CN117021101A - Multi-arm path planning method for belt conveyor dismounting robot - Google Patents
Multi-arm path planning method for belt conveyor dismounting robot Download PDFInfo
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Abstract
Description
技术领域Technical field
本发明涉及一种用于带式输送机拆卸任务的多臂路径规划算法,主要应用于多机械臂的路径规划,属于机器人技术领域。The invention relates to a multi-arm path planning algorithm for belt conveyor disassembly tasks, is mainly used in path planning of multi-manipulators, and belongs to the field of robotic technology.
背景技术Background technique
带式输送机是井工煤矿重要运输设备,需要随着采掘面推进、工作面回撤及自身磨损程度而随时进行拆卸。现有拆卸主要依靠人工作业,存在难度大、效率低、易发生危险等问题,研究并开发带式输送机智能化拆卸设备对提高井下生产安全和效率均具有重要意义;目前一些起重机械设备、多自由度的机械臂等均可实现拆卸作业,但是对于带式输送机这种体积较大、质量较重的物体,双机械臂作业比单机械臂更具有优势,但随之而来的是由于任务复杂、运动耦合和多机械臂避障等带来的机械臂路径规划困难。因此,多机械臂的路径规划问题成为学者们的重要研究问题。因此针对多机械臂进行路径规划研究具有重要意义。The belt conveyor is an important transportation equipment in underground coal mines. It needs to be disassembled at any time as the mining surface advances, the working surface withdraws and its own wear and tear increases. Existing disassembly mainly relies on manual work, which has problems such as difficulty, low efficiency, and prone to danger. Research and development of intelligent disassembly equipment for belt conveyors is of great significance to improving the safety and efficiency of underground production; currently some lifting machinery Equipment, multi-degree-of-freedom robotic arms, etc. can all perform disassembly operations. However, for larger and heavier objects such as belt conveyors, dual robotic arm operations have more advantages than single robotic arms, but there are attendant consequences. The main reason is the difficulty of robot arm path planning due to task complexity, motion coupling, and obstacle avoidance of multiple robot arms. Therefore, the path planning problem of multi-robot arms has become an important research issue for scholars. Therefore, it is of great significance to conduct path planning research on multi-robot arms.
路径规划是保障机械臂安全作业的关键技术,目前国内外学者已提出许多路径规划算法如人工势场法、蚁群算法、A*算法、遗传算法和RRT算法等。其中,RRT算法是一种高效、灵活且适用于各种场景的路径规划算法,由于具有快速探索、简单实现、自适应性强、适用于复杂环境和可扩展性强等优势,便于机械臂在高维空间和复杂约束下进行路径规划,近年来被广泛应用于机器人路径规划领域。但传统RRT算法也存在固有缺陷,其全局随机采样会导致计算资源浪费,算法收敛慢,且生成路径不平滑,难以被机器人直接执行,因此对传统RRT算法进行改进对机器人路径规划研究具有重要意义。Path planning is a key technology to ensure the safe operation of robotic arms. At present, domestic and foreign scholars have proposed many path planning algorithms such as artificial potential field method, ant colony algorithm, A* algorithm, genetic algorithm and RRT algorithm. Among them, the RRT algorithm is an efficient, flexible and suitable path planning algorithm for various scenarios. Due to its advantages of fast exploration, simple implementation, strong adaptability, suitability for complex environments and strong scalability, it is convenient for the robot arm to operate in various scenarios. Path planning under high-dimensional space and complex constraints has been widely used in the field of robot path planning in recent years. However, the traditional RRT algorithm also has inherent flaws. Its global random sampling will lead to a waste of computing resources, the algorithm converges slowly, and the generated path is not smooth, making it difficult to be directly executed by the robot. Therefore, improving the traditional RRT algorithm is of great significance to the research of robot path planning. .
发明内容Contents of the invention
本发明针对带式输送机拆卸机器人的手臂在拆卸任务中的路径规划问题,提出了一种多臂路径规划算法,针对传统RRT算法进行了改进,在传统RRT算法的节点更新过程中引入节点权重函数来引导探索过程中新节点的生成,能够改善路径规划过程中的避障能力和探索无方向性,有效减少无效采样点;本发明使用关节空间内的机械臂关节角度组来表示机械臂位置信息,将其应用于引入改进后的RRT算法中,避免了逆运动学求解的繁琐运算,提高了规划效率;最后提出主被动双树拓展方法,主机械臂使用路径规划算法进行主动探索,从机械臂来被动验证,探索过程和验证过程同时进行,实现了多臂协同运动。详见下文描述:Aiming at the path planning problem of the belt conveyor disassembly robot's arms in the disassembly task, the present invention proposes a multi-arm path planning algorithm, which is improved on the traditional RRT algorithm and introduces node weights in the node update process of the traditional RRT algorithm. function to guide the generation of new nodes during the exploration process, which can improve the obstacle avoidance ability and non-directional exploration in the path planning process, and effectively reduce invalid sampling points; the present invention uses the robot arm joint angle group in the joint space to represent the robot arm position Information is introduced into the improved RRT algorithm, which avoids the cumbersome calculations of inverse kinematics solution and improves planning efficiency. Finally, an active and passive dual-tree expansion method is proposed. The main manipulator uses a path planning algorithm to actively explore, from The mechanical arm is used for passive verification, and the exploration process and verification process are carried out simultaneously, realizing multi-arm coordinated movement. See description below for details:
一种用于带式输送机拆卸机器人的多臂路径规划方法,包括以下步骤:A multi-arm path planning method for a belt conveyor disassembly robot, including the following steps:
步骤1、针对带式输送机拆卸任务进行运动学模型建立;Step 1. Establish a kinematic model for the belt conveyor disassembly task;
步骤2、初始化机械臂初始位姿和目标位姿,确定环境中障碍物基本信息;Step 2. Initialize the initial pose and target pose of the robotic arm, and determine the basic information about obstacles in the environment;
步骤3、在传统RRT算法基础上,使用Sobol序列来进行采样点生成,并引入节点权重函数来拓展新节点;Step 3. Based on the traditional RRT algorithm, use the Sobol sequence to generate sampling points, and introduce the node weight function to expand new nodes;
步骤4、引入主被动拓展,通过主机械臂采样和从机械臂验证的方式,实现多机械臂的协调规划;Step 4. Introduce active and passive expansion, and achieve coordinated planning of multiple robot arms through master robot arm sampling and slave robot arm verification;
步骤5、采用基于最小二乘法的多项式拟合方法来对路径进行优化处理,使机械臂运动更加平滑。Step 5. Use the polynomial fitting method based on the least squares method to optimize the path to make the robot arm movement smoother.
进一步的,步骤2具体包括如下内容:Further, step 2 specifically includes the following content:
提出一种主被动拓展RRT算法来进行双机械臂的路径规划探索,其中主机械臂R使用主动生长随机树TR,从机械臂L使用被动生长随机树TL,并在关节空间内对随机树进行扩展,定义主机械臂的初始关节角度为xstart,目标位置为xgoal,从机械臂的初始关节角度为x′start,目标位置为x′goal,并将其作为起始点和目标点分别加入随机树TR和TL中。An active-passive extended RRT algorithm is proposed to explore the path planning of dual manipulators. The master manipulator R uses an active growing random tree T R , and the slave manipulator L uses a passive growing random tree T L , and randomly grows a random tree T L in the joint space. The tree is expanded to define the initial joint angle of the main robotic arm as x start , the target position as x goal , the initial joint angle of the slave robotic arm as x′ start , and the target position as x′ goal , and use them as the starting point and target point. Add to random trees T R and TL respectively.
进一步的,步骤3具体包括如下内容:Further, step 3 specifically includes the following content:
多臂关节空间路径规划通常涉及到较高的维度,由于维度过高,计算的复杂度增加,这使得机械臂难以在规定时间内完成路径规划;虽然在各自关节空间中进行RRT采样可以完成路径规划,但无法实现机械臂间的协调;步骤3的具体操作步骤包括:Multi-arm joint space path planning usually involves higher dimensions. Because the dimensions are too high, the calculation complexity increases, which makes it difficult for the manipulator to complete path planning within the specified time; although RRT sampling in their respective joint spaces can complete the path Planning, but coordination between robotic arms cannot be achieved; the specific steps in step 3 include:
S31、相对于伪随机序列,Sobol序列能够在保证随机性的前提下,使采样点分布更加均匀,在关节空间内定义采样点xsample,由机械臂各关节角度组成,皆由Sobol序列生成,将随机树中与采样点欧氏距离最小的节点作为最近点,将该点记为xnearest;S31. Compared with the pseudo-random sequence, the Sobol sequence can make the distribution of sampling points more uniform while ensuring randomness. The sampling point x sample is defined in the joint space, consisting of the joint angles of the robotic arm, and is generated by the Sobol sequence. The node with the smallest Euclidean distance from the sampling point in the random tree is regarded as the nearest point, and the point is recorded as x nearest ;
S32、引入节点权重函数,通过该函数来自适应调整在随机采样点方向和目标点方向的拓展权重;在无障碍物的情况下,赋予目标点更高的权重,以引导随机树朝着目标点方向扩展,而在有障碍物的情况下,赋予随机采样点更高的权重,S32. Introduce the node weight function, and use this function to adaptively adjust the expansion weight in the direction of the random sampling point and the target point; in the absence of obstacles, give the target point a higher weight to guide the random tree towards the target point. direction expansion, and in the case of obstacles, random sampling points are given a higher weight,
以引导随机树绕过障碍物;新采样点的拓展公式为:Guide the random tree to bypass obstacles; the expanded formula of the new sampling point is:
其中,为采样点方向的权重,由下式决定,in, is the weight of the direction of the sampling point, determined by the following formula,
m初始值设为0,n初始值设为1,k1∈(0,1)反映采样点方向权重的变化,k2∈(0,1)表示目标点方向的权重,当环境复杂障碍物较多时,k2应取值较小,反之较大。The initial value of m is set to 0, the initial value of n is set to 1, k 1 ∈ (0,1) reflects the change of the direction weight of the sampling point, k 2 ∈ (0,1) represents the weight of the target point direction, when the environment is complex and obstacles When the number is large, the value of k 2 should be smaller, otherwise it should be larger.
进一步的,步骤4具体包括如下内容:Further, step 4 specifically includes the following content:
S41、将关节空间内随机树的更新节点xnew代入到主机械臂R正运动学求解得到笛卡尔坐标系的节点信息;S41. Substitute the update node x new of the random tree in the joint space into the forward kinematics solution of the main manipulator R to obtain the node information of the Cartesian coordinate system;
S42、拆卸机械臂在工作空间中遇到的障碍物主要是连杆支架和碎煤料,分别使用长方形包络盒法和球形包络盒法进行碰撞检测;S42. The obstacles encountered by the dismantling robot arm in the work space are mainly connecting rod brackets and crushed coal materials. The rectangular envelope box method and the spherical envelope box method are used for collision detection respectively;
S43、判断机械臂各关节是否与障碍物发生碰撞,若否,将xnew加入到主动生长随机树中,并定义xnearest为xnew的父节点;若是,则剔除该节点并返回重新选择节点;S43. Determine whether each joint of the robotic arm collides with an obstacle. If not, add x new to the actively growing random tree, and define x nearest as the parent node of x new ; if so, delete the node and return to reselect the node. ;
S44、将主动生长随机树中的更新节点xnew对应的被动生长随机树中的x′new代入到从机械臂L正运动学求解;S44. Substitute x′ new in the passively grown random tree corresponding to the update node x new in the actively growing random tree into the forward kinematics solution of the mechanical arm L;
S45、通过包络盒法来进行碰撞检测,判断机械臂各关节是否与障碍物发生碰撞,若否,则将x′new加入到随机树TL中,并定义x′nearest为x′new的父节点;若是,将xnew从TR中剔除,并将x′new从TL中剔除,返回重新选择节点。S45. Use the envelope box method to perform collision detection to determine whether each joint of the robotic arm collides with an obstacle. If not, add x′ new to the random tree T L , and define x′ nearest as x′ new Parent node; if so, remove x new from TR , remove x′ new from T L , and return to reselect the node.
本发明提供的技术方案的有益效果是:The beneficial effects of the technical solution provided by the present invention are:
1)针对传统RRT算法进行了改进,在传统RRT算法的节点更新过程中引入节点权重函数来引导探索过程中新节点的生成,能够改善路径规划过程中的避障能力和探索无方向性,有效减少无效采样点;1) Improvements have been made to the traditional RRT algorithm. In the node update process of the traditional RRT algorithm, the node weight function is introduced to guide the generation of new nodes during the exploration process, which can improve the obstacle avoidance ability and non-directional exploration during the path planning process, and is effective Reduce invalid sampling points;
2)本发明使用关节空间内的机械臂关节角度组来表示机械臂位置信息,将其应用于引入改进后的RRT算法中,避免了逆运动学求解的繁琐运算,提高了规划效率;2) The present invention uses the mechanical arm joint angle group in the joint space to represent the mechanical arm position information, and applies it to the improved RRT algorithm, which avoids the cumbersome calculation of inverse kinematics solution and improves planning efficiency;
3)提出主被动双树拓展方法,主机械臂使用路径规划算法进行主动探索,从机械臂来被动验证,探索过程和验证过程同时进行,实现了多机械臂臂协同运动。3) An active and passive dual-tree expansion method is proposed. The main robotic arm uses a path planning algorithm to actively explore, and the slave robotic arm performs passive verification. The exploration process and verification process are carried out simultaneously, achieving coordinated movement of multiple robotic arms.
附图说明Description of the drawings
图1是本发明中用于带式输送机拆卸机器人的多臂路径规划方法框架图;Figure 1 is a framework diagram of the multi-arm path planning method for a belt conveyor disassembly robot in the present invention;
图2是本发明提供的新节点生成过程示意图;Figure 2 is a schematic diagram of the new node generation process provided by the present invention;
图3是本发明提供的主被动双树拓展采样过程示意图;Figure 3 is a schematic diagram of the active and passive dual tree expansion sampling process provided by the present invention;
图4是本发明使用的带式输送机拆卸机器人模型示意图;Figure 4 is a schematic diagram of the belt conveyor disassembly robot model used in the present invention;
图5是实施例中纵梁工序机械臂末端路径结果示意图;Figure 5 is a schematic diagram of the end path result of the robotic arm in the longitudinal beam process in the embodiment;
图6是实施例中纵梁工序机械臂R各关节角度变化曲线图;Figure 6 is a graph showing changes in the angles of each joint of the robotic arm R during the longitudinal beam process in the embodiment;
图7是实施例中纵梁工序机械臂L各关节角度变化曲线图;Figure 7 is a graph showing changes in the angles of each joint of the robotic arm L during the longitudinal beam process in the embodiment;
图8是实施例中纵梁工序机械臂R末端位移变化结果图;Figure 8 is a diagram showing the displacement change results of the end of the robotic arm R in the longitudinal beam process in the embodiment;
图9是实施例中纵梁工序机械臂L末端位移变化结果图;Figure 9 is a diagram showing the displacement change results of the end of the robotic arm L during the longitudinal beam process in the embodiment;
图10是实施例中H架工序机械臂末端路径结果示意图;Figure 10 is a schematic diagram of the end path result of the robotic arm in the H frame process in the embodiment;
图11是实施例中H架工序机械臂R各关节角度变化曲线图;Figure 11 is a graph showing changes in angles of each joint of the H-frame process robot arm R in the embodiment;
图12是实施例中H架工序机械臂L各关节角度变化曲线图;Figure 12 is a graph showing changes in angles of each joint of the robotic arm L in the H-frame process in the embodiment;
图13是实施例中H架工序机械臂R末端位移变化结果图;Figure 13 is a diagram showing the displacement change results of the end of the robotic arm R in the H frame process in the embodiment;
图14是实施例中H架工序机械臂L末端位移变化结果图。Figure 14 is a diagram showing the displacement change results of the end of the robot arm L in the H frame process in the embodiment.
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作详细说明。In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings.
一种用于带式输送机拆卸机器人的多臂路径规划方法,其算法框架如图1,具体方法包括:A multi-arm path planning method for belt conveyor disassembly robots. The algorithm framework is shown in Figure 1. The specific methods include:
步骤1、针对如图4所示的带式输送机拆卸机器人进行运动学模型建立;Step 1. Establish a kinematics model for the belt conveyor disassembly robot shown in Figure 4;
步骤2、初始化机械臂初始位姿和目标位姿,确定环境中障碍物基本信息;Step 2. Initialize the initial pose and target pose of the robotic arm, and determine the basic information about obstacles in the environment;
步骤3、在传统RRT算法基础上,使用Sobol序列来进行采样点生成,并引入节点权重函数来拓展新节点,新节点生成过程如图2所示;Step 3. Based on the traditional RRT algorithm, use the Sobol sequence to generate sampling points, and introduce the node weight function to expand new nodes. The new node generation process is shown in Figure 2;
步骤4、引入主被动拓展,通过主机械臂采样和从机械臂验证的方式,实现多机械臂的协调规划,主被动双树拓展采样过程如图3所示;Step 4. Introduce active and passive expansion, and achieve coordinated planning of multiple robot arms through master robotic arm sampling and slave robotic arm verification. The active and passive dual-tree expansion sampling process is shown in Figure 3;
步骤5、采用基于最小二乘法的多项式拟合方法来对路径进行优化处理,使机械臂运动更加平滑。Step 5. Use the polynomial fitting method based on the least squares method to optimize the path to make the robot arm movement smoother.
步骤2具体包括如下内容:Step 2 specifically includes the following:
提出一种主被动拓展RRT算法来进行双机械臂的路径规划探索,其中主机械臂R使用主动生长随机树TR,从机械臂L使用被动生长随机树TL,并在关节空间内对随机树进行扩展,定义主机械臂的初始关节角度为xstart,目标位置为xgoal,从机械臂的初始关节角度为x′start,目标位置为x′goal,并将其作为起始点和目标点分别加入随机树TR和TL中。An active-passive extended RRT algorithm is proposed to explore the path planning of dual manipulators. The master manipulator R uses an active growing random tree T R , and the slave manipulator L uses a passive growing random tree T L , and randomly grows a random tree T L in the joint space. The tree is expanded to define the initial joint angle of the main robotic arm as x start , the target position as x goal , the initial joint angle of the slave robotic arm as x′ start , and the target position as x′ goal , and use them as the starting point and target point. Add to random trees T R and TL respectively.
步骤3具体包括如下内容:Step 3 specifically includes the following:
多臂关节空间路径规划通常涉及到较高的维度,由于维度过高,计算的复杂度增加,这使得机械臂难以在规定时间内完成路径规划;虽然在各自关节空间中进行RRT采样可以完成路径规划,但无法实现机械臂间的协调;步骤3的具体操作步骤包括:Multi-arm joint space path planning usually involves higher dimensions. Because the dimensions are too high, the calculation complexity increases, which makes it difficult for the manipulator to complete path planning within the specified time; although RRT sampling in their respective joint spaces can complete the path Planning, but coordination between robotic arms cannot be achieved; the specific steps in step 3 include:
S31、相对于伪随机序列,Sobol序列能够在保证随机性的前提下,使采样点分布更加均匀,在关节空间内定义采样点xsample,由机械臂各关节角度组成,皆由Sobol序列生成,将随机树中与采样点欧氏距离最小的节点作为最近点,将该点记为xnearest;S31. Compared with the pseudo-random sequence, the Sobol sequence can make the distribution of sampling points more uniform while ensuring randomness. The sampling point x sample is defined in the joint space, consisting of the joint angles of the robotic arm, and is generated by the Sobol sequence. The node with the smallest Euclidean distance from the sampling point in the random tree is regarded as the nearest point, and the point is recorded as x nearest ;
S32、引入节点权重函数,通过该函数来自适应调整在随机采样点方向和目标点方向的拓展权重;在无障碍物的情况下,赋予目标点更高的权重,以引导随机树朝着目标点方向扩展,而在有障碍物的情况下,赋予随机采样点更高的权重,以引导随机树绕过障碍物;新采样点的拓展公式为:S32. Introduce the node weight function, and use this function to adaptively adjust the expansion weight in the direction of the random sampling point and the target point; in the absence of obstacles, give the target point a higher weight to guide the random tree towards the target point. direction expansion, and in the case of obstacles, random sampling points are given higher weights to guide the random tree to bypass obstacles; the expansion formula of new sampling points is:
其中,为采样点方向的权重,由下式决定,in, is the weight of the direction of the sampling point, determined by the following formula,
m初始值设为0,n初始值设为1,k1∈(0,1)反映采样点方向权重的变化,k2∈(0,1)表示目标点方向的权重,当环境复杂障碍物较多时,k2应取值较小,反之较大。The initial value of m is set to 0, the initial value of n is set to 1, k 1 ∈ (0,1) reflects the change of the direction weight of the sampling point, k 2 ∈ (0,1) represents the weight of the target point direction, when the environment is complex and obstacles When the number is large, the value of k 2 should be smaller, otherwise it should be larger.
步骤4具体包括如下内容:Step 4 specifically includes the following:
S41、将关节空间内随机树的更新节点xnew代入到主机械臂R正运动学求解得到笛卡尔坐标系的节点信息;S41. Substitute the update node x new of the random tree in the joint space into the forward kinematics solution of the main manipulator R to obtain the node information of the Cartesian coordinate system;
S42、拆卸机械臂在工作空间中遇到的障碍物主要是连杆支架和碎煤料,分别使用长方形包络盒法和球形包络盒法进行碰撞检测;S42. The obstacles encountered by the dismantling robot arm in the work space are mainly connecting rod brackets and crushed coal materials. The rectangular envelope box method and the spherical envelope box method are used for collision detection respectively;
S43、判断机械臂各关节是否与障碍物发生碰撞,若否,将xnew加入到主动生长随机树中,并定义xnearest为xnew的父节点;若是,则剔除该节点并返回重新选择节点;S43. Determine whether each joint of the robotic arm collides with an obstacle. If not, add x new to the actively growing random tree, and define x nearest as the parent node of x new ; if so, delete the node and return to reselect the node. ;
S44、将主动生长随机树中的更新节点xnew对应的被动生长随机树中的x′new代入到从机械臂L正运动学求解;S44. Substitute x′ new in the passively grown random tree corresponding to the update node x new in the actively growing random tree into the forward kinematics solution of the mechanical arm L;
S45、通过包络盒法来进行碰撞检测,判断机械臂各关节是否与障碍物发生碰撞,若否,则将x′new加入到随机树TL中,并定义x′nearest为x′new的父节点;若是,将xnew从TR中剔除,并将x′new从TL中剔除,返回重新选择节点。S45. Use the envelope box method to perform collision detection to determine whether each joint of the robotic arm collides with an obstacle. If not, add x′ new to the random tree T L , and define x′ nearest as x′ new Parent node; if so, remove x new from TR , remove x′ new from T L , and return to reselect the node.
本发明方法针对带式输送机拆卸任务,属于机器人领域。为使本领域技术人员更好的理解本发明,下面结合具体实施例,对带式输送机拆卸机器人的多臂路径规划算法进行详细说明。The method of the invention is aimed at the disassembly task of the belt conveyor and belongs to the field of robots. In order to enable those skilled in the art to better understand the present invention, the multi-arm path planning algorithm of the belt conveyor disassembly robot will be described in detail below with reference to specific embodiments.
以纵梁拆卸任务为例,纵梁拆卸工序由同侧的两个机械臂完成,以图4中机械臂①和机械臂②为例进行仿真,定义机械臂②为主臂,记作R,定义机械臂①为从臂,记作L。采用主被动拓展RRT算法对纵梁工序进行仿真,得到机械臂末端路径如图5所示,机械臂关节角度如图6、图7所示,两机械臂在x,y,z方向的位移如图8、图9所示,两机械臂各关节角度变化相同,得到的机械臂末端路径在x和z方向相同,在y方向上保持1.4m的距离,机械臂可以完成纵梁的拆卸-放置工作,验证了本发明的可行性。Take the longitudinal beam disassembly task as an example. The longitudinal beam disassembly process is completed by two robotic arms on the same side. Take the robotic arm ① and robotic arm ② in Figure 4 as an example to simulate. The robotic arm ② is defined as the main arm, denoted as R. Define robotic arm ① as the slave arm, denoted as L. The active and passive extended RRT algorithm is used to simulate the longitudinal beam process, and the end path of the robotic arm is shown in Figure 5. The joint angles of the robotic arms are shown in Figures 6 and 7. The displacements of the two robotic arms in the x, y, and z directions are as follows As shown in Figure 8 and Figure 9, the angles of the joints of the two robotic arms change the same. The end paths of the robotic arms are the same in the x and z directions, and a distance of 1.4m is maintained in the y direction. The robotic arms can complete the disassembly and placement of the longitudinal beams. The work verified the feasibility of the invention.
H架拆卸工序由对侧的两个机械臂完成,本节以机械臂②和机械臂④为例进行仿真,定义机械臂②为主臂,记作R,定义机械臂④为从臂,记作L。采用主被动拓展RRT算法对H架工序进行仿真,得到机械臂末端路径如图10所示,机械臂关节角度如图11、图12所示,两机械臂在x,y,z方向的位移如图13、图14所示,两机械臂关节角度变化相反,得到的机械臂末端路径在y和z方向相同,在x方向上关于x=0对称,机械臂可以完成H架的推倒工作,验证了本发明的可行性。The disassembly process of the H frame is completed by the two robotic arms on the opposite side. This section takes the robotic arm ② and the robotic arm ④ as examples for simulation. The robotic arm ② is defined as the master arm, denoted by R, and the robotic arm ④ is defined as the slave arm, denoted by R. Make L. The active and passive extended RRT algorithm is used to simulate the H-frame process, and the end path of the robotic arm is shown in Figure 10. The joint angle of the robotic arm is shown in Figures 11 and 12. The displacements of the two robotic arms in the x, y, and z directions are as follows As shown in Figure 13 and Figure 14, the joint angles of the two robotic arms change in opposite directions. The obtained end paths of the robotic arms are the same in the y and z directions and are symmetrical about x=0 in the x direction. The robotic arms can complete the work of knocking down the H frame. Verification The feasibility of the present invention.
以上所述仅为本发明的较佳实施例,并不用以限制本发明,凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection of the present invention. within the range.
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