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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- 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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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Abstract
The invention discloses a multi-arm path planning method for a belt conveyor disassembly robot, which comprises the following steps: the method is used for solving the path planning problem of the arm of the belt conveyor dismantling robot in the dismantling task, and comprises the steps of establishing a kinematic model of the belt conveyor dismantling task; the traditional RRT algorithm is improved, a node weight function is introduced in the node updating process of the traditional RRT algorithm to guide the generation of new nodes in the exploration process, the obstacle avoidance capability in the path planning process and the exploration non-directionality are improved, and invalid sampling points are effectively reduced; according to the method, the mechanical arm joint angle group in the joint space is used for representing the mechanical arm position information, and the mechanical arm joint angle group is applied to the RRT algorithm after the improvement is introduced, so that complicated operation of inverse kinematics solution is avoided, and the planning efficiency is improved; and finally, an active and passive dual-tree expansion method is provided, the active exploration is carried out on the main mechanical arm by using a path planning algorithm, the passive verification is carried out on the auxiliary mechanical arm, the exploration process and the verification process are carried out simultaneously, and the multi-arm cooperative motion is realized.
Description
Technical Field
The invention relates to a multi-arm path planning algorithm for a disassembly task of a belt conveyor, which is mainly applied to path planning of multiple mechanical arms and belongs to the technical field of robots.
Background
The belt conveyor is important transportation equipment for underground coal mines, and needs to be detached at any time along with the pushing of the mining face, the withdrawing of the working face and the self-abrasion degree. The existing disassembly mainly depends on manual operation, has the problems of high difficulty, low efficiency, easy danger and the like, and has important significance in researching and developing intelligent disassembly equipment of the belt conveyor to improve the underground production safety and efficiency; at present, some lifting mechanical equipment, mechanical arms with multiple degrees of freedom and the like can realize disassembly operation, but for an object with larger volume and heavier mass of a belt conveyor, the double-mechanical-arm operation has advantages compared with the single mechanical arm, but the path planning of the mechanical arm is difficult due to complex tasks, motion coupling, obstacle avoidance of the multiple mechanical arms and the like. Therefore, the path planning problem of multiple mechanical arms becomes an important research problem for students. Therefore, the method has important significance in path planning research for multiple mechanical arms.
The path planning is a key technology for guaranteeing the safety operation of the mechanical arm, and at present, scholars at home and abroad have proposed a plurality of path planning algorithms such as an artificial potential field method, an ant colony algorithm, an A-x algorithm, a genetic algorithm, an RRT algorithm and the like. The RRT algorithm is an efficient, flexible and applicable path planning algorithm for various scenes, has the advantages of quick exploration, simple implementation, strong self-adaptability, suitability for complex environments, strong expandability and the like, is convenient for a mechanical arm to carry out path planning under high-dimensional space and complex constraint, and is widely applied to the field of robot path planning in recent years. However, the conventional RRT algorithm also has inherent defects, such as waste of computational resources, slow algorithm convergence, unsmooth generation path, and difficulty in direct execution by a robot, so improvement of the conventional RRT algorithm has important significance for path planning research of the robot.
Disclosure of Invention
Aiming at the problem of path planning of an arm of a belt conveyor dismantling robot in a dismantling task, the invention provides a multi-arm path planning algorithm, improves the traditional RRT algorithm, introduces a node weight function in the node updating process of the traditional RRT algorithm to guide the generation of new nodes in the exploration process, can improve the obstacle avoidance capability in the path planning process and explore nondirectionality, and effectively reduces invalid sampling points; according to the invention, the mechanical arm joint angle group in the joint space is used for representing the mechanical arm position information, and the mechanical arm joint angle group is applied to the RRT algorithm after the improvement is introduced, so that complicated operation of inverse kinematics solution is avoided, and the planning efficiency is improved; and finally, an active and passive dual-tree expansion method is provided, the active exploration is carried out on the main mechanical arm by using a path planning algorithm, the passive verification is carried out on the auxiliary mechanical arm, the exploration process and the verification process are carried out simultaneously, and the multi-arm cooperative motion is realized. The details are described below:
a multi-arm path planning method for a belt conveyor disassembly robot, comprising the steps of:
step 1, establishing a kinematic model aiming at a disassembly task of a belt conveyor;
step 2, initializing an initial pose and a target pose of the mechanical arm, and determining basic information of obstacles in the environment;
step 3, on the basis of a traditional RRT algorithm, a Sobol sequence is used for generating sampling points, and a node weight function is introduced to expand new nodes;
step 4, introducing active and passive expansion, and realizing coordinated planning of multiple mechanical arms in a mode of sampling by the master mechanical arm and verifying by the slave mechanical arm;
and 5, optimizing the path by adopting a polynomial fitting method based on a least square method, so that the movement of the mechanical arm is smoother.
Further, the step 2 specifically includes the following:
an active-passive expansion RRT algorithm is provided for double machineryPath planning exploration of arms, where a master robotic arm R uses actively growing random tree T R From arm L use passive growth random tree T L Expanding the random tree in the joint space, and defining the initial joint angle of the main mechanical arm as x start The target position is x goal The initial joint angle of the slave mechanical arm is x' start The target position is x' goal And takes the same as a starting point and a target point to be respectively added into a random tree T R And T L Is a kind of medium.
Further, the step 3 specifically includes the following:
multi-arm joint space path planning generally involves higher dimensions, and the complexity of computation increases due to the excessively high dimensions, which makes it difficult for a robotic arm to complete path planning within a prescribed time; although the path planning can be completed by performing RRT sampling in the respective joint space, coordination between mechanical arms cannot be realized; the specific operation steps of the step 3 comprise:
s31, compared with a pseudo-random sequence, the Sobol sequence can enable sampling points to be distributed more uniformly on the premise of guaranteeing randomness, and define sampling points x in joint space sample The method comprises the steps of forming each joint angle of a mechanical arm by a Sobol sequence, taking a node with the smallest Euclidean distance with a sampling point in a random tree as a closest point, and marking the point as x nearest ；
S32, introducing a node weight function, and adaptively adjusting the expansion weights in the random sampling point direction and the target point direction through the function; in the case of no obstacle, giving higher weight to the target point to guide the random tree to expand toward the target point, while in the case of obstacle, giving higher weight to the random sampling point,
to guide the random tree around the obstacle; the expansion formula of the new sampling point is as follows:
wherein,the weight of the sampling point direction is determined by the following formula,
the initial value of m is set to 0, the initial value of n is set to 1, k 1 E (0, 1) reflects the change of the sampling point direction weight, k 2 E (0, 1) represents the weight of the direction of the target point, and k is when more complex barriers exist in the environment 2 The value should be smaller and larger.
Further, the step 4 specifically includes the following:
s41, updating node x of random tree in joint space new Substituting the node information into the main mechanical arm R to obtain node information of a Cartesian coordinate system through positive kinematic solution;
s42, detaching barriers encountered by the mechanical arm in a working space mainly comprises a connecting rod bracket and crushed coal, and collision detection is carried out by using a rectangular enveloping box method and a spherical enveloping box method respectively;
s43, judging whether each joint of the mechanical arm collides with an obstacle, if not, carrying out x new Adding to an actively growing random tree and defining x nearest Is x new Is a parent node of (a); if yes, rejecting the node and returning to reselect the node;
s44, updating node x in the active growth random tree new X 'in the corresponding passively grown random tree' new Substituting the motion vector into the positive kinematics solution of the slave mechanical arm L;
s45, performing collision detection through an envelope box method, judging whether each joint of the mechanical arm collides with an obstacle, and if not, judging x' new Adding to a random tree T L In, and define x' nearest Is x' new Is a parent node of (a); if yes, x is new From T R Culling, and x' new From T L And (3) removing the nodes, and returning to reselecting the nodes.
The technical scheme provided by the invention has the beneficial effects that:
1) Aiming at the improvement of the traditional RRT algorithm, a node weight function is introduced in the node updating process of the traditional RRT algorithm to guide the generation of new nodes in the exploration process, so that the obstacle avoidance capability in the path planning process and the exploration non-directionality can be improved, and invalid sampling points are effectively reduced;
2) According to the invention, the mechanical arm joint angle group in the joint space is used for representing the mechanical arm position information, and the mechanical arm joint angle group is applied to the RRT algorithm after the improvement is introduced, so that complicated operation of inverse kinematics solution is avoided, and the planning efficiency is improved;
3) The active and passive dual-tree expansion method is provided, the active exploration is carried out on the main mechanical arm by using a path planning algorithm, the passive verification is carried out on the auxiliary mechanical arm, the exploration process and the verification process are carried out simultaneously, and the cooperative movement of the multiple mechanical arms is realized.
Drawings
FIG. 1 is a frame diagram of a multi-arm path planning method for a belt conveyor removal robot in accordance with the present invention;
FIG. 2 is a schematic diagram of a new node generation process provided by the present invention;
FIG. 3 is a schematic diagram of an active-passive dual-tree extended sampling process provided by the invention;
FIG. 4 is a schematic diagram of a belt conveyor removal robot used in the present invention;
FIG. 5 is a schematic diagram of the end path result of a stringer process robot in an embodiment;
FIG. 6 is a graph showing the angle change of each joint of the stringer process robot R in the example;
fig. 7 is a graph showing the angle change of each joint of the stringer process robot L in the example;
FIG. 8 is a graph showing the result of the displacement change of the R end of the mechanical arm in the stringer process in the example;
FIG. 9 is a graph showing the result of the displacement change of the end of the arm L in the stringer process in the example;
FIG. 10 is a schematic diagram of the end path results of an H-frame process robot in an embodiment;
FIG. 11 is a graph showing the variation of the angle of each joint of the H-arm process robot R in the example;
FIG. 12 is a graph showing the angle change of each joint of the H-arm process robot L according to the embodiment;
FIG. 13 is a graph showing the results of the displacement change of the R-end of the H-stage process robot in the example;
FIG. 14 is a graph showing the results of the displacement change of the end of the arm L in the H-stage process in the example.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings.
A multi-arm path planning method for a belt conveyor disassembly robot, the algorithm framework of which is shown in figure 1, specifically comprises the following steps:
step 1, establishing a kinematic model for a belt conveyor disassembly robot shown in fig. 4;
step 2, initializing an initial pose and a target pose of the mechanical arm, and determining basic information of obstacles in the environment;
step 3, on the basis of the traditional RRT algorithm, a Sobol sequence is used for generating sampling points, a node weight function is introduced to expand new nodes, and the generation process of the new nodes is shown in figure 2;
step 4, introducing active and passive expansion, and realizing coordinated planning of multiple mechanical arms in a mode of sampling by the active mechanical arms and verifying by the slave mechanical arms, wherein the active and passive double-tree expansion sampling process is shown in a figure 3;
and 5, optimizing the path by adopting a polynomial fitting method based on a least square method, so that the movement of the mechanical arm is smoother.
The step 2 specifically comprises the following contents:
an active-passive expansion RRT algorithm is provided for path planning exploration of double mechanical arms, wherein the active-growth random tree T is used by the main mechanical arm R R From arm L use passive growth random tree T L Expanding the random tree in the joint space, and defining the initial joint angle of the main mechanical arm as x start The target position is x goal The initial joint angle of the slave mechanical arm is x' start The target position is x' goal And takes the same as a starting point and a target point respectivelyAdding a random tree T R And T L Is a kind of medium.
The step 3 specifically comprises the following contents:
multi-arm joint space path planning generally involves higher dimensions, and the complexity of computation increases due to the excessively high dimensions, which makes it difficult for a robotic arm to complete path planning within a prescribed time; although the path planning can be completed by performing RRT sampling in the respective joint space, coordination between mechanical arms cannot be realized; the specific operation steps of the step 3 comprise:
s31, compared with a pseudo-random sequence, the Sobol sequence can enable sampling points to be distributed more uniformly on the premise of guaranteeing randomness, and define sampling points x in joint space sample The method comprises the steps of forming each joint angle of a mechanical arm by a Sobol sequence, taking a node with the smallest Euclidean distance with a sampling point in a random tree as a closest point, and marking the point as x nearest ；
S32, introducing a node weight function, and adaptively adjusting the expansion weights in the random sampling point direction and the target point direction through the function; under the condition of no obstacle, giving higher weight to the target point so as to guide the random tree to extend towards the direction of the target point, and under the condition of the obstacle, giving higher weight to the random sampling point so as to guide the random tree to bypass the obstacle; the expansion formula of the new sampling point is as follows:
wherein,the weight of the sampling point direction is determined by the following formula,
the initial value of m is set to 0, the initial value of n is set to 1, k 1 E (0, 1) reflects the change of the sampling point direction weight, k 2 E (0, 1) represents the target point partyWeight of direction, k when more complex barriers are in environment 2 The value should be smaller and larger.
The step 4 specifically comprises the following contents:
s41, updating node x of random tree in joint space new Substituting the node information into the main mechanical arm R to obtain node information of a Cartesian coordinate system through positive kinematic solution;
s42, detaching barriers encountered by the mechanical arm in a working space mainly comprises a connecting rod bracket and crushed coal, and collision detection is carried out by using a rectangular enveloping box method and a spherical enveloping box method respectively;
s43, judging whether each joint of the mechanical arm collides with an obstacle, if not, carrying out x new Adding to an actively growing random tree and defining x nearest Is x new Is a parent node of (a); if yes, rejecting the node and returning to reselect the node;
s44, updating node x in the active growth random tree new X 'in the corresponding passively grown random tree' new Substituting the motion vector into the positive kinematics solution of the slave mechanical arm L;
s45, performing collision detection through an envelope box method, judging whether each joint of the mechanical arm collides with an obstacle, and if not, judging x' new Adding to a random tree T L In, and define x' nearest Is x' new Is a parent node of (a); if yes, x is new From T R Culling, and x' new From T L And (3) removing the nodes, and returning to reselecting the nodes.
The method aims at the disassembly task of the belt conveyor, and belongs to the field of robots. In order to better understand the present invention, a multi-arm path planning algorithm of a belt conveyor disassembly robot will be described in detail with reference to specific embodiments.
Taking a longitudinal beam disassembly task as an example, the longitudinal beam disassembly procedure is completed by two mechanical arms on the same side, simulation is carried out by taking the mechanical arm (1) and the mechanical arm (2) as examples in fig. 4, the mechanical arm (2) is defined as a master arm and is marked as R, and the mechanical arm (1) is defined as a slave arm and is marked as L. The working procedure of the longitudinal beam is simulated by adopting an active-passive expansion RRT algorithm, the tail end path of the mechanical arm is shown in fig. 5, the joint angles of the mechanical arm are shown in fig. 6 and 7, the displacement of the two mechanical arms in the x, y and z directions is shown in fig. 8 and 9, the angle changes of the joints of the two mechanical arms are the same, the tail end path of the mechanical arm is the same in the x and z directions, the distance of 1.4m is kept in the y direction, and the mechanical arm can finish the disassembling-placing work of the longitudinal beam, so that the feasibility of the invention is verified.
The H frame disassembling procedure is completed by two mechanical arms on opposite sides, the mechanical arm (2) and the mechanical arm (4) are taken as examples in this section to simulate, the mechanical arm (2) is defined as a main arm and is marked as R, the mechanical arm (4) is defined as a slave arm and is marked as L. The H frame procedure is simulated by adopting an active-passive expansion RRT algorithm, the tail end paths of the mechanical arms are shown in fig. 10, the joint angles of the mechanical arms are shown in fig. 11 and 12, the displacements of the two mechanical arms in the x, y and z directions are shown in fig. 13 and 14, the joint angles of the two mechanical arms are changed inversely, the tail end paths of the obtained mechanical arms are the same in the y and z directions, the tail end paths of the obtained mechanical arms are symmetrical about x=0 in the x direction, the mechanical arms can finish the pushing work of the H frame, and the feasibility of the H frame is verified.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (4)
1. The multi-arm path planning method for the belt conveyor disassembling robot is characterized by comprising the following steps of:
step 1, establishing a kinematic model aiming at a disassembly task of a belt conveyor;
step 2, initializing an initial pose and a target pose of the mechanical arm, and determining basic information of obstacles in the environment;
step 3, on the basis of a traditional RRT algorithm, a Sobol sequence is used for generating sampling points, and a node weight function is introduced to expand new nodes;
step 4, introducing active and passive expansion, and realizing coordinated planning of multiple mechanical arms in a mode of sampling by the master mechanical arm and verifying by the slave mechanical arm;
and 5, optimizing the path by adopting a polynomial fitting method based on a least square method, so that the movement of the mechanical arm is smoother.
2. The multi-arm path planning method for a belt conveyor disassembly robot according to claim 1, wherein step 2 specifically comprises:
an active-passive expansion RRT algorithm is provided for path planning exploration of double mechanical arms, wherein the active-growth random tree T is used by the main mechanical arm R R From arm L use passive growth random tree T L Expanding the random tree in the joint space, and defining the initial joint angle of the main mechanical arm as x start The target position is x goal From the initial joint angle of the mechanical arm x s ′ tart The target position is x g ′ oal And takes the same as a starting point and a target point to be respectively added into a random tree T R And T L Is a kind of medium.
3. A multi-arm path planning method for a belt conveyor disassembly robot according to claim 2, characterized in that step 3 specifically comprises:
s31, compared with a pseudo-random sequence, the Sobol sequence can enable sampling points to be distributed more uniformly on the premise of guaranteeing randomness, and define sampling points x in joint space sample The method comprises the steps of forming each joint angle of a mechanical arm by a Sobol sequence, taking a node with the smallest Euclidean distance with a sampling point in a random tree as a closest point, and marking the point as x nearest ；
S32, introducing a node weight function, and adaptively adjusting the expansion weights in the random sampling point direction and the target point direction through the function; under the condition of no obstacle, giving higher weight to the target point so as to guide the random tree to extend towards the direction of the target point, and under the condition of the obstacle, giving higher weight to the random sampling point so as to guide the random tree to bypass the obstacle; the expansion formula of the new sampling point is as follows:
wherein,the weight of the sampling point direction is determined by the following formula,
the initial value of m is set to 0, the initial value of n is set to 1, k 1 E (0, 1) reflects the change of the sampling point direction weight, k 2 E (0, 1) represents the weight of the direction of the target point, and k is when more complex barriers exist in the environment 2 The value should be smaller and larger.
4. A multi-arm path planning method for a belt conveyor removal robot according to claim 3, characterized in that step 4 method is as follows:
s41, updating node x of random tree in joint space new Substituting the node information into the main mechanical arm R to obtain node information of a Cartesian coordinate system through positive kinematic solution;
s42, detaching barriers encountered by the mechanical arm in a working space mainly comprises a connecting rod bracket and crushed coal, and collision detection is carried out by using a rectangular enveloping box method and a spherical enveloping box method respectively;
s43, judging whether each joint of the mechanical arm collides with an obstacle, if not, carrying out x new Adding to an actively growing random tree and defining x nearest Is x new Is a parent node of (a); if yes, rejecting the node and returning to reselect the node;
s44, updating node x in the active growth random tree new X in corresponding passively grown random tree n ′ ew Substituting the motion vector into the positive kinematics solution of the slave mechanical arm L;
s45, collision detection is carried out through an envelope box method, and whether each joint of the mechanical arm collides with an obstacle or not is judgedIf not, x is calculated n ′ ew Adding to a random tree T L In, and define x n ′ earest Is x n ′ ew Is a parent node of (a); if yes, x is new From T R Culling, and x n ′ ew From T L And (3) removing the nodes, and returning to reselecting the nodes.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8972057B1 (en) * | 2013-01-09 | 2015-03-03 | The Boeing Company | Systems and methods for generating a robotic path plan in a confined configuration space |
| CN110181515A (en) * | 2019-06-10 | 2019-08-30 | 浙江工业大学 | A kind of double mechanical arms collaborative assembly working path planing method |
| CN111230875A (en) * | 2020-02-06 | 2020-06-05 | 北京凡川智能机器人科技有限公司 | Double-arm robot humanoid operation planning method based on deep learning |
| CN112356033A (en) * | 2020-11-09 | 2021-02-12 | 中国矿业大学 | Mechanical arm path planning method integrating low-difference sequence and RRT algorithm |
| CN113276109A (en) * | 2021-04-21 | 2021-08-20 | 国网上海市电力公司 | RRT algorithm-based double-mechanical-arm decoupling motion planning method and system |
| CN113442170A (en) * | 2021-06-28 | 2021-09-28 | 国网上海市电力公司 | Method and system for reverse split calculation of redundant node of mechanical arm path |
| CN116533244A (en) * | 2023-05-25 | 2023-08-04 | 济南大学 | Double-arm cooperative motion planning method and system for closed-chain singular point avoidance |
-
2023
- 2023-08-24 CN CN202311074271.7A patent/CN117021101B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8972057B1 (en) * | 2013-01-09 | 2015-03-03 | The Boeing Company | Systems and methods for generating a robotic path plan in a confined configuration space |
| CN110181515A (en) * | 2019-06-10 | 2019-08-30 | 浙江工业大学 | A kind of double mechanical arms collaborative assembly working path planing method |
| CN111230875A (en) * | 2020-02-06 | 2020-06-05 | 北京凡川智能机器人科技有限公司 | Double-arm robot humanoid operation planning method based on deep learning |
| CN112356033A (en) * | 2020-11-09 | 2021-02-12 | 中国矿业大学 | Mechanical arm path planning method integrating low-difference sequence and RRT algorithm |
| CN113276109A (en) * | 2021-04-21 | 2021-08-20 | 国网上海市电力公司 | RRT algorithm-based double-mechanical-arm decoupling motion planning method and system |
| CN113442170A (en) * | 2021-06-28 | 2021-09-28 | 国网上海市电力公司 | Method and system for reverse split calculation of redundant node of mechanical arm path |
| CN116533244A (en) * | 2023-05-25 | 2023-08-04 | 济南大学 | Double-arm cooperative motion planning method and system for closed-chain singular point avoidance |
Non-Patent Citations (3)
| Title |
|---|
| KUN WEI: "An improved Rapidly-exploring Random Tree Approach for Robotic Dynamic Path Planning", 11TH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING, 3 December 2021 (2021-12-03), pages 181 - 187, XP034052527, DOI: 10.1109/ICICIP53388.2021.9642182 * |
| 代伟: "基于低差异序列与快速扩展随机树融合算法的机械臂路径规划", 控制理论与应用, vol. 39, no. 1, 31 January 2022 (2022-01-31), pages 130 - 144 * |
| 朱子祺: "基于G-RRT*算法的煤矸石分拣机器人路径规划", 工矿自动化, vol. 48, no. 3, 31 March 2022 (2022-03-31), pages 55 - 62 * |
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