CN112549016A - Mechanical arm motion planning method - Google Patents
Mechanical arm motion planning method Download PDFInfo
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- 238000009499 grossing Methods 0.000 claims abstract description 9
- 238000004088 simulation Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 4
- 238000005070 sampling Methods 0.000 claims description 7
- 230000004888 barrier function Effects 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 4
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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
- B25J9/1666—Avoiding collision or forbidden zones
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J18/00—Arms
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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
- B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
- B25J9/1676—Avoiding collision or forbidden zones
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Abstract
The invention discloses a mechanical arm movement planning method, which is implemented according to the following steps: step 1, performing obstacle modeling by using a common space rule object to approximate an envelope obstacle; step 2, giving an initial position and a target position of the mechanical arm, and obtaining an inverse solution of the target position by an inverse kinematics method; step 3, calculating a track through a p-rrt algorithm; step 4, importing a seven-degree-of-freedom mechanical arm model, giving a target position of the mechanical arm, and obtaining an inverse solution of the target position through inverse kinematics; taking the path node of the obtained track as input and carrying out smooth optimization processing on the path by using a B spline curve; and 5, carrying out a simulation experiment in Matlab, adding simulation comparison after B spline curve optimization, comparing path curves before and after smoothing, verifying effectiveness, finishing arm motion planning, and solving the problem of poor smoothness of a planned path in the prior art.
Description
Technical Field
The invention belongs to the technical field of mechanical arm motion planning, and relates to a mechanical arm motion planning method.
Background
The motion planning problem of the robot is an important problem in the robot research field, and mainly comprises path planning and track planning (time), wherein a sequence point or a curve connecting a starting point position and a target point position is called a path, and a strategy of the path, namely the path planning, is formed.
Path planning is to find a series of path points to be traversed, path points being positions in space or joint angles, and trajectory planning is to give the path time information. Motion planning is the insertion of a sequence of intermediate points for control between given path end points to achieve smooth motion along a given path. And (3) adding time sequence information into the path planning in the track planning, and planning the speed and the acceleration of the robot when the robot executes a task so as to meet the requirements of smoothness, speed controllability and the like.
By adopting a rapid expanding Random Tree (rapid expanding Random Tree) path planning algorithm and performing collision detection on sampling points in a state space, the modeling of the space is avoided, and the problem of motion planning of the mechanical arm in a high-dimensional space and a complex environment can be effectively solved. However, the traditional RRT algorithm has poor guidance, and when the planning space is increased, the planned path has poor smoothness and the algorithm has high time complexity.
Disclosure of Invention
The invention aims to provide a mechanical arm motion planning method, which solves the problem of poor smoothness of a planned path in the prior art.
The technical scheme adopted by the invention is that a mechanical arm motion planning method is implemented according to the following steps:
step 1, performing obstacle modeling by using a common space rule object to approximate an envelope obstacle;
step 2, by using the model established in the step 1, giving an initial position and a target position of the mechanical arm, and obtaining an inverse solution of the target position by an inverse kinematics method;
step 3, calculating a track by using the inverse solution value obtained in the step 2 through a p-rrt algorithm;
step 4, importing a seven-degree-of-freedom mechanical arm model, giving a target position of the mechanical arm, and obtaining an inverse solution of the target position through inverse kinematics; taking the path node of the track obtained in the step 3 as input and carrying out smooth optimization processing on the path by using a B spline curve;
and 5, obtaining a path curve of the mechanical arm after smoothing according to the step 4, performing a simulation experiment in Matlab on the premise of ensuring that the mechanical arm does not collide with the obstacle, adding simulation comparison after B spline curve optimization into P, comparing the path curve before and after smoothing, verifying effectiveness, and finishing mechanical arm motion planning.
The invention is also characterized in that:
step 1 is specifically carried out as follows: acquiring the size and position information of the barrier, and setting a collision detection scheme; in order to avoid collision between each connecting rod of the mechanical arm and the obstacle, after the obstacle is enveloped into a sphere, the spatial position relation between the spherical obstacle and the connecting rod of the mechanical arm needs to be calculated, if the distance between the connecting rod of the mechanical arm and the circle center of the spherical obstacle is smaller than the sum of the radii, namely the intersection part of the connecting rod of the mechanical arm and the obstacle exists, collision can occur, and the other situations show that collision cannot occur.
Step 3 is specifically implemented according to the following steps:
step 3.1, random sampling is carried out in a map environment and a sampling point x is determinedrandFind the distance xrandNearest node xnear；
Step 3.2, determining a random point boundary and selecting whether the growth direction of the tree is towards a target point direction or a random direction according to the value of the probability function P; the probability set here is that there is a 0.5 probability straight going;
step 3.3, along xnearTo xrandThe distance of the step is advanced to obtain xnew；
Step 3.4, collisionFree (M, E) is adoptedi) Method for detecting Edge (x)new，xnear) Whether collision exists between the map and a barrier in the map environment or not can be judged, and if no collision exists, one-time successful space expansion search can be completed;
step 3.5, calculate the new point xnewIf the distance between the target point and the end point is smaller than the target point threshold value, finishing the tracing and generating a track;
in step 3, the map environment is initialized and a starting point x is determinedinitAnd target point xgoalAnd setting a target point threshold, and when the target point threshold is reached, the target point threshold is considered to be reached, wherein the threshold is set as follows: thr ═ 0.03.
Step 4 is specifically implemented according to the following steps:
step 4.1, the track obtained in step 3 is optimized by adopting three times of non-uniform B-spline interpolation:
k non-uniform B-spline curve definitions:
wherein d isiN is a control point, and the control points are sequentially connected to form a B spline control polygon, and N is a control pointi，k(U) is a k-th order canonical B-spline basis function defined on the non-uniform node vector U; and is provided with
N when K is 0i，k(u) if the degree is zero (i.e., K is 0), then the basis functions are all step functions, if in the ith node interval [ u ═ 0 ]i,ui+1]N of the above, then basis functioni,0(u)＝1；
Wherein li＝|di-di-1|(i＝1,2,...,n)；
Wherein k is an even number of times:
where k is an odd number of times:
path point q obtained based on P-RRTiObtaining a control point d by inverse solution of the non-uniform B-splineiAnd then generating a smooth track through the control points, completing the track optimization obtained in the step 3, and completing the motion planning of the mechanical arm.
The invention has the beneficial effects that: the invention relates to a mechanical arm motion planning method, which plans an obstacle avoidance path from an initial position to a target position by using a probability P-based RRT algorithm (P-RRT) in an obstacle environment and uses a B-spline curve for smoothing. Based on the traditional RRT algorithm, in each growth process of random numbers, whether the growth direction is a target point or a random point is selected according to the probability (a random value p of 0.0 to 1.0). The main improvement of the algorithm is that a Chose Target function is added in the extended function. This function is not all used as the extension direction, but a probability P is chosen for extension, with a probability choice of 1-P for extension; the convergence speed is accelerated; in addition, the invention adopts a local RRT algorithm, thereby avoiding the defects that the global RRT wastes time and is easy to generate singular solution or interference solution. And the B-spline is used for smoothing the path on the planned path, so that a smooth obstacle avoidance path is generated, and the robot is ensured to execute the planned path in a good state.
Drawings
Fig. 1 is a flowchart of the method for planning the motion of the robot arm according to the present invention, which combines the improved RRT algorithm with the B-spline.
Fig. 2 is a schematic diagram of a spherical envelope surface of an obstacle for describing the obstacle in the method for planning the motion of the mechanical arm according to the present invention.
Fig. 3 is a schematic diagram of a position relationship between an obstacle and a link in the method for planning the movement of the robot arm according to the present invention.
FIG. 4 is a schematic diagram of a search process of a P-RRT algorithm in a three-dimensional space according to the mechanical arm motion planning method.
FIG. 5 is a comparison diagram of paths before and after a B-spline function is added to a path planned by a manipulator model of the manipulator motion planning method according to the invention along a P-RRT for smoothing.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a mechanical arm motion planning method, which is implemented according to the following steps as shown in figure 1:
step 1, performing obstacle modeling by using a common space rule object to approximate an envelope obstacle; as shown in figure 2 of the drawings, in which,
step 1 is specifically carried out as follows: acquiring the size and position information of the barrier, and setting a collision detection scheme; in order to avoid collision between each connecting rod of the mechanical arm and the obstacle, after the obstacle is enveloped into a sphere, the spatial position relationship between the spherical obstacle and the connecting rod of the mechanical arm needs to be calculated, as shown in fig. 3, if the distance between the connecting rod of the mechanical arm and the circle center of the spherical obstacle is smaller than the sum of the radii, namely the intersection part of the connecting rod of the mechanical arm and the obstacle, collision occurs, and the other situations indicate that collision does not occur.
Step 2, by using the model established in the step 1, giving an initial position and a target position of the mechanical arm, and obtaining an inverse solution of the target position by an inverse kinematics method;
and (3) solving the inverse function ikfine in the robot tool box (equivalent to a function pack), and solving the inverse solution of the mechanical arm according to the path points output in the third step by using the ikfine function.
Step 3, calculating a track by using the inverse solution value obtained in the step 2 through a p-rrt algorithm;
step 3 is specifically implemented according to the following steps:
initializing a map environment and determining a starting point xinitAnd target point xgoalAnd setting a target point threshold, and when the target point threshold is reached, the target point threshold is considered to be reached, wherein the threshold is set as follows:
Thr＝0.03。
step 3.1, random sampling is carried out in a map environment and a sampling point x is determinedrandFind the distance xrandNearest node xnear；
Step 3.2, determining a random point boundary and selecting whether the growth direction of the tree is towards a target point direction or a random direction according to the value of the probability function P; the probability set here is that there is a 0.5 probability straight going;
step 3.3, along xnearTo xrandThe distance of the step is advanced to obtain xnew；
Step 3.4, collisionFree (M, E) is adoptedi) Method for detecting Edge (x)new，xnear) Whether collision exists between the map and a barrier in the map environment or not can be judged, and if no collision exists, one-time successful space expansion search can be completed;
step 3.5, calculate the new point xnewAnd if the distance from the end point is less than the target point threshold value, finishing the tracking and generating the track.
Probability P-based RRT path planning involved in step three
The pseudo code of the algorithm is as follows:
in the above code, M is a map environment, xinitIs the starting position, xgoalIs the target position and P is the probability function. The path space searching process starts from a starting point, firstly randomly scattering points xrand(ii) a Then find the distance xrandNearest node xnearAnd selecting the growing direction of the tree according to the value of the probability P; then along xnearTo xrandThe distance of the step is advanced to obtain xnew；collisionFree(M,Ei) Method for detecting Edge (x)new,xnear) Whether collision exists between the map and obstacles in the map environment or not can be judged, and if no collision exists, a successful space expansion search can be completed.
Step 4, importing a seven-degree-of-freedom mechanical arm model, giving a target position of the mechanical arm, and obtaining an inverse solution of the target position through inverse kinematics; taking the path node of the track obtained in the step 3 as input and carrying out smooth optimization processing on the path by using a B spline curve;
step 4 is specifically implemented according to the following steps:
step 4.1, the track obtained in step 3 is optimized by adopting three times of non-uniform B-spline interpolation:
k non-uniform B-spline curve definitions:
wherein d isiN is a control point, and the control points are sequentially connected to form a B spline control polygon, and N is a control pointi,k(U) is a k-th order canonical B-spline basis function defined on the non-uniform node vector U; and is provided with
N when K is 0i,k(u) if the degree is zero (i.e., K is 0), then the basis functions are all step functions, if in the ith node interval [ u ═ 0 ]i,ui+1]N of the above, then basis functioni,0(u)＝1；
Wherein li＝|di-di-1|(i＝1,2,...,n)；
Wherein k is an even number of times:
where k is an odd number of times:
path point q obtained based on P-RRTiFor non-uniform B-splinesInverse solution to obtain a control point diAnd then generating a smooth track through the control points, completing the track optimization obtained in the step 3, and completing the motion planning of the mechanical arm.
And 5, obtaining a path curve after the mechanical arm is smoothed according to the step 4, adding smooth path points as many as possible on the premise of ensuring that the mechanical arm does not collide with the obstacle during movement, so that the movement is more stable and effective, carrying out a simulation experiment in Matlab, searching a schematic diagram of a P-RRT in a three-dimensional space as shown in FIG. 4, adding a simulation pair optimized by a B-spline curve as shown in FIG. 5, comparing the path curves before and after smoothing, verifying the effectiveness, and finishing the movement planning of the mechanical arm.
Claims (5)
1. A mechanical arm motion planning method is characterized by being implemented according to the following steps:
step 1, performing obstacle modeling by using a common space rule object to approximate an envelope obstacle;
step 2, by using the model established in the step 1, giving an initial position and a target position of the mechanical arm, and obtaining an inverse solution of the target position by an inverse kinematics method;
step 3, calculating a track by using the inverse solution value obtained in the step 2 through a p-rrt algorithm;
step 4, importing a seven-degree-of-freedom mechanical arm model, giving a target position of the mechanical arm, and obtaining an inverse solution of the target position through inverse kinematics; taking the path node of the track obtained in the step 3 as input and carrying out smooth optimization processing on the path by using a B spline curve;
and 5, obtaining a path curve of the mechanical arm after smoothing according to the step 4, performing a simulation experiment in Matlab on the premise of ensuring that the mechanical arm does not collide with the obstacle during movement, adding simulation comparison after B spline curve optimization, comparing the path curve before and after smoothing, verifying effectiveness, and finishing mechanical arm movement planning.
2. The method for planning the motion of the robot arm according to claim 1, wherein the step 1 is specifically performed as follows: acquiring the size and position information of the barrier, and setting a collision detection scheme; in order to avoid collision between each connecting rod of the mechanical arm and the obstacle, after the obstacle is enveloped into a sphere, the spatial position relation between the spherical obstacle and the connecting rod of the mechanical arm needs to be calculated, if the distance between the connecting rod of the mechanical arm and the circle center of the spherical obstacle is smaller than the sum of the radii, namely the intersection part of the connecting rod of the mechanical arm and the obstacle exists, collision can occur, and the other situations show that collision cannot occur.
3. The method for planning the motion of the robot arm according to claim 1, wherein the step 3 is specifically performed according to the following steps:
step 3.1, random sampling is carried out in a map environment and a sampling point x is determinedrandFind the distance xrandNearest node xnear；
Step 3.2, determining a random point boundary and selecting whether the growth direction of the tree is towards a target point direction or a random direction according to the value of the probability function P; the probability set here is that there is a 0.5 probability straight going;
step 3.3, along xnearTo xrandThe distance of the step is advanced to obtain xnew；
Step 3.4, collisionFree (M, E) is adoptedi) Method for detecting Edge (x)new,xnear) Whether collision exists between the map and a barrier in the map environment or not can be judged, and if no collision exists, one-time successful space expansion search can be completed;
step 3.5, calculate the new point xnewAnd if the distance from the end point is less than the target point threshold value, finishing the tracking and generating the track.
4. A method for planning the movement of a robot arm according to claim 3, wherein in step 3, a map environment is initialized and a starting point x is determinedinitAnd target point xgoalAnd setting a target point threshold, and when the target point threshold is reached, the target point threshold is considered to be reached, wherein the threshold is set as follows:
Thr＝0.03。
5. the method for planning the motion of the robot arm according to claim 1, wherein the step 4 is specifically performed according to the following steps:
step 4.1, the track obtained in step 3 is optimized by adopting three times of non-uniform B-spline interpolation:
k non-uniform B-spline curve definitions:
wherein d isiN is a control point, and the control points are sequentially connected to form a B spline control polygon, and N is a control pointi,k(U) is a k-th order canonical B-spline basis function defined on the non-uniform node vector U; and is provided with
N when K is 0i,k(u) if the degree is zero (i.e., K is 0), then the basis functions are all step functions, if in the ith node interval [ u ═ 0 ]i,ui+1]N of the above, then basis functioni,0(u)＝1；
Wherein li＝|di-di-1|(i＝1,2,...,n)；
Wherein k is an even number of times:
where k is an odd number of times:
path point q obtained based on P-RRTiObtaining a control point d by inverse solution of the non-uniform B-splineiAnd then generating a smooth track through the control points, completing the track optimization obtained in the step 3, and completing the motion planning of the mechanical arm.
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Cited By (8)
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| CN112947489A (en) * | 2021-04-08 | 2021-06-11 | 华东理工大学 | Method and device for planning collision-free path of welding robot in complex environment |
| CN113084811A (en) * | 2021-04-12 | 2021-07-09 | 贵州大学 | Mechanical arm path planning method |
| CN113246143A (en) * | 2021-06-25 | 2021-08-13 | 视比特(长沙)机器人科技有限公司 | Mechanical arm dynamic obstacle avoidance trajectory planning method and device |
| CN113478495A (en) * | 2021-09-08 | 2021-10-08 | 南京蓝昊智能科技有限公司 | Multi-dimensional mechanical arm smooth path planning method |
| CN113954073A (en) * | 2021-11-08 | 2022-01-21 | 北京华航唯实机器人科技股份有限公司 | Trajectory analysis method and device for movable part of robot and robot equipment |
| CN114227672A (en) * | 2021-11-26 | 2022-03-25 | 北京工业大学 | Mechanical arm safety collision trajectory planning method and device, storage medium and equipment |
| CN114310918A (en) * | 2022-03-14 | 2022-04-12 | 珞石（北京）科技有限公司 | Mechanical arm track generation and correction method under man-machine cooperation |
| CN114952870A (en) * | 2022-07-29 | 2022-08-30 | 山东亚历山大智能科技有限公司 | Four-axis mechanical arm motion control method and system for high-frequency contact object disinfection |
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| CN113954073A (en) * | 2021-11-08 | 2022-01-21 | 北京华航唯实机器人科技股份有限公司 | Trajectory analysis method and device for movable part of robot and robot equipment |
| CN113954073B (en) * | 2021-11-08 | 2024-03-22 | 北京华航唯实机器人科技股份有限公司 | Track analysis method and device for movable part of robot and robot equipment |
| CN114227672A (en) * | 2021-11-26 | 2022-03-25 | 北京工业大学 | Mechanical arm safety collision trajectory planning method and device, storage medium and equipment |
| CN114227672B (en) * | 2021-11-26 | 2022-07-26 | 北京工业大学 | Mechanical arm safety collision track planning method and device, storage medium and equipment |
| CN114310918A (en) * | 2022-03-14 | 2022-04-12 | 珞石（北京）科技有限公司 | Mechanical arm track generation and correction method under man-machine cooperation |
| CN114952870A (en) * | 2022-07-29 | 2022-08-30 | 山东亚历山大智能科技有限公司 | Four-axis mechanical arm motion control method and system for high-frequency contact object disinfection |
| CN114952870B (en) * | 2022-07-29 | 2023-09-29 | 山东亚历山大智能科技有限公司 | Four-axis mechanical arm motion control method and system for high-frequency contact object disinfection |
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