CN117047776A - Path planning method for grabbing complex parts by six-degree-of-freedom mechanical arm - Google Patents
Path planning method for grabbing complex parts by six-degree-of-freedom mechanical arm Download PDFInfo
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Abstract
The embodiment of the application provides a path planning method for grabbing complex parts by a six-degree-of-freedom mechanical arm, which comprises the following steps: shooting scene information by a depth camera, and identifying and positioning the initial pose and the obstacle position of a part in the scene by using a target detection algorithm; acquiring information of a starting point and a target point position of an end effector of the six-degree-of-freedom mechanical arm; comparing the initial pose and the obstacle position of the part in the scene with the initial point and the target point pose information of the six-degree-of-freedom mechanical arm end effector, and planning the path of the six-degree-of-freedom mechanical arm end effector through a path planning algorithm to generate a moving path; and optimizing the moving path according to the NURBS interpolation method, and obtaining joint coordinates through inverse kinematics of the mechanical arm to obtain the six-degree-of-freedom mechanical arm moving track.
Description
Technical Field
The application relates to the field of mechanical arm track planning, in particular to a path planning method for grabbing complex parts by a six-degree-of-freedom mechanical arm.
Background
After the 3D point cloud information of the scene is obtained, the robot arm is required to automatically grab the target object, and the robot arm has the functions of identifying the target pose and avoiding the obstacle of the autonomous scene. These techniques require techniques in the fields of machine vision and robot arm kinematics. The process of realizing the autonomous obstacle avoidance of the mechanical arm is as follows: once the target pose is identified, the mechanical arm kinematics principle is utilized to determine the round trip track required by the mechanical arm to reach the target pose, so that the mechanical arm is ensured to avoid collision with any obstacle in a scene in the round trip process. This is a very time-consuming process, whereby the computer searches for collision-free grab routes at random.
A widely used and effective technique in the path planning field is the fast search random tree algorithm (RRT). In each iteration, the RRT randomly locates a state point in the motion space of the mechanical arm, where the state consists of rotational degrees of freedom of each joint of the mechanical arm. For a 6 degree of freedom rotation of the robotic arm, this state is a 6-dimensional vector. The RRT takes the vector from the current state of the mechanical arm to the new positioning state as the change direction of the state of the mechanical arm, and gives a small change amplitude as the step length of the mechanical arm movement in the wheel. If the mechanical arm moves in the new direction by the designated step length and does not collide with the obstacle in the scene, the new state can be used as a feasible path point for forming the grabbing path, otherwise, the new state point is searched again randomly. And (5) circulating until the mechanical arm reaches the target position state. The entire searched feasible path points form a path leading to the grabbing object, and the path cannot collide with obstacles in the scene. The RRT technique is to randomly search for a state point in the entire state space, thereby detecting a feasible path point. Although this technique frequently searches for non-viable path points, RRT algorithms propose to reselect parent nodes and reroute random trees to reduce path costs, so that relatively better paths can be obtained in a limited number of iterations. However, as the search tree expands, the number of nodes increases, and the amount of calculation of the algorithm increases, so that the convergence time of the algorithm becomes a significant problem.
Therefore, the path planning method of the improved mechanical arm is researched and developed to efficiently realize obstacle avoidance path planning of the six-degree-of-freedom mechanical arm in a complex obstacle environment, shorten planning time of a collision-free grabbing path and have very important practical significance.
In view of the above problems, an effective technical solution is currently needed.
Disclosure of Invention
The embodiment of the application aims to provide a path planning for grabbing complex parts by a six-degree-of-freedom mechanical arm, and the joint coordinates can be solved by inverse kinematics solution of the mechanical arm to obtain all information of the whole running track of the six-degree-of-freedom mechanical arm, so that the path distance is reduced, the path quality is optimized, and the time required by grabbing by the mechanical arm is greatly reduced.
The embodiment of the application also provides a path planning method for grabbing complex parts by the six-degree-of-freedom mechanical arm, which comprises the following steps:
shooting scene information by a depth camera, and identifying and positioning the initial pose and the obstacle position of a part in the scene by using a target detection algorithm;
acquiring information of a starting point and a target point position of an end effector of the six-degree-of-freedom mechanical arm;
comparing the initial pose and the obstacle position of the part in the scene with the initial point and the target point pose information of the six-degree-of-freedom mechanical arm end effector, and planning the path of the six-degree-of-freedom mechanical arm end effector through a path planning algorithm to generate a moving path;
and optimizing the moving path according to the NURBS interpolation method, and obtaining joint coordinates through inverse kinematics of the mechanical arm to obtain the six-degree-of-freedom mechanical arm moving track.
Optionally, in the path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to the embodiment of the present application, scene information is shot by using a depth camera, and a target detection algorithm is used to identify and locate a starting pose and an obstacle position of the part in the scene, specifically:
shooting a scene picture by using a depth camera, calibrating, and controlling the depth camera to shoot a part picture by a computer after camera parameters are acquired;
positioning the pose of the part in the scene by using an FF6D target detection algorithm;
and converting the position relation of the part under the depth camera coordinate system into the mechanical arm base coordinate system by a hand-eye calibration method, and generating the initial pose and the obstacle position of the part in the scene.
Optionally, in the path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to the embodiment of the application, whether the parts in the scene are suitable for grabbing or not is obtained, and the parts unsuitable for grabbing and the edges of the material frame are regarded as obstacles;
the method comprises the steps of adopting an enveloping method, surrounding an obstacle in the environment by a sphere, surrounding a connecting rod of the mechanical arm by a cylinder, superposing the radius of the cylinder on the radius of the sphere, taking the axis of each shaft of the mechanical arm as a line segment, and acquiring the position relation between the line segment and the sphere.
Optionally, in the path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to the embodiment of the present application, path planning of the end effector of the six-degree-of-freedom mechanical arm is performed by using a path planning algorithm, which specifically includes:
planning the end path of the mechanical arm based on an improved RRT algorithm, wherein,
the steps based on the improved RRT algorithm are:
s31, initializing a random tree, taking the pose of a grabbing target as a starting point of random tree searching, setting a target point, and defining a step length, a target sampling rate and a radius;
s32, starting random sampling in the three-dimensional scene, wherein random sampling points are close to target points due to the influence of the target sampling rate;
s33, judging whether the random sampling point is in the obstacle range, if so, jumping to the step S32 to re-sample the random point, if not, calculating Euclidean distances from all nodes in the random tree to the target point, and finding the nearest random point X rand Along a random point X rand Direction generating path with set step length and generating new node X new ；
S34, when generating node X new Thereafter, parent node is reselected to newly generated node X new Searching adjacent nodes in a defined radius range for the circle center as a replacement node X new Alternatively to the parent node,
s35, after the father node is reselected, rewiring is carried out on the random tree;
s36, in the path planning, the obstacle is a regular geometric body, a boolean value bool is set for the geometric body boundary, and a collision is indicated when bool=1, and no collision is indicated when bool=0.
Optionally, in the path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to the embodiment of the present application, step S34 includes sequentially calculating path costs from adjacent nodes to start up plus X new The path cost to each adjacent node is compared with the path cost value;
if the path cost value of the current parent node is smaller than a preset cost threshold value, reserving the current parent node;
if the path cost value from the new parent node to the current node is smaller than the path cost value of the current parent node, the current parent node is replaced by the new parent node and the path is regenerated.
Optionally, in the path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to the embodiment of the present application, the RRT algorithm freely adjusts parameters of the curve according to actual requirements by adjusting the shape of the NURBS weight factor control curve.
Optionally, in the path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to the embodiment of the application, the tail end of the mechanical arm is connected with a standard clamping jaw, a claw finger is arranged on the standard clamping jaw, the stroke of the clamping jaw is adjusted by the size of the claw finger, and an elastic anti-slip pad is arranged at the end part of the claw finger.
Optionally, in the path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to the embodiment of the present application, the moving path is optimized according to the NURBS interpolation method, including the following steps:
calculating a node vector; calculating boundary conditions through the node vectors; and back-calculating the control vertex according to the boundary condition.
As can be seen from the above, in the path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm provided by the embodiment of the application, in the situation of stacking multiple targets, firstly, the starting position, the target position and the obstacle information are acquired by using an industrial camera, then, the path planning is performed by adopting an improved RRT algorithm, the algorithm compares the path cost functions and can reselect the optimal father node, thereby realizing the progressive optimal effect of the overall planned path, and realizing obstacle avoidance by combining collision information feedback; and finally, smoothing the generated path by using a non-uniform rational B-spline curve, and reducing the shake of the mechanical arm. The joint coordinates are solved through inverse kinematics solution of the mechanical arm, and all information of the whole running track of the mechanical arm with six degrees of freedom is obtained.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, the claims, and the drawings, as well as the objects and advantages of the application may be realized and obtained by means of the instrumentalities particularly pointed out in the written description, claims, and drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a path planning method for grabbing complex parts by a six-degree-of-freedom mechanical arm provided by an embodiment of the application;
fig. 2 is an improved RRT algorithm flowchart of a path planning method for grabbing complex parts by a six-degree-of-freedom mechanical arm according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1-2, the application discloses a path planning method for grabbing complex parts by a six-degree-of-freedom mechanical arm, which comprises the following steps:
s1, shooting scene information through a depth camera, and identifying and positioning the initial pose and the obstacle position of a part in the scene by using a target detection algorithm;
s2, acquiring initial point and target point pose information of an end effector of the six-degree-of-freedom mechanical arm;
s3, comparing the initial pose and the obstacle position of the part in the scene with the initial point and the target point position information of the six-degree-of-freedom mechanical arm end effector, and planning the path of the six-degree-of-freedom mechanical arm end effector through a path planning algorithm to generate a moving path;
and S4, optimizing the moving path according to the NURBS interpolation method, and obtaining joint coordinates through inverse kinematics of the mechanical arm to obtain the six-degree-of-freedom mechanical arm moving track.
Specifically, the camera adopts a depth camera, and is used for shooting a scene picture and acquiring position information and depth information of objects in the scene.
The model and the degree of freedom of the mechanical arm can be arbitrarily selected, and the system also comprises mechanical arm accessories such as a control cabinet and a demonstrator.
The mechanical arm is provided with an external communication interface, so that the mechanical arm can conveniently communicate with external equipment. The software part of the computer is used for the image processing and the simulation of the path planning algorithm.
The end of the mechanical arm is provided with a standard clamping jaw, but the clamping jaw finger adopts a non-standard design. By adjusting the size of the non-standard claw fingers, the stroke of the clamping jaw can be controlled, and an elastic anti-slip pad is added to the head of the claw fingers. The elastic anti-slip pad increases friction force during grabbing on one hand, and reduces errors during grabbing parts by the clamping jaw through elasticity on the other hand.
Image processing is an important step of the method for acquiring the positions of all obstacles in the working scene of the mechanical arm. Specifically, all obstacles are identified and located using the FF6D target detection algorithm, and the positions thereof are converted by a hand-eye calibration method. Under the condition that the obstacle information is known, a path planning algorithm can be quickly programmed and realized, so that the mechanical arm moves to a designated position without contacting the obstacle.
The mechanical arm is not contacted with the obstacle, namely the distance between the mechanical arm and the obstacle is larger than the set distance, in the scene of stacking the parts, the parts suitable for grabbing are required to be selected, and the edges of other parts and the material frame are regarded as the obstacle. To simplify the operation, the obstacle may be surrounded by a sphere, the arm link by a cylinder, and the cylinder radius added to the sphere radius, i.e. r=r 1 +r 2 . The collision detection problem can be converted into the position relationship between the line segment and the sphere, so that the calculated amount is obviously reduced, and the vertical distance from the sphere center O of the sphere to the straight line AB can be set as d v Shortest distance to point A and point B respectively is d min ＝min(d OA ,d OB ) The following discussion is classified according to the positional relationship between the envelopes:
(1) The drop foot p of the sphere center on the line segment is positioned on the connecting rod AB, and d v >r, the connecting rod does not collide with the obstacle;
(2) The drop foot p of the sphere center on the line segment is positioned on the connecting rod AB, and d v <r, the connecting rod and the enveloping sphere have an intersection point, namely collide with an obstacle;
(3) The foot drop p of the sphere center on the line segment is positioned on the extension line of the connecting rod AB, and d v >r, at the moment, the connecting rod and the enveloping sphere have no intersection point, i.e. can not collide with the obstacle；
(4) The foot drop p of the sphere center on the line segment is positioned on the extension line of the connecting rod AB, and d v <And r, the connecting rod and the enveloping sphere have an intersection point at the moment, namely collide with the obstacle.
Then generating a collision-free progressive optimal path based on an improved RRT algorithm, wherein the method mainly comprises the following steps of: (1) Initializing a random tree, taking the pose of a grabbing target as a starting point, and setting a target point, a step length, a target sampling rate and a radius; (2) Starting random sampling in a three-dimensional scene, wherein a sampling point approaches to a target point; (3) Judging whether the random sampling points are in the range of the obstacle, if so, jumping to the step (2) to re-sample the random points, otherwise, calculating Euclidean distances from all nodes in the random tree to the target point, finding a random point Xrand closest to the random point, generating a path along the direction of the random point Xrand in a set step length, and generating a new node Xnew; (4) After generating a node Xnew, reselecting a father node, searching for adjacent nodes in a defined radius range by taking the Xnew as a circle center, sequentially calculating path cost from the adjacent nodes to the starting node and path cost from the Xnew to each adjacent node as alternatives for replacing the father node Xnew, comparing the cost values, if the path cost of the current father node is minimum, retaining the current father node, otherwise, replacing the original father node with a new father node and regenerating a path again; (5) After the father node is reselected, the random tree is rerouted in order to make the cost of connection among the nodes of the random tree as small as possible; (6) In this path planning, the obstacle is approximated to a more regular geometric body by an envelope method, a boolean value bol is set for the geometric body boundary, a collision is indicated when bol=1, and no collision is indicated when bol=0. Defining a judgment function judge (M, N, P) wherein:
M＝(x 1 ，y 1 ，z 1 )，N＝(x 2 ，y 2 ，z 2 )，P＝(x，y，z)
judge(M，N，P)＝((z-z 1 )(y 2 -y 1 )>(z 2 -z 1 )(y-y 1 )
(y-y 1 )(x 2 -x 1 )>(y 2 -y 1 )(x-x 1 )
(z-z 1 )(x 2 -x 1 )>(z 2 -z 1 )(x-x 1 ))
the jodge function is a boolean function, when the right of the equation is true, jodge=1, whereas jodge=0.
For collision-free progressive optimal paths generated by the improved RRT algorithm, NURBS interpolation optimization can be employed to adjust curve shape and parameterization. Interpolation optimization typically includes three steps of computing node vectors, computing boundary conditions, and back-computing control vertices.
NURBS curves are mathematically defined as C (u):
wherein: u (u) i Is a node; omega i Is a whole factor; D i represents a control point;
N i，k the double subscript of (u) represents the ith kth B-spline basis function;
p (u) is a B spline curve; c (u) is NURBS spline curve;
the following formula calculates NURBS node vectors for the chord length parameter method:
wherein: u (u) i Is a node; p (P) i Representing a value point;
the linear system of equations represented by the following equation consists of n+1 vector equations with n+3 unknown control vertices, requiring the addition of appropriate boundary conditions, and is solved.
D in j Representing the j-th control point.
Typically 3 boundary conditions can be added, with tangential conditions: the tangential direction of the head and tail ends is fixed.
Delta in i ＝u i+1 -u i ,p′ 0 ,P′ 1 Is the tangential vector of the head and tail points.
Free end point condition: the free end point condition requires that the head-to-end point have zero curvature.
And (3) a closed curve condition, namely requiring that the initial end point and the final end point of the curve are coincident and the second order is continuous.
For a continuous NURBS three-closed curve, since the first and the last data points are mutually overlapped, n-1 equations are enough to calculate n+1 control vertexes, so that the matrix expression of the inverse calculation of the control vertexes of the NURBS three-closed curve is as follows
For NURBS open curves, additional equations are added that meet two boundary conditions. The first and last control vertexes of the NURBS open curve are the model value points of the first and the last parts, namely: d, d 0 ＝P 0 ,d n+2 ＝P n . Taking a tangential condition as an example, the boundary condition gives an expression of NURBS open curve control vertex back calculation in the form of an application matrix:
wherein: d, d i For control points, (i=0, 1,) n,
e i ＝(Δ i+l +Δ i+2 )p i-1
by solving the above-described system of linear equations, all unknown control vertices can be obtained. Appropriate weighting factors are selected as needed to determine NURBS curve expression for the robot arm tip trajectory.
According to the path planning method for grabbing complex parts by the six-degree-of-freedom mechanical arm, in the scene of stacking a plurality of targets, firstly, the starting position, the target position and the obstacle information are acquired by using an industrial camera, then, the path planning is carried out by adopting an improved RRT algorithm, the algorithm is compared through a path cost function, and an optimal father node can be reselected, so that the progressive optimal effect of the overall planned path is realized, and obstacle avoidance is realized by combining collision information feedback; and finally, smoothing the generated path by using a non-uniform rational B-spline curve, and reducing the shake of the mechanical arm. The joint coordinates are solved through inverse kinematics solution of the mechanical arm, and all information of the whole running track of the mechanical arm with six degrees of freedom is obtained.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of units is only one logical function division, and there may be other divisions in actual implementation, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present application may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present application may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (8)
1. A path planning method for grabbing complex parts by a six-degree-of-freedom mechanical arm is characterized by comprising the following steps:
shooting scene information by a depth camera, and identifying and positioning the initial pose and the obstacle position of a part in the scene by using a target detection algorithm;
acquiring information of a starting point and a target point position of an end effector of the six-degree-of-freedom mechanical arm;
comparing the initial pose and the obstacle position of the part in the scene with the initial point and the target point pose information of the six-degree-of-freedom mechanical arm end effector, and planning the path of the six-degree-of-freedom mechanical arm end effector through a path planning algorithm to generate a moving path;
and optimizing the moving path according to the NURBS interpolation method, and obtaining joint coordinates through inverse kinematics of the mechanical arm to obtain the six-degree-of-freedom mechanical arm moving track.
2. The path planning method for capturing complex parts by the six-degree-of-freedom mechanical arm according to claim 1, wherein scene information is captured by a depth camera, and the initial pose and the obstacle position of the parts in the scene are identified and positioned by a target detection algorithm, specifically:
shooting a scene picture by using a depth camera, calibrating, and controlling the depth camera to shoot a part picture by a computer after camera parameters are acquired;
positioning the pose of the part in the scene by using an FF6D target detection algorithm;
and converting the position relation of the part under the depth camera coordinate system into the mechanical arm base coordinate system by a hand-eye calibration method, and generating the initial pose and the obstacle position of the part in the scene.
3. The path planning method for grabbing complex parts by a six-degree-of-freedom mechanical arm according to claim 2, wherein whether the parts in the scene are suitable for grabbing or not is obtained, and the parts which are not suitable for grabbing and the edges of a material frame are regarded as obstacles;
the method comprises the steps of adopting an enveloping method, surrounding an obstacle in the environment by a sphere, surrounding a connecting rod of the mechanical arm by a cylinder, superposing the radius of the cylinder on the radius of the sphere, taking the axis of each shaft of the mechanical arm as a line segment, and acquiring the position relation between the line segment and the sphere.
4. The path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to claim 3, wherein the path planning of the end effector of the six-degree-of-freedom mechanical arm is performed by using a path planning algorithm, specifically:
planning the end path of the mechanical arm based on an improved RRT algorithm, wherein,
the steps based on the improved RRT algorithm are:
s31, initializing a random tree, taking the pose of a grabbing target as a starting point of random tree searching, setting a target point, and defining a step length, a target sampling rate and a radius;
s32, starting random sampling in the three-dimensional scene, wherein random sampling points are close to target points due to the influence of the target sampling rate;
s33, judging whether the random sampling point is in the obstacle range, if so, jumping to the step S32 to re-sample the random point, if not, calculating Euclidean distances from all nodes in the random tree to the target point, and finding the nearest random point X rand Along a random point X rand Direction to generate path with set step length and generate new sectionPoint X new ；
S34, when generating node X new Thereafter, parent node is reselected to newly generated node X new Searching adjacent nodes in a defined radius range for the circle center as a replacement node X new Alternatively to the parent node,
s35, after the father node is reselected, rewiring is carried out on the random tree;
s36, in the path planning, the obstacle is a regular geometric body, a boolean value bool is set for the geometric body boundary, and a collision is indicated when bool=1, and no collision is indicated when bool=0.
5. The method of path planning for complex part gripping by a six degree of freedom robot arm according to claim 4, wherein step S34 comprises sequentially calculating path costs from adjacent nodes to start plus X new The path cost to each adjacent node is compared with the path cost value;
if the path cost value of the current parent node is smaller than a preset cost threshold value, reserving the current parent node;
if the path cost value from the new parent node to the current node is smaller than the path cost value of the current parent node, the current parent node is replaced by the new parent node and the path is regenerated.
6. The path planning method for grabbing complex parts by using a six-degree-of-freedom mechanical arm according to claim 5, wherein the RRT algorithm freely adjusts parameters of the curve according to actual requirements by adjusting the shape of the NURBS weight factor control curve.
7. The path planning method for grabbing complex parts by using the six-degree-of-freedom mechanical arm according to claim 6, wherein the tail end of the mechanical arm is connected with a standard clamping jaw, a claw finger is arranged on the standard clamping jaw, the stroke of the clamping jaw is adjusted by the size of the claw finger, and an elastic anti-slip pad is arranged at the end part of the claw finger.
8. The path planning method for complex part grabbing by a six degree of freedom mechanical arm according to claim 7, wherein optimizing the moving path according to NURBS interpolation method comprises the steps of:
calculating a node vector;
calculating boundary conditions through the node vectors;
and back-calculating the control vertex according to the boundary condition.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110228069A (en) * | 2019-07-17 | 2019-09-13 | 东北大学 | A kind of online avoidance motion planning method of mechanical arm |
| CN112677153A (en) * | 2020-12-16 | 2021-04-20 | 东北林业大学 | Improved RRT algorithm and industrial robot path obstacle avoidance planning method |
| CN114131612A (en) * | 2021-12-20 | 2022-03-04 | 中国科学院长春光学精密机械与物理研究所 | Redundant manipulator real-time look-ahead trajectory planning method based on NURBS curve interpolation algorithm |
| CN115390551A (en) * | 2022-05-26 | 2022-11-25 | 武汉工程大学 | Robot path planning method and device, electronic equipment and storage medium |
| CN115570566A (en) * | 2022-09-28 | 2023-01-06 | 长春工业大学 | Robot obstacle avoidance path planning method for improving APF-RRT algorithm |
| CN116690557A (en) * | 2023-05-09 | 2023-09-05 | 中国科学院自动化研究所 | Method and device for controlling humanoid three-dimensional scanning motion based on point cloud |
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2023
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110228069A (en) * | 2019-07-17 | 2019-09-13 | 东北大学 | A kind of online avoidance motion planning method of mechanical arm |
| CN112677153A (en) * | 2020-12-16 | 2021-04-20 | 东北林业大学 | Improved RRT algorithm and industrial robot path obstacle avoidance planning method |
| CN114131612A (en) * | 2021-12-20 | 2022-03-04 | 中国科学院长春光学精密机械与物理研究所 | Redundant manipulator real-time look-ahead trajectory planning method based on NURBS curve interpolation algorithm |
| CN115390551A (en) * | 2022-05-26 | 2022-11-25 | 武汉工程大学 | Robot path planning method and device, electronic equipment and storage medium |
| CN115570566A (en) * | 2022-09-28 | 2023-01-06 | 长春工业大学 | Robot obstacle avoidance path planning method for improving APF-RRT algorithm |
| CN116690557A (en) * | 2023-05-09 | 2023-09-05 | 中国科学院自动化研究所 | Method and device for controlling humanoid three-dimensional scanning motion based on point cloud |
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