the coupling recalculation to establish a control law in each direction; then set the interpolation interval, and use the coupling recalculation results to control The interpolation increment in each direction is determined by the law; finally, the control threshold is set, and the motion control amount determined by the interpolation increment is used.
作为本发明的一种优选方案，利用S6中所述的运动控制量，确定每个从智能体的各轮转速。As a preferred solution of the present invention, the rotation speed of each wheel of each slave agent is determined by using the motion control quantity described in S6.
作为本发明的一种优选方案，所述多智能体呈品字形分布。As a preferred solution of the present invention, the multi-agents are distributed in a zigzag shape.
作为本发明的一种优选方案，所述多智能体协同作业过程中通过TCP/IP通信协议以100Hz的周期进行通信，且利用激光扫描雷达实时测量从智能体相对于各自参考点之间的距离和角度。As a preferred solution of the present invention, during the cooperative operation of the multi-agents, the communication is performed at a cycle of 100 Hz through the TCP/IP communication protocol, and the distance between the slave agents and the respective reference points is measured in real time by using laser scanning radar. and angle.
作为本发明的一种优选方案，S6中，对每个从智能体，利用调整幅值进行每个方向幅值的耦合重计算时，根据高度方向的调整幅值，对其他方向的位姿调整偏差进行重计算，获得重计算后的各方向调整幅值。其中利用重计算后的各方向调整幅值建立各方向的控制律。As a preferred solution of the present invention, in S6, when the coupling recalculation of the amplitude in each direction is performed by using the adjustment amplitude for each slave agent, the pose adjustment in other directions is adjusted according to the adjustment amplitude in the height direction. The deviation is recalculated to obtain the recalculated adjustment amplitudes in each direction. The control law of each direction is established by using the recalculated adjustment amplitude of each direction.
作为本发明的一种优选方案，利用重计算后的各方向调整幅值建立各方向的控制律时，首先对重计算后的各方向调整幅值进行归一化，然后建立各方向的指数趋近律。As a preferred solution of the present invention, when using the recalculated adjustment amplitudes in each direction to establish the control law in each direction, first normalize the recalculated adjustment amplitudes in each direction, and then establish the exponential trend of each direction. Near law.
作为本发明的一种优选方案，S6中，当插补间隔大于控制阈值时，运动控制量逐渐增大；当插补间隔不超过控制阈值时，运动控制量逐渐减小。As a preferred solution of the present invention, in S6, when the interpolation interval is greater than the control threshold, the motion control amount gradually increases; when the interpolation interval does not exceed the control threshold, the motion control amount gradually decreases.
实施例：Example:
基于多智能体协同转运实时在线位姿补偿控制方法，多智能体协同转运系统由3个智能体组成，其中前智能体由4个麦克纳姆轮组合实现驱动控制，后两智能体由8个麦克纳姆轮组合实现驱动控制，8麦克纳姆轮布局示意图见图1。Based on the real-time online pose compensation control method of multi-agent cooperative transport, the multi-agent cooperative transport system consists of 3 agents. The combination of Mecanum wheels realizes drive control, and the schematic diagram of the layout of 8 Mecanum wheels is shown in Figure 1.
其中，多智能体协同转运系统的三个智能体之间呈品字形分布并以几何形心O点为中心进行运动，相对分布示意图见图2。以前四麦克纳姆轮组成的智能体为主，后两个八麦克纳姆轮组成的智能体为从，三智能体之间的初始相对 位姿可随实际需求进行柔性化自适应，即图2中的a、b可随意变化，其中a为前主智能体后端面与后从智能体前端面的距离值，b为后两智能体几何中心点之间的距离值。Among them, the three agents of the multi-agent cooperative transport system are distributed in a zigzag shape and move with the geometric centroid O as the center. The schematic diagram of the relative distribution is shown in Figure 2. The agent composed of the first four Mecanum wheels is the main agent, and the agent composed of the last two eight Mecanum wheels is the slave. The initial relative poses between the three agents can be flexibly adapted according to the actual needs, that is, Fig. A and b in 2 can be changed at will, where a is the distance between the rear face of the former master agent and the front face of the rear slave agent, and b is the distance between the geometric center points of the latter two agents.
其中，按照转运对象的实际质量特性选择合适的a、b参数，即形成了多智能体的初始分布设计。根据个智能体的物理模型计算从智能体1中心点O 1在主智能体的坐标系X 0O 0Y 0的初始位姿以及从智能体2中心点O 1在主智能体的坐标系X 0O 0Y 0的理论初始位姿；同时在多智能体协同作业过程中通过TCP/IP通信协议以100Hz的通信周期读取激光扫描雷达实时测量从智能体相对主智能体后端面A、B的轮廓数据并拟合出两个端面轮廓中心在激光扫描雷达的距离和角度数据，并求解从智能体1中心点O 1、从智能体2中心点O 2在主智能体的坐标系X 0O 0Y 0的实时位姿。如图3所示。 Among them, the appropriate a and b parameters are selected according to the actual quality characteristics of the transported object, that is, the initial distribution design of the multi-agent is formed. Calculate the initial pose of the center point O 1 of the slave agent 1 in the coordinate system X 0 O 0 Y 0 of the main agent and the center point O 1 of the slave agent 2 in the coordinate system X of the main agent according to the physical model of each agent The theoretical initial pose of 0 O 0 Y 0 ; at the same time, in the process of multi-agent cooperative operation, the laser scanning radar is read in real time with a communication cycle of 100Hz through the TCP/IP communication protocol, and the slave agent relative to the main agent rear face A and B are measured in real time. and fit the distance and angle data of the two end face contour centers in the laser scanning radar, and solve the coordinate system X 0 from the center point O 1 of the agent 1 and the center point O 2 of the agent 2 in the main agent The real-time pose of O 0 Y 0 . As shown in Figure 3.
其中，以从智能体1的中心O 1建立坐标系X 1O 1Y 1，以从智能体2的中心O 2建立坐标系X 2O 2Y 2，以主智能体后端面中心点O 0建立坐标系X 0O 0Y 0，则可知： Among them, the coordinate system X 1 O 1 Y 1 is established from the center O 1 of the agent 1, the coordinate system X 2 O 2 Y 2 is established from the center O 2 of the agent 2, and the center point O 0 of the rear face of the main agent is used. Establishing the coordinate system X 0 O 0 Y 0 , it can be known that:
从智能体1中心点O 1在主智能体的坐标系X 0O 0Y 0的初始位姿为： The initial pose from the center point O 1 of agent 1 in the coordinate system X 0 O 0 Y 0 of the main agent is:
Figure PCTCN2021129479-appb-000001
Figure PCTCN2021129479-appb-000001
从智能体2中心点O 2在主智能体的坐标系X 0O 0Y 0的初始位姿为： The initial pose from the center point O 2 of agent 2 in the coordinate system X 0 O 0 Y 0 of the main agent is:
Figure PCTCN2021129479-appb-000002
Figure PCTCN2021129479-appb-000002
其中，多智能体协同转运系统在运动过程中，从智能体1的前端面中心点相对于主智能体后端面A、B点的实时测量数据为{(d A1',θ A1'),(d B1',θ B1')}，则可得从智能体1几何中心点O 1在主智能体的坐标系X 0O 0Y 0的实时位姿(d x1',d y1',d z1')： Among them, during the movement of the multi-agent cooperative transport system, the real-time measurement data of the center point of the front face of the agent 1 relative to the points A and B of the rear face of the main agent are {(d A1 ', θ A1 '), ( d B1 ', θ B1 ')}, the real-time pose (d x1 ',d y1 ',d z1 of the geometric center point O 1 of the agent 1 in the coordinate system X 0 O 0 Y 0 of the main agent can be obtained '):
Figure PCTCN2021129479-appb-000003
Figure PCTCN2021129479-appb-000003
Figure PCTCN2021129479-appb-000004
Figure PCTCN2021129479-appb-000004
Figure PCTCN2021129479-appb-000005
Figure PCTCN2021129479-appb-000005
Figure PCTCN2021129479-appb-000006
Figure PCTCN2021129479-appb-000006
从智能体2的前端面中心点相对于主智能体后端面A、B点的实时测量数据为{(d A2',θ A2'),(d B2',θ B2')}，则可得从智能体2几何中心点O 1在主智能体的坐标系X 0O 0Y 0的实时位姿(d x2',d y2',d z2')： From the real-time measurement data of the center point of the front face of the agent 2 relative to the points A and B of the rear face of the main agent as {(d A2 ', θ A2 '), (d B2 ', θ B2 ')}, we can get From the real-time pose (d x2 ',d y2 ',d z2 ') of the geometric center point O 1 of the agent 2 in the coordinate system X 0 O 0 Y 0 of the main agent:
Figure PCTCN2021129479-appb-000007
Figure PCTCN2021129479-appb-000007
Figure PCTCN2021129479-appb-000008
Figure PCTCN2021129479-appb-000008
Figure PCTCN2021129479-appb-000009
Figure PCTCN2021129479-appb-000009
Figure PCTCN2021129479-appb-000010
Figure PCTCN2021129479-appb-000010
计算从智能体相对主智能体后端面的实时位姿(d xi',d yi',d zi')与初始设定位姿(d xi,d yi,d zi)的偏差为(Δε xi,Δε yi,Δε zi): Calculate the deviation between the real-time pose (d xi ',d yi ',d zi ') of the slave agent relative to the back face of the main agent and the initial set pose (d xi ,d yi ,d zi ) as (Δε xi , Δε yi ,Δε zi ):
Figure PCTCN2021129479-appb-000011
Figure PCTCN2021129479-appb-000011
其中，实时根据后两从智能体中心点在主智能体的坐标系X 0O 0Y 0的实时位姿与初始位姿之差联合建立后双智能体协同调整控制策略：首先以同一时刻两智能体的x，y，z三轴位姿偏差记为(Δε 1,Δε 2,Δε 3,Δε 4,Δε 5,Δε 6)，并作为调整控制的输入参数。 Among them, in real time, according to the difference between the real-time pose and the initial pose of the center point of the last two slave agents in the coordinate system X 0 O 0 Y 0 of the master agent, the two agents coordinately adjust the control strategy. The x, y, z three-axis pose deviation of the agent is recorded as (Δε 1 , Δε 2 , Δε 3 , Δε 4 , Δε 5 , Δε 6 ), and is used as the input parameter of the adjustment control.
Figure PCTCN2021129479-appb-000012
Figure PCTCN2021129479-appb-000012
按照设定姿态调整阈值(ξ 1,ξ 2,ξ 3,ξ 4,ξ 5,ξ 6)计算两智能体的三轴位姿偏差数据的百分比(ρ 1,ρ 2,ρ 3,ρ 4,ρ 5,ρ 6)，按照百分比大小排序求得最大偏差百分比ρ max。按照如下步骤进行： According to the set attitude adjustment thresholds (ξ 1 , ξ 2 , ξ 3 , ξ 4 , ξ 5 , ξ 6 ), the percentages (ρ 1 , ρ 2 , ρ 3 , ρ 4 ) of the three-axis pose deviation data of the two agents are calculated , ρ 5 , ρ 6 ), and the maximum deviation percentage ρ max is obtained according to the percentage order. Follow these steps:
Figure PCTCN2021129479-appb-000013
以最大偏差百分比ρ max的轴姿态偏差进行两智能体的各轴调整幅值Δμ i计算，可知有：
Figure PCTCN2021129479-appb-000013
Using the axis attitude deviation of the maximum deviation percentage ρ max to calculate the adjustment amplitude Δμ i of each axis of the two agents, it can be known that:
Figure PCTCN2021129479-appb-000014
Figure PCTCN2021129479-appb-000014
Figure PCTCN2021129479-appb-000015
根据两智能体的各轴调整幅值进行耦合重计算：
Figure PCTCN2021129479-appb-000015
Coupling recalculation is performed according to the adjustment amplitude of each axis of the two agents:
Figure PCTCN2021129479-appb-000016
由z轴姿态调整偏差Δμ z在调整过程中引起的x、y轴位姿偏差：
Figure PCTCN2021129479-appb-000016
The x-axis and y-axis pose deviations caused by the z-axis attitude adjustment deviation Δμ z during the adjustment process:
Figure PCTCN2021129479-appb-000017
Figure PCTCN2021129479-appb-000017
Figure PCTCN2021129479-appb-000018
各轴位姿调整偏差重计算：
Figure PCTCN2021129479-appb-000018
Recalculation of each axis pose adjustment deviation:
Figure PCTCN2021129479-appb-000019
Figure PCTCN2021129479-appb-000019
Figure PCTCN2021129479-appb-000020
根据两智能体耦合重计算后各轴的调整幅值建立各自的控制律：
Figure PCTCN2021129479-appb-000020
According to the adjustment amplitude of each axis after the coupling recalculation of the two agents, the respective control laws are established:
Figure PCTCN2021129479-appb-000021
对各轴调整幅值的归一化处理：
Figure PCTCN2021129479-appb-000021
The normalization processing of the adjustment amplitude of each axis:
δ(i)＝(Δμ' i/ξ i)/max(Δμ' i/ξ i) δ(i)=(Δμ' i /ξ i )/max(Δμ' i /ξ i )
Figure PCTCN2021129479-appb-000022
建立各轴指数趋近律
Figure PCTCN2021129479-appb-000022
Establish the exponential approach law of each axis
l(i)＝(e δ(i)-e -δ(i))/(e δ(i)+e -δ(i)) l(i)=(e δ(i) -e -δ(i) )/(e δ(i) +e -δ(i) )
Figure PCTCN2021129479-appb-000023
以Τ τ为插补间隔，计算各轴的插补增量：
Figure PCTCN2021129479-appb-000023
Taking Τ τ as the interpolation interval, calculate the interpolation increment of each axis:
Δσ i＝Δμ i'Τ τl(i) Δσ i =Δμ i 'Τ τ l(i)
Figure PCTCN2021129479-appb-000024
设当前的各轴给定控制参数为
Figure PCTCN2021129479-appb-000025
以当前控制参数的1/10为阈值为依据对各轴姿态调整插补增量Δσ i设计积分分离PID算法：当插补增量Δσ i大于阈值时，调整的速度输出量应逐渐增大，且误差小时增长率小，误差大时增长率大；当偏差值小于或等于插补增量Δσ i时，调整的速度输出量应逐渐减小，即有：
Figure PCTCN2021129479-appb-000024
Set the current given control parameters of each axis as
Figure PCTCN2021129479-appb-000025
Based on 1/10 of the current control parameter as the threshold value, the interpolation increment Δσ i of each axis is adjusted to design the integral separation PID algorithm: when the interpolation increment Δσ i is greater than the threshold value, the adjusted speed output should gradually increase, And when the error is small, the growth rate is small, and when the error is large, the growth rate is large; when the deviation value is less than or equal to the interpolation increment Δσ i , the adjusted speed output should gradually decrease, namely:
Figure PCTCN2021129479-appb-000026
Figure PCTCN2021129479-appb-000026
Δυ i＝K pi(Δσi-Δσi')+K ii*Δσi+K di*(Δσi-2*Δσi'+Δσi”)) Δυ i =K pi (Δσi-Δσi')+K ii *Δσi+K di *(Δσi-2*Δσi'+Δσi"))
故知姿态调整后的运动控制量为:Therefore, it is known that the motion control amount after attitude adjustment is:
Figure PCTCN2021129479-appb-000027
Figure PCTCN2021129479-appb-000027
故可以求出各个轮的转速：Therefore, the rotational speed of each wheel can be obtained:
Figure PCTCN2021129479-appb-000028
Figure PCTCN2021129479-appb-000028
其中，采用MECHATROLINK_II现场运动总线实现主、从车多轴驱动电机的拓扑连接。根据主、从车所有电机的当前速度V i实际和目标速度V i目标进行二十轴的联动插补控制，实现主、从车的同步规划控制。 Among them, the MECHATROLINK_II field motion bus is used to realize the topological connection of the multi-axis drive motors of the master and slave vehicles. Twenty-axis linkage interpolation control is performed according to the current speed V i of all motors of the master and slave cars and the target speed V i target to realize the synchronous planning control of the master and slave cars.
本发明说明书中未作详细描述的内容属本领域技术人员的公知技术。The content not described in detail in the specification of the present invention belongs to the well-known technology of those skilled in the art.
本发明虽然已以较佳实施例公开如上，但其并不是用来限定本发明，任何本领域技术人员在不脱离本发明的精神和范围内，都可以利用上述揭示的方法和技术内容对本发明技术方案做出可能的变动和修改，因此，凡是未脱离本发明技术方案的内容，依据本发明的技术实质对以上实施例所作的任何简单修改、等同变化及修饰，均属于本发明技术方案的保护范围。Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can use the methods and technical contents disclosed above to improve the present invention without departing from the spirit and scope of the present invention. The technical solutions are subject to possible changes and modifications. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solutions of the present invention belong to the technical solutions of the present invention. protected range.

Claims (8)

  1. 一种基于多智能体协同转运实时在线位姿补偿控制方法，其特征在于，将多智能体中最前端的智能体作为主智能体，其他作为从智能体，具体包括如下步骤：A real-time online pose compensation control method based on multi-agent cooperative transport, characterized in that the front-end agent among the multi-agents is used as the main agent, and the others are used as the slave agents, which specifically includes the following steps:
    S1、每个从智能体在主智能体的后端面选取一个参考点；并确定每个从智能体与该从智能体所选参考点之间的位姿作为初始位姿；S1. Each slave agent selects a reference point on the rear face of the master agent; and determines the pose between each slave agent and the selected reference point of the slave agent as the initial pose;
    S2、在多智能体协同转运过程中，每个从智能体实时获取该从智能体与该从智能体所选参考点之间的位姿作作为实时位姿；S2. During the multi-agent cooperative transport process, each slave agent obtains the pose between the slave agent and the reference point selected by the slave agent in real time as the real-time pose;
    S3、根据所述初始位姿和实时位姿，确定每个从智能体的位姿偏差；S3, according to the initial pose and the real-time pose, determine the pose deviation of each slave agent;
    S4、对每个从智能体，设定位姿调整阈值，然后利用位姿偏差计算位姿偏差百分比；S4. For each slave agent, set the pose adjustment threshold, and then use the pose deviation to calculate the pose deviation percentage;
    S5、选取所有百分比中的最大值，然后进行归一化后，确定每个从智能体的调整幅值；S5. Select the maximum value among all percentages, and then normalize to determine the adjustment amplitude of each slave agent;
    S6、对每个从智能体，利用调整幅值进行每个方向幅值的耦合重计算；利用耦合重计算结果建立各方向的控制律；然后设定插补间隔，利用耦合重计算结果、控制律确定各方向的插补增量；最后设定控制阈值，利用插补增量确定的运动控制量。S6. For each slave agent, use the adjusted amplitude to perform the coupling recalculation of the amplitude in each direction; use the coupling recalculation results to establish the control law in each direction; then set the interpolation interval, use the coupling recalculation results, control The interpolation increment in each direction is determined by the law; finally, the control threshold is set, and the motion control amount determined by the interpolation increment is used.
  2. 根据权利要求1所述的一种基于多智能体协同转运实时在线位姿补偿控制方法，其特征在于，利用S6中所述的运动控制量，确定每个从智能体的各轮转速。A real-time online pose compensation control method based on multi-agent cooperative transport according to claim 1, characterized in that the rotation speed of each slave agent is determined by using the motion control amount described in S6.
  3. 根据权利要求1所述的一种基于多智能体协同转运实时在线位姿补偿控制方法，其特征在于，所述多智能体呈品字形分布。A real-time online pose compensation control method based on multi-agent cooperative transport according to claim 1, wherein the multi-agents are distributed in a glyph shape.
  4. 根据权利要求1所述的一种基于多智能体协同转运实时在线位姿补偿控制方法，其特征在于，所述多智能体协同作业过程中通过TCP/IP通信协议以100Hz的周期进行通信，且利用激光扫描雷达实时测量从智能体相对于各自参考点之间的距离和角度。The real-time online pose compensation control method based on multi-agent cooperative transport according to claim 1, wherein during the multi-agent cooperative operation process, communication is performed at a cycle of 100 Hz through a TCP/IP communication protocol, and Using LiDAR to measure the distance and angle of slave agents relative to their respective reference points in real time.
  5. 根据权利要求1～4之一所述的一种基于多智能体协同转运实时在线位姿补偿控制方法，其特征在于，S6中，对每个从智能体，利用调整幅值进行每个方向幅值的耦合重计算时，根据高度方向的调整幅值，对其他方向的位姿调整偏差进行重计算，获得重计算后的各方向调整幅值。A real-time online pose compensation control method based on multi-agent cooperative transport according to any one of claims 1 to 4, wherein in S6, for each slave agent, the adjustment amplitude is used to perform the amplitude adjustment in each direction. During the coupling recalculation of the value, according to the adjustment amplitude in the height direction, recalculate the pose adjustment deviation in other directions, and obtain the recalculated adjustment amplitude in each direction.
  6. 根据权利要求5所述的一种基于多智能体协同转运实时在线位姿补偿控制方法，其特征在于，S6中，利用重计算后的各方向调整幅值建立各方向的控制律。A real-time online pose compensation control method based on multi-agent cooperative transport according to claim 5, characterized in that, in S6, a control law for each direction is established by using the recalculated amplitude adjustment in each direction.
  7. 根据权利要求6所述的一种基于多智能体协同转运实时在线位姿补偿控制方法，其特征在于，利用重计算后的各方向调整幅值建立各方向的控制律时，首先对重计算后的各方向调整幅值进行归一化，然后建立各方向的指数趋近律。A real-time online pose compensation control method based on multi-agent cooperative transport according to claim 6, characterized in that, when using the recalculated amplitude adjustment in each direction to establish the control law in each direction, The adjustment amplitudes in each direction are normalized, and then the exponential reaching law in each direction is established.
  8. 根据权利要求1～4之一所述的一种基于多智能体协同转运实时在线位姿补偿控制方法，其特征在于，S6中，当插补间隔大于控制阈值时，运动控制量逐渐增大；当插补间隔不超过控制阈值时，运动控制量逐渐减小。The real-time online pose compensation control method based on multi-agent cooperative transport according to one of claims 1 to 4, wherein in S6, when the interpolation interval is greater than the control threshold, the motion control amount gradually increases; When the interpolation interval does not exceed the control threshold, the motion control amount gradually decreases.
PCT/CN2021/129479 2021-02-26 2021-11-09 Real-time online pose compensation control method based on multi-agent cooperative transportation WO2022179180A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110220770.7A CN113031595B (en) 2021-02-26 2021-02-26 Real-time online pose compensation control method based on multi-agent collaborative transfer
CN202110220770.7 2021-02-26

Publications (1)

Publication Number Publication Date
WO2022179180A1 true WO2022179180A1 (en) 2022-09-01

Family

ID=76462136

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/129479 WO2022179180A1 (en) 2021-02-26 2021-11-09 Real-time online pose compensation control method based on multi-agent cooperative transportation

Country Status (2)

Country Link
CN (1) CN113031595B (en)
WO (1) WO2022179180A1 (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113031595B (en) * 2021-02-26 2023-02-03 北京卫星制造厂有限公司 Real-time online pose compensation control method based on multi-agent collaborative transfer

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150353291A1 (en) * 2014-06-09 2015-12-10 Ty-Crop Manufacturing Ltd. Control System for Material Handling Conveyor Vehicle
CN111123985A (en) * 2020-01-14 2020-05-08 北京卫星制造厂有限公司 Multi-vehicle automatic posture adjusting following system based on laser scanning
CN111399509A (en) * 2020-03-24 2020-07-10 华中科技大学 Multi-mobile-robot cooperative transfer control method and system
CN113003229A (en) * 2021-02-26 2021-06-22 北京卫星制造厂有限公司 Heterogeneous characteristic-oriented multi-agent cooperative autonomous transfer system for large equipment
CN113031595A (en) * 2021-02-26 2021-06-25 北京卫星制造厂有限公司 Real-time online pose compensation control method based on multi-agent cooperative transportation

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003048181A (en) * 2001-08-03 2003-02-18 Denso Corp Articulated robot
CN108297101B (en) * 2018-03-20 2020-12-22 四川大学 Multi-joint-arm series robot end pose error detection and dynamic compensation method
CN110253574B (en) * 2019-06-05 2020-11-17 浙江大学 Multi-task mechanical arm pose detection and error compensation method
CN112130582B (en) * 2020-09-10 2021-09-10 北京理工大学 Multi-agent formation forming method
CN112394644B (en) * 2020-12-03 2022-09-02 西北工业大学 Preset time control method of rope system multi-unmanned aerial vehicle cooperative operation system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150353291A1 (en) * 2014-06-09 2015-12-10 Ty-Crop Manufacturing Ltd. Control System for Material Handling Conveyor Vehicle
CN111123985A (en) * 2020-01-14 2020-05-08 北京卫星制造厂有限公司 Multi-vehicle automatic posture adjusting following system based on laser scanning
CN111399509A (en) * 2020-03-24 2020-07-10 华中科技大学 Multi-mobile-robot cooperative transfer control method and system
CN113003229A (en) * 2021-02-26 2021-06-22 北京卫星制造厂有限公司 Heterogeneous characteristic-oriented multi-agent cooperative autonomous transfer system for large equipment
CN113031595A (en) * 2021-02-26 2021-06-25 北京卫星制造厂有限公司 Real-time online pose compensation control method based on multi-agent cooperative transportation

Also Published As

Publication number Publication date
CN113031595A (en) 2021-06-25
CN113031595B (en) 2023-02-03

Similar Documents

Publication Publication Date Title
WO2022179179A1 (en) Multi-agent collaborative autonomous transfer system for large equipment having heterogeneous characteristic
CN109514133B (en) 3D curve welding seam autonomous teaching method of welding robot based on line structure light perception
CN110653826B (en) Real-time robot trajectory planning method oriented to conveyor belt target following
CN108594815B (en) Staged wheeled robot moving path planning method
CN106647623B (en) The smooth interpolating method of five-axle linkage that a kind of geometric accuracy and linking speed optimize
CN105183009B (en) A kind of redundant mechanical arm method for controlling trajectory
CN110315396B (en) Industrial robot constant-force grinding and polishing method based on big data
CN113341728B (en) Anti-noise type return-to-zero neural network four-wheel mobile mechanical arm trajectory tracking control method
CN108638068B (en) Design method of flying robot control system with redundant mechanical arm
CN111308884A (en) Multi-steering wheel cooperative control method for omnidirectional moving AGV
WO2022179180A1 (en) Real-time online pose compensation control method based on multi-agent cooperative transportation
CN112685858B (en) Forging piece correcting and adjusting method based on three-dimensional scanning auxiliary positioning
CN114523477A (en) Joint pose calibration method, system and storage medium
CN113919100A (en) Treatment method based on increase and decrease of surface quality of composite manufacturing process
CN107363832B (en) Industrial robot feedforward moment real-time calculation method
Gao et al. Time-optimal trajectory planning of industrial robots based on particle swarm optimization
CN107560620A (en) A kind of method for path navigation and chip and robot
CN114043479A (en) Error-correcting floor grinding and polishing robot track deviation correcting method and device
CN109032128B (en) Triangular formation control method of discrete multiple AGV non-particle system
CN107085432B (en) Target track tracking method of mobile robot
CN113467475B (en) Mecanum wheel omnidirectional mobile robot track tracking sliding mode control method
CN110940351A (en) Robot precision compensation method based on parameter dimension reduction identification
CN113059561B (en) Control method of adsorption robot
CN113985738A (en) Gradient neural network cooperative control of non-convex constraint omnidirectional four-wheel mobile mechanical arm repetitive motion
Yang et al. Collision avoidance trajectory planning for a dual-robot system: using a modified APF method

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21927604

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21927604

Country of ref document: EP

Kind code of ref document: A1