CN115431260A - Mechanical arm motion planning method and system based on virtual point state backtracking - Google Patents
Mechanical arm motion planning method and system based on virtual point state backtracking Download PDFInfo
- Publication number
- CN115431260A CN115431260A CN202111479894.3A CN202111479894A CN115431260A CN 115431260 A CN115431260 A CN 115431260A CN 202111479894 A CN202111479894 A CN 202111479894A CN 115431260 A CN115431260 A CN 115431260A
- Authority
- CN
- China
- Prior art keywords
- motion
- mechanical arm
- point
- path
- virtual
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000004364 calculation method Methods 0.000 claims description 26
- 238000012545 processing Methods 0.000 claims description 15
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 description 15
- 238000011217 control strategy Methods 0.000 description 14
- 238000012795 verification Methods 0.000 description 11
- 230000006399 behavior Effects 0.000 description 10
- 238000005070 sampling Methods 0.000 description 9
- 238000010276 construction Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 230000036544 posture Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000002349 favourable effect Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000007634 remodeling Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Manipulator (AREA)
- Numerical Control (AREA)
Abstract
The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm motion planning method and a mechanical arm motion planning system based on virtual point state backtracking.
Description
Technical Field
The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm motion planning method and system based on virtual point state backtracking of a mechanical arm.
Background
In extraterrestrial celestial body sampling and detecting tasks, with the gradual deepening of detection requirements and the increase of complexity of detection activities, the adoption of mechanical arms to finish approaching detection and sampling return becomes a common way for engineering implementation, and the accessibility support of the spatial positions of the mechanical arms, the strategy planning of the fine action implementation of the mechanical arms and other work become necessary technical paths for engineering implementation.
The teleoperation control is carried out on the mechanical arm, a motion planning mode is usually adopted, and the motion planning comprises two types of path planning and trajectory planning. In the space detection task, the accessibility problem of the mechanical arm is usually only required to be solved, namely the mechanical arm is controlled to move from one point to another point in the space and avoid obstacles, and the problems of movement time and the like are not required to be treated as core problems, so that the teleoperation planning of the mechanical arm can be simplified into path planning, and a planner calculates and outputs a geometric path meeting the conditions.
In practical engineering application, due to unknown space environment and complex constraint of various resources, various constraints need to be comprehensively considered for teleoperation control of the mechanical arm, and a multi-branch control strategy is formed according to different constraint conditions. If under ideal conditions, the mechanical arm needs to be controlled to reach the position of a point b from the position of a point; if some conditions are not met, in order to meet requirements such as safety and the like, the mechanical arm needs to be controlled to move from the position of a point to the position of b point through the position of p point, and under the scene, due to the difference of the positions of the moving target points, a multi-branch movement strategy is formed at the point a. For such a mode, if each branch motion strategy is planned separately, a planner needs to be called for many times, which results in a large amount of repetitive planning work, reduces planning efficiency, and is not favorable for verification and implementation of the planning strategy.
Disclosure of Invention
The invention provides a mechanical arm motion planning method and system based on virtual point state backtracking, aiming at the defects of the prior art.
The technical scheme of the mechanical arm motion planning method based on virtual point state backtracking comprises the following steps:
determining an initial mechanical arm motion scene, wherein the initial mechanical arm motion scene comprises: the method comprises the following steps of starting a position, at least one preset path point position and at least one target position, and a first path from the starting position to each target position, wherein at least one first path comprises at least one preset path point position;
calculating the branch number K of the ith preset path point position i And adding K corresponding to the position of the ith preset path point in the initial mechanical arm motion scene i -1 virtual preset path point location and for K including the ith preset path point location i Performing one-to-one corresponding replacement on the position of the ith preset path point in each first path until each first path is replaced, and performing backtracking processing on the initial mechanical arm motion scene to obtain an intermediate mechanical arm motion scene, wherein i is a positive integer;
in the middle mechanical arm motion scene, establishing an incidence relation between each preset path point position and a corresponding virtual preset path point position;
performing linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established to obtain a linear mechanical arm motion scene;
calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
and acquiring an all-state motion strategy according to a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path, and controlling the mechanical arm according to an actual motion requirement and the all-state motion strategy.
The mechanical arm motion planning method based on virtual point state backtracking has the following beneficial effects:
the method comprises the steps of obtaining an intermediate mechanical arm motion scene according to an initial mechanical arm motion scene, establishing an incidence relation between each preset path point position and a corresponding virtual preset path point position in the initial mechanical arm motion scene, performing linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established, performing path planning calculation according to the linear mechanical arm motion scene, calculating a virtual motion path of a mechanical arm and a motion strategy corresponding to the virtual motion path, further obtaining an all-state motion strategy, realizing control over the mechanical arm, avoiding repeated planning, reducing the repeated calculation amount and greatly improving the path planning efficiency of the mechanical arm.
On the basis of the scheme, the mechanical arm motion planning method based on virtual point state backtracking can be further improved as follows.
Further, the branch number K of the position of the ith preset path point is calculated i The method comprises the following steps:
calculating the branch number K of the position of the ith preset path point according to a first formula i Wherein the first formula is: k is i ＝(K in ) i ×(K out ) i ，(K in ) i Represents: from all first paths including the ith preset path point positionThe total number of the acquired first path branches directly reaching the position of the ith preset path point, (K) out ) i Represents: and the total number of the second path branches directly starting from the ith preset path point position, which are acquired from all paths comprising the ith preset path point position.
Further, the association relationship includes a semi-equivalence relationship and a full-equivalence relationship, and the full-equivalence relationship represents: position of ith preset path point and corresponding K i -any two of the 1 virtual preset path point locations have the same updated second path branch, and the semi-equivalent relationship represents: position of ith preset path point and corresponding K i -at least one of the second path branches updated by any two of the 1 virtual preset path point locations respectively is different.
The technical scheme of the mechanical arm motion planning system based on virtual point state backtracking is as follows:
the system comprises a scene preprocessing module, a calculation backtracking module, a correlation module, a linearization reconstruction module, a path planning module and a control module;
the scene preprocessing module is used for: determining an initial mechanical arm motion scene, wherein the initial mechanical arm motion scene comprises: the method comprises the following steps of starting a position, at least one preset path point position and at least one target position, and a first path from the starting position to each target position, wherein at least one first path comprises at least one preset path point position;
the computation backtracking module is used for: calculating the branch number K of the ith preset path point position i And adding K corresponding to the position of the ith preset path point in the initial mechanical arm motion scene i -1 virtual preset path point location and for K comprising the ith preset path point location i Performing one-to-one corresponding replacement on the position of the ith preset path point in each first path until each first path is replaced, and performing backtracking processing on the initial mechanical arm motion scene to obtain an intermediate mechanical arm motion scene, wherein i is a positive integer;
the association module is configured to: establishing an incidence relation between each preset path point position and a corresponding virtual preset path point position in the middle mechanical arm motion scene;
the linearized reconstruction module is to: performing linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established to obtain a linear mechanical arm motion scene;
the path planning module is configured to: calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
the control module is used for:
calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
and acquiring an all-state motion strategy according to a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path, and controlling the mechanical arm according to an actual motion requirement and the all-state motion strategy.
The mechanical arm motion planning system based on virtual point state backtracking has the following beneficial effects:
the method comprises the steps of obtaining an intermediate mechanical arm motion scene according to an initial mechanical arm motion scene, establishing an incidence relation between the position of each preset path point and the position of a corresponding virtual preset path point in the initial mechanical arm motion scene, carrying out linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established, carrying out route planning calculation according to the linear mechanical arm motion scene, calculating a virtual motion path of a mechanical arm and a motion strategy corresponding to the virtual motion path, further obtaining a full-state motion strategy, realizing control over the mechanical arm, avoiding repeated planning, reducing repeated calculation amount and greatly improving the path planning efficiency of the mechanical arm.
On the basis of the scheme, the mechanical arm motion planning system based on virtual point state backtracking can be further improved as follows.
Further, the computation backtracking module is specifically configured to:
calculating the branch number K of the position of the ith preset path point according to a first formula i Wherein the first formula is: k is i ＝(K in ) i ×(K out ) i ，(K in ) i Represents: a total number of first path branches directly reaching an ith preset path point position, acquired from all first paths including the ith preset path point position, (K) out ) i Represents: and the total number of the second path branches directly starting from the ith preset path point position is acquired from all paths comprising the ith preset path point position.
Further, the association relationship includes a semi-equivalence relationship and a full-equivalence relationship, and the full-equivalence relationship represents: position of ith preset path point and corresponding K i -any two of the 1 virtual preset path point locations have the same updated second path branch, and the semi-equivalent relationship represents: position of ith preset path point and corresponding K i -at least one of the second path branches updated by any two of the 1 virtual preset path point locations respectively is different.
The storage medium stores instructions, and when the instructions are read by a computer, the computer is enabled to execute any one of the above mechanical arm motion planning methods based on virtual point state backtracking.
An electronic device of the present invention includes a processor and the storage medium, where the processor executes instructions in the storage medium.
Drawings
Fig. 1 is a schematic flowchart of a mechanical arm motion planning method based on virtual point state backtracking according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an initial robotic arm motion scenario;
FIG. 3 is a schematic diagram of a first path;
FIG. 4 is a schematic diagram of a second first path;
FIG. 5 is a schematic view of a third first path;
FIG. 6 is a schematic view of an intermediate robotic arm motion scenario;
FIG. 7 is a schematic diagram of an intermediate mechanical arm motion scene after an association relationship is established;
FIG. 8 is a schematic view of a linearized robotic arm motion scenario;
fig. 9 is a schematic flowchart of a robot arm movement planning system based on virtual point state backtracking according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, a method for planning motion of a mechanical arm based on virtual point state backtracking in an embodiment of the present invention includes the following steps:
s1, determining an initial mechanical arm motion scene, wherein the initial mechanical arm motion scene comprises: the method comprises the following steps of starting position, at least one preset path point position, at least one target position and a first path from the starting position to each target position, wherein at least one first path comprises at least one preset path point position, the starting position, the preset path point position and the target position can be set according to actual conditions, and the first path from the starting position to each target position represents: controlling a first path of a terminal point of the robot arm from a start position to each target position;
s2, calculating the branch number K of the position of the ith preset path point i And adding K corresponding to the position of the ith preset path point in the initial mechanical arm motion scene i -1 virtual preset path point location and for K including the ith preset path point location i Performing one-to-one corresponding replacement on the position of the ith preset path point in each first path until each first path is replaced, and performing backtracking processing on the initial mechanical arm motion scene to obtain an intermediate mechanical arm motion scene, wherein i is a positive integer; wherein, to the processing of backtracking is carried out to initial arm motion scene, specifically is: utilizing all the added virtual preset path point positions to perform backtracking processing on the initial mechanical arm motion scene to obtainTo the intermediate mechanical arm motion scenario.
S3, establishing an incidence relation between each preset path point position and a corresponding virtual preset path point position in the middle mechanical arm motion scene;
s4, performing linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established to obtain a linear mechanical arm motion scene;
s5, calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
s6, acquiring an all-state motion strategy according to the virtual motion path of the mechanical arm and the motion strategy corresponding to the virtual motion path, and controlling the mechanical arm according to the actual motion requirement and the all-state motion strategy.
The method comprises the steps of obtaining an intermediate mechanical arm motion scene according to an initial mechanical arm motion scene, establishing an incidence relation between each preset path point position and a corresponding virtual preset path point position in the initial mechanical arm motion scene, performing linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established, performing path planning calculation according to the linear mechanical arm motion scene, calculating a virtual motion path of a mechanical arm and a motion strategy corresponding to the virtual motion path, further obtaining an all-state motion strategy, realizing control over the mechanical arm, avoiding repeated planning, reducing the repeated calculation amount and greatly improving the path planning efficiency of the mechanical arm.
Preferably, in the above technical solution, the calculating of the branch number K of the ith preset path point position i The method comprises the following steps:
calculating the branch number K of the position of the ith preset path point according to a first formula i Wherein the first formula is: k i ＝(K in ) i ×(K out ) i ，(K in ) i Represents: directly reaching the ith preset path point position acquired from all first paths including the ith preset path point positionTotal number of first path branches, (K) out ) i Represents: and the total number of the second path branches directly starting from the ith preset path point position, which are acquired from all paths comprising the ith preset path point position.
Preferably, in the above technical solution, the association relationship includes a semi-equivalence relationship and a full-equivalence relationship, and the full-equivalence relationship represents: position of ith preset path point and corresponding K i -any two positions of the 1 virtual preset path point positions have the same updated second path branch, and the semi-equivalent relationship represents: position of ith preset path point and corresponding K i -at least one of the updated second path branches of any two of the 1 virtual preset path point locations is different.
Wherein at least one of the updated second path branches is embodied as: at least one state in the respectively updated second path branches is different, or/and at least one position in the respectively updated second path branches is different.
The following explains a mechanical arm motion planning method based on virtual point state backtracking according to an embodiment, which specifically includes:
s20, determining an initial mechanical arm motion scene, as shown in fig. 2, where the initial mechanical arm motion scene includes an initial position, 3 preset path point positions, and 2 target positions, the initial position is marked as an O point, the 3 preset path point positions are respectively marked as an E point, a P point, and a B point, the 2 target positions are respectively marked as an E point and a T point, and the initial mechanical arm motion scene includes three first paths, which are respectively:
1) First path: from the point O to the point E, the motion path is the point O → the point A → the point E, and is marked as the motion path theta 1 ，θ 1 ＝[O，A，E]The motion strategy is as follows: omega 1 ＝[ω OA ，ω AE ]Wherein, ω is OA Represents: control parameter, omega, for controlling the end point of a robot arm from point O to point A AE Represents: control parameters for controlling the end point of the robot arm from point a to point E, as shown in fig. 3.
2) A second first path: from the point O to the point T, the motion path is the point O → the point A → the point B → the point E, and the mark is the motion path theta 2 ，θ 2 ＝[O，A，B，T]The motion strategy is as follows: omega 1 ＝[ω OA ，ω AB ，ω BT ]Wherein, ω is OA Represents: control parameter, ω, for controlling the end point of the robot arm from point O to point a AB Represents: control parameter, omega, for controlling the end point of a robot arm from point A to point B BT Represents: control parameters for controlling the end point of the robot arm from point B to point T, as shown in fig. 4.
3) Third first path: from point O to point T, the motion path is: point O → point A → point P → point B → point E, marked as motion path θ 3 ，θ 3 ＝[O，A，P，B，T]The motion strategy is as follows: omega 1 ＝[ω OA ，ω AP ，ω PB ，ω BT ]Wherein, ω is OA Represents: control parameter, ω, for controlling the end point of the robot arm from point O to point a AP Represents: control parameters for controlling the end point of the mechanical arm from the point A to the point P; omega PB Represents: control parameters for controlling the end point of the mechanical arm from the point P to the point B; omega BT Represents: control parameters for controlling the end point of the robot arm from point B to point T, as shown in fig. 5.
S21, calculating the branch number K of the position of the ith preset path point i ：
1) For example, the number of branches of point a is calculated, specifically:
all the first paths including the point A have 3, namely the first path theta 1 A second first path theta 2 And a first third path theta 3 On a first path theta 1 In the middle, the first path branch directly reaching the point A is the point O → the point A; on a second first path theta 2 The first path directly reaching point A is branched to point O → point A, and on the third first path theta 3 In the method, the first path branch directly reaching the point A is the point O → the point A, and since the three first path branches are all the same, the total number of the first path branches directly reaching the point A is determined to be 1I.e., point O → point A;
in the first path, a second path directly starting from the point A branches into the point A → the point E; in the second first path, the second path directly starting from the point A branches into the point A → the point B; in the third first path, the second path directly from the point A branches into the point A → the point P; then, the total number of the branches of the second path directly from the point a is determined to be 3, which are point a → E, point a → B and point a → P, respectively, and the number of the branches at the point a is calculated by the first formula as: 1 × 3= 3;
1) For example, the number of branches of point B is calculated, specifically:
all the first paths including the point B have 2, which are the second first paths theta 2 And a first and a third path theta 3 On a second first path theta 1 The first path directly reaching point B is branched to point A → point B, and on the third first path theta 3 In the above example, if the first path branch directly reaching point B is point P → point B, the total number of the first path branches directly reaching point B is determined to be 2, which are: point a → point B and point P → point B;
in the second first path, the second path directly from the point B branches into the point B → the point T; in the third first path, the second path directly from the point B branches into the point B → the point T; because the two second path branches are the same, the total number of the second path branches directly starting from the point B is determined to be 1, and the branch number of the point B is calculated by a first formula as follows: 2 × 1= 2;
3) For example, the number of branches of point P is calculated, specifically:
all first paths including the pth point have 1: first third path theta 3 On a third first path theta 3 If the first path branch directly reaching the point P is the point A → the point P, the total number of the first path branches directly reaching the point B is determined to be 1, namely the point A → the point P;
in the third first path, the second path directly from the point P branches into the point P → the point T; determining the total number of the second path branches directly from the point P as 1, and calculating the number of the branches at the point P by using a first formula as follows: 1 × 1= 1;
s22, determining a motion scene of the middle mechanical arm, specifically:
1) Adding virtual preset path point positions corresponding to the point A, wherein the number of the virtual preset path point positions is 3-1=2, and the virtual preset path point positions are marked as a point A1 and a point A2 respectively;
2) Adding virtual preset path point positions corresponding to the B points, wherein the number of the virtual preset path point positions is 2-1=1, and the virtual preset path point positions are marked as B1 points;
3) And adding the virtual preset path point positions corresponding to the P points, wherein the number of the virtual preset path point positions is 1-1=0, namely, the virtual preset path point positions corresponding to the P points are not added.
Wherein, K corresponding to the position of the ith preset path point i -the position parameters and attitude parameters of the 1 virtual preset path point position are the same as the position parameters and attitude parameters of the ith preset path point position;
then, for K including the ith preset path point position i And carrying out one-to-one corresponding replacement on the position of the ith preset path point in each first path to obtain: theta.theta. 1 ＝[O，A，E]，θ 2 ＝[O，A1，B，E]，θ 3 ＝[O，A2，P，B1，T]According to the updated' theta 1 ＝[O，A，E]，θ 2 ＝[O，A1，B，E]，θ 3 ＝[O，A2，P，B1，T]"go back to the initial mechanical arm motion scene, get the middle mechanical arm motion scene, as shown in fig. 6;
because the position of the ith preset path point corresponds to K i -1 virtual preset path point position and attitude parameters are the same as the position parameters and attitude parameters of the ith preset path point position, then: omega OA And omega OA1 、ω OA2 Are all the same, ω A2P And omega AP Same, ω PB1 And omega PB Same, ω B1T And omega BT Wherein ω is OA1 Represents: control parameter, ω, for controlling the end point of the robot arm from point O to point A1 OA2 Represents: control parameter, ω, for controlling the end point of the robot arm from point O to point A2 A2P Represents: control parameter, omega, for controlling the end point of the robot arm from point A2 to point P PB1 Represents: for controlling machinesControl parameter, ω, of the end point of the arm from point P to point B1 B1T Represents: control parameters for controlling the end point of the mechanical arm from the point B1 to the point T;
s23, establishing an association relationship, specifically: in the intermediate mechanical arm motion scene, establishing an association relationship between each preset path point position and a corresponding virtual preset path point position, specifically:
1) Establishing an incidence relation among the point A, the point A1 and the point A2, and updating the theta 1 ＝[O，A，E]，θ 2 ＝[O，A1，B，T]，θ 3 ＝[O，A2，P，B1，T]Then:
1) The updated second path branch corresponding to the point A is A → E, the updated second path branch corresponding to the point A1 is A1 → B, and the updated second path branch corresponding to the point A2 is A2 → P, and then the second path branches of the point A, the point A1 and the point A2 are determined to be different;
thereby judging that the association relationship between the point A and the point A1 is a half-equivalence relationship, the association relationship between the point A and the point A2 is a half-equivalence relationship, and the association relationship between the point A1 and the point A2 is a half-equivalence relationship;
2) The updated second path branch corresponding to the point B is B → T, the updated second path branch corresponding to the point B1 is B1 → T, and since the point B and the point B1 are completely the same, it is determined that the second path branches of the point B and the point B1 are different, and thus it is determined that the association relationship between the point B and the point B1 is an equivalent relationship, and thus an intermediate mechanical arm motion scene after the association relationship is established is obtained, as shown in fig. 7.
S24, determining a linear mechanical arm motion scene: according to the intermediate mechanical arm motion scene after the incidence relation is established, performing linearization reconstruction on all updated first paths to obtain a linearized mechanical arm motion scene, specifically:
according to the intermediate mechanical arm motion scene after the association relationship is established as shown in fig. 7, since the points a, A1 and A2 are in a semi-equivalence relationship and the points B and B1 are in a full-equivalence relationship, the points A1, A2 and B1 can be reached through the safe path. A state backtracking process is added from the point E to the point A1 and from the point B to the point A2, and the positions corresponding to the full equivalence relations are simplified, namely, all the updated first paths are subjected to linearization reconstruction, so that a linearization mechanical arm motion scene is obtained, as shown in fig. 8;
s25, path planning:
calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation; the virtual motion path to the starting position to the last target position in the linearized robot arm motion scene, then:
the virtual motion path θ from the point O to the point T is obtained according to fig. 8 as: o → A → E → A1 → B → A2 → P → B → T, mark θ = [ O, A, E, A1, B, A2, P, B, T]According to the virtual motion path, a motion strategy omega, omega = [ omega ] corresponding to the virtual motion path can be planned at one time OA ，ω AE ，ω EA1 ，ω A1B ，ω BA2 ，ω A2P ，ω PB ，ω BT ]Wherein, ω is OA Denotes a control parameter, ω, for controlling the end point of the robot arm from the point O to the point a AE Denotes a control parameter, ω, for controlling the end point of the robot arm from point A to point E EA1 Denotes a control parameter, ω, for controlling the end point of the robot arm from the point E to the point A1 A1B Denotes a control parameter, ω, for controlling the end point of the robot arm from point A1 to point B BA2 Denotes a control parameter, ω, for controlling the end point of the robot arm from point B to point A2 A2P Denotes a control parameter, ω, for controlling the end point of the robot arm from the point A2 to the point P PB Denotes a control parameter, ω, for controlling the end point of the robot arm from point P to point B BT Control parameters for controlling the end point of the robot arm from point B to point T are shown, and since A, A1, A2 are equivalent starting points when the multi-branch motion of the robot arm is constructed linearly, i.e., all from point O to points A, A1 and A2, ω is ω A1B ＝ω AB ，ω A2P ＝ω AP At this time, the motion strategy is Ω = [ ω ] OA ，ω AE ，ω EA1 ，ω AB ，ω BA2 ，ω AP ，ω PB ，ω BT ]。
S26, acquiring a full-state motion strategy according to the virtual motion path of the mechanical arm and the motion strategy corresponding to the virtual motion path, specifically:
1) Obtaining control parameters for controlling the mechanical arm from the point O to the point E according to the theta = [ O, A, E, A1, B, A2, P, B, T ]]And Ω = [ ω = OA ，ω AE ，ω EA1 ，ω AB ，ω BA2 ，ω AP ，ω PB ，ω BT ]It can be seen that by sequentially executing ω OA ，ω AE And the mechanical arm can be controlled from the point O to the point E, so that the corresponding first motion strategy is as follows: [ omega ] of OA ，ω AE ]The motion path is O point → A point → E point;
2) Obtaining control parameters for controlling the mechanical arm from the point O to the point T, according to theta = [ O, A, E, A1, B, A2, P, B, T]And Ω = [ ω = OA ，ω AE ，ω EA1 ，ω AB ，ω BA2 ，ω AP ，ω PB ，ω BT ]It can be seen that by sequentially executing ω OA 、ω AB And ω BT That is, the mechanical arm can be controlled from the point O to the point T, and therefore, the corresponding second motion strategy is [ ω [ ] OA ，ω AB ，ω BT ]The motion path is O point → A point → B point → E point;
and, according to θ = [ O, a, E, A1, B, A2, P, B, T]And Ω = [ ω ] OA ，ω AE ，ω EA1 ，ω AB ，ω BA2 ，ω AP ，ω PB ，ω BT ]It can be seen that by sequentially performing ω OA 、ω AP 、ω PB ，ω BT And ω BT The mechanical arm can be controlled from the point O to the point T, and the corresponding third motion strategy is [ omega ] OA ，ω AP ，ω PB ，ω BT ]The corresponding motion path is: point O → point a → point P → point B → point E.
When a user needs to move the mechanical arm from the point O to the point E through the point A, the first motion strategy is an all-state motion strategy, the mechanical arm is controlled according to the first motion strategy, and the second motion strategy and the third motion strategy can be used as all-state motion strategies to achieve control over the mechanical arm;
according to the three specific motion strategies, the control of the tail end point of the mechanical arm is realized, and in the existing path planning mode, omega is required to be controlled every time a path is calculated OA And omega AE And performing operation repeatedly, wherein in the application, the linear mechanical arm motion scene obtained through linear reconstruction can be used for planning the starting position to the last target position in the linear mechanical arm motion scene at one time, and then different control parameters are selected as required to control the mechanical arm.
According to the method and the device, a backtracking state of the virtual point bearing mechanical arm is constructed aiming at a mechanical arm multi-branch motion scene under a target point control mode, concepts of equivalence of an initial point and equivalence of a target point are introduced, the mechanical arm multi-branch motion scene is reconstructed through equivalence processing, mechanical arm multi-branch motion behaviors are longitudinally decomposed into a plurality of single-branch motion behaviors, and a plurality of single-branch motion strategies are formed.
And (3) performing state backtracking on the mechanical arm motion scene after the equivalent construction, performing linear construction on the mechanical arm multi-branch motion scene to obtain a set of the virtual motion path theta and a set of the motion strategy omega of the mechanical arm after the linear construction, and optimizing the motion path and the motion strategy set based on the equivalent relation.
After the linear construction, the planner develops planning calculation according to the motion path and the motion strategy set, and then classifies and outputs corresponding motion strategies according to the actual motion path branches.
Wherein the motion behavior is decomposed based on the setting of virtual points and state backtracking processing. Traversing the next path which the mechanical arm may reach to a target point at each path point, longitudinally splitting multiple branches of the mechanical arm movement into multiple single branch processes according to the method, and analyzing the movement path and the movement strategy of each single branch process of the mechanical arm. And setting corresponding virtual points on the basis, and adding the virtual points with the number of K-1 on the path point with the number of K branches for bearing the backtracking state of the mechanical arm and constructing a mechanical arm motion scene based on the virtual points.
The method comprises the steps of conducting equivalent processing on a mechanical arm motion scene based on virtual point remodeling, and obtaining an equivalent structure of the mechanical arm multi-branch motion scene.
Firstly, in a scene of multi-branch motion of the mechanical arm with an equivalent structure, a state backtracking process is added according to the motion of the mechanical arm which is decomposed. And then, simplifying the equivalence relation to obtain a linear relation scene structure of the multi-branch motion of the mechanical arm.
And under a mechanical arm motion linearization control mode, obtaining a mechanical arm virtual motion path and a motion control strategy set, and finishing calculation in a centralized manner through one-time planning. The actual mechanical arm motion path branch control strategies are contained in the calculation aggregate, and the planning results of the branch motion control strategies are output in a classification mode through the actual motion paths.
The invention provides a linear programming method for a mechanical arm multi-branch motion control strategy of a space detection task by analyzing a multi-branch motion scene of the mechanical arm in the space detection task. According to the method, the multi-branch motion scene of the mechanical arm is analyzed, the state backtracking is used as a core processing method, the equivalence concept is introduced for simplification processing, and deep optimization is carried out on the multi-branch motion behavior planning method of the mechanical arm. The method greatly reduces the problem of repeated planning, effectively improves the efficiency of strategy planning, provides favorable conditions for verification and implementation of planning strategies, effectively optimizes the generation mode of the mechanical arm motion control strategy, and has higher engineering application value for the planning and verification of the mechanical arm complex multi-branch motion. The method can be practically applied to strategy planning and verification of mechanical arm multi-branch motion behaviors in deep space exploration and manned space missions.
The solution idea of the invention is as follows: introducing a virtual point bearing the backtracking state of the mechanical arm, carrying out linearization processing on the multi-branch mechanical arm motion behavior in a decomposition, equivalence, backtracking and other modes, simplifying the mechanical arm motion scene with branches into a linear motion control scene with consistent control logic, and optimizing the complex multi-branch motion path control strategy planning of the extraterrestrial celestial body sampling detection mechanical arm.
The linearization programming method for extraterrestrial celestial body sampling detection mechanical arm multi-branch movement behavior based on virtual point state backtracking comprises the following steps:
s30, decomposing multi-branch motion, and setting a simple typical mechanical arm multi-branch motion scene as shown in figure 2 to obtain an initial mechanical arm motion scene;
as can be seen from fig. 2, the mechanical arm starts to move from the point O, the mechanical arm may select a predetermined movement path in the drawing, and the multi-branch movement of the mechanical arm is divided into three first paths according to the path points, which are shown in fig. 3, fig. 4 and fig. 5, respectively, and the detailed analysis of each first path is as described above and is not repeated herein;
s31, setting of virtual points: in fig. 2, point a is used as a starting point, i.e. a starting position, and 3 different moving target points, i.e. preset path point positions, E, B, and P, can be selected according to different constraints, thereby forming a target point 3 branch motion strategy, where the branch number K is out Is 3;
the point B is used as a target point and can be reached by 2 different motion starting points A and P, so that a starting point 2-branch motion strategy is formed, and the branch number K of the strategy is in Is 2.
Adding a set number of virtual points K-1 to the path point with the branch number K based on the branch number K of the point A out Adding virtual points A1 and A2, wherein the positions and postures of the virtual points A1 and A2 are the same as those of the point A; branch number K based on point B in The virtual point B1 is added, and the position and posture thereof are the same as those of the point B.
S32, state backtracking: in the typical multi-branch motion scene of the mechanical arm, after the mechanical arm moves from a point A to a point E, a virtual point of the next motion is set, the mechanical arm moves to a point B from the virtual point as a starting point, the virtual point is a bearing object for backtracking the state of the mechanical arm and is set as A1, and the setting mode of other virtual points is the same as the state backtracking mode.
When the state of the mechanical arm is backtracked, the motion variables can be simplified, and only the position, the posture, the safety and the like of the virtual point need to be considered. Obtaining a middle mechanical arm motion scene as shown in fig. 6;
s33, establishing an equivalent point concept: since the target point control mode is adopted, when a, A1, A2 are taken as the starting points, the states of the positions, postures, safety, accessibility and the like are the same, but the state parameters of the target points E, B, P are different, so that a, A1, A2 are equivalent, that is, half equivalent, to the starting points.
B. When B1 is taken as a starting point, the states of position, attitude, security, reachability, etc. are all the same, and the state parameters of the target point T are all the same, so B and B1 are all equivalent.
Reconstructing the intermediate mechanical arm motion scene shown in fig. 6 through equivalent processing to obtain the intermediate mechanical arm motion scene after the association relationship is established as shown in fig. 7;
s34, backtracking the linearization state of the equivalent reconstruction scene:
based on the multi-branch motion scene of the mechanical arm after the equivalence relation reconstruction shown in fig. 7, since the points a, A1, and A2 are equivalent as the starting points, and the points B and B1 are fully equivalent, it can be known that the points A1, A2, and B1 can all be reached through the safe path.
The state backtracking process is added from the point E to the point A1 and the point B to the point A2, the fully equivalent points are simplified, and the multi-branch motion scene of the mechanical arm can be linearly reconstructed into a linear mechanical arm motion scene shown in fig. 8;
s35, generating a multi-branch motion linearization construction control strategy:
according to fig. 8, in this type of control mode, the linearized virtual motion path θ = [ O, a, E, A1, B, A2, P, B, T of the robot arm]Motion strategy Ω = [ ω = [ ] OA ，ω AE ，ω EA1 ，ω A1B ，ω BA2 ，ω A2P ，ω PB ，ω BT ]。
When the multi-branch motion of the mechanical arm is linearly structured, A1 and A2 are equivalent to each other as starting points, so that the control parameter omega is controlled A1B ＝ω AB ，ω A2P ＝ω AP Then the motion strategy Ω = [ ω = [ ] OA ，ω AE ，ω EA1 ，ω AB ，ω BA2 ，ω AP ，ω PB ，ω BT ]。
Consider the 3 actual motion paths θ before the linearization configuration 1 ，θ 2 ，θ 3 Corresponding motion control strategy
And S36, planning the multi-branch motion strategy of the mechanical arm after linear construction. After the linearization construction, when planning the typical mechanical arm multi-branch motion scene case, the planner carries out planning calculation of the motion control strategy omega according to the linearized mechanical arm virtual motion path theta.
According to 3 actual motion paths theta 1 ，θ 2 ，θ 3 And outputs the corresponding motion strategy omega in classification 1 ，Ω 2 ，Ω 3 And the planning of each branch motion strategy is intensively completed through one planning process.
The invention has the following beneficial effects:
1) The method realizes the decomposition of the complex multi-branch motion of the extraterrestrial celestial body sampling detection mechanical arm so as to retrieve the equivalence relation and backtrack the state of the linearized multi-branch motion model, thereby greatly improving the planning efficiency.
2) The simplification of a strategy planning verification mode under the scene of complex multi-branch motion of the extraterrestrial celestial body sampling detection mechanical arm is realized.
3) The optimization of the control strategy under the complex motion scene of the extraterrestrial celestial body sampling detection mechanical arm is realized.
The invention realizes the planning and verification of the multi-branch motion strategy of the extraterrestrial celestial body sampling detection mechanical arm, improves the efficiency of path planning and verification, optimizes the multi-branch motion behavior control strategy of the mechanical arm and saves planning and verification resources.
In another embodiment, the method comprises the following steps:
s40, before a teleoperation task of the extraterrestrial celestial body sampling detection mechanical arm starts, multi-branch motion of the mechanical arm is longitudinally decomposed;
s41, calculating the branch number corresponding to the mechanical arm movement path point, and adding virtual points with the number of K-1 on the path point with the branch number of K.
And S42, carrying out backtracking processing on the mechanical arm state, loading the backtracked mechanical arm state on a virtual point, simplifying the backtracked motion variable, and only considering the position, the posture, the safety and the like of the virtual point.
And S43, introducing an equivalence point concept, and analyzing the half-equivalence and full-equivalence relations of the path points through the motion control parameters.
And S44, simplifying the multi-branch motion scene of the mechanical arm backtraced through the virtual point, and simplifying a control mode on the basis of determining the equivalence relation. And reconstructing the multi-branch motion scene of the mechanical arm through an equivalence relation.
And S45, adding a state backtracking process to the equivalent reconstructed mechanical arm motion scene, and carrying out linear construction on the mechanical arm multi-branch motion scene.
And S46, in the mechanical arm motion scene of the linear structure, the planner calculates the virtual motion path theta and the motion strategy omega set of the mechanical arm according to the mechanical arm multi-branch motion path expansion planning after the linear structure.
And S47, outputting corresponding motion strategies in a classified manner according to the actual motion path branches.
The invention provides a linear programming method for a multi-branch motion control strategy of a mechanical arm in a space detection task by analyzing a multi-branch motion scene of the mechanical arm in the space detection task. According to the method, the multi-branch motion scene of the mechanical arm is analyzed, the state backtracking is used as a core processing method, the equivalence concept is introduced for simplification processing, and the deep optimization is carried out on the multi-branch motion behavior planning method of the mechanical arm. The method greatly reduces the problem of repeated planning, effectively improves the efficiency of strategy planning, provides favorable conditions for verification and implementation of planning strategies, effectively optimizes the generation mode of the mechanical arm motion control strategy, and has higher engineering application value for the planning and verification of the mechanical arm complex multi-branch motion. The method can be practically applied to strategy planning and verification of mechanical arm multi-branch motion behaviors in deep space exploration and manned space missions.
In the above embodiments, although the steps are numbered as S1, S2, etc., but only the specific embodiments are given in the present application, and a person skilled in the art may adjust the execution sequence of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention, it is understood that some embodiments may include some or all of the above embodiments.
As shown in fig. 9, a mechanical arm motion planning system 200 based on virtual point state backtracking according to an embodiment of the present invention includes a scene preprocessing module 210, a computation backtracking module 220, an association module 230, a linearization reconstruction module 240, a path planning module 250, and a control module 260;
the scene preprocessing module 210 is configured to: determining an initial mechanical arm motion scene, wherein the initial mechanical arm motion scene comprises: the method comprises the following steps of starting a position, at least one preset path point position and at least one target position, and a first path from the starting position to each target position, wherein at least one first path comprises at least one preset path point position;
the compute backtracking module 220 is configured to: calculating the branch number K of the ith preset path point position i And adding K corresponding to the position of the ith preset path point in the initial mechanical arm motion scene i -1 virtual preset path point location and for K including the ith preset path point location i Performing one-to-one corresponding replacement on the position of the ith preset path point in each first path until each first path is replaced, and performing backtracking processing on the initial mechanical arm motion scene to obtain an intermediate mechanical arm motion scene, wherein i is a positive integer;
the association module 230 is configured to: in the middle mechanical arm motion scene, establishing an incidence relation between each preset path point position and a corresponding virtual preset path point position;
the linearized reconstruction module 240 is configured to: performing linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established to obtain a linear mechanical arm motion scene;
the path planning module 250 is configured to: calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
the control module 260 is configured to:
calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
and acquiring a full-state motion strategy according to the virtual motion path of the mechanical arm and the motion strategy corresponding to the virtual motion path, and controlling the mechanical arm according to the actual motion requirement and the full-state motion strategy.
The method comprises the steps of obtaining an intermediate mechanical arm motion scene according to an initial mechanical arm motion scene, establishing an incidence relation between the position of each preset path point and the position of a corresponding virtual preset path point in the initial mechanical arm motion scene, carrying out linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established, carrying out route planning calculation according to the linear mechanical arm motion scene, calculating a virtual motion path of a mechanical arm and a motion strategy corresponding to the virtual motion path, further obtaining a full-state motion strategy, realizing control over the mechanical arm, avoiding repeated planning, reducing repeated calculation amount and greatly improving the path planning efficiency of the mechanical arm.
Preferably, in the above technical solution, the calculation backtracking module 220 is specifically configured to:
calculating the branch number K of the ith preset path point position according to a first formula i Wherein the first formula is: k i ＝(K in ) i ×(K out ) i ，(K in ) i Represents: total number of first path branches directly reaching the ith preset path point position acquired from all first paths including the ith preset path point position, (K) out ) i Represents: and the total number of the second path branches directly starting from the ith preset path point position, which are acquired from all paths comprising the ith preset path point position.
Preferably, in the above technical solution, the association relationship includes a semi-equivalence relationship and a full-equivalence relationshipThe full equivalence relation represents: position of ith preset path point and corresponding K i -any two positions of the 1 virtual preset path point positions have the same updated second path branch, and the semi-equivalent relationship represents: position of ith preset path point and corresponding K i -at least one of the second path branches updated by any two of the 1 virtual preset path point locations respectively is different.
The above steps for realizing the corresponding functions of each parameter and each unit module in the virtual point state backtracking-based mechanical arm motion planning system 200 of the present invention refer to the above parameters and steps in the embodiment of the virtual point state backtracking-based mechanical arm motion planning method, which are not described herein again.
The storage medium of the embodiment of the present invention stores instructions, and when the computer reads the instructions, the computer is enabled to execute any one of the above methods for planning the motion of the mechanical arm based on virtual point state backtracking.
An electronic device of the present invention includes a processor and the storage medium, where the processor executes instructions in the storage medium. The electronic device can be a computer, a mobile phone and the like.
As will be appreciated by one skilled in the art, the present invention may be embodied as a system, method or computer program product.
Accordingly, the present disclosure may be embodied in the form of: may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software, and may be referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media having computer-readable program code embodied in the medium.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. A mechanical arm motion planning method based on virtual point state backtracking is characterized by comprising the following steps:
determining an initial mechanical arm motion scene, wherein the initial mechanical arm motion scene comprises: the method comprises the following steps of starting a position, at least one preset path point position and at least one target position, and a first path from the starting position to each target position, wherein at least one first path comprises at least one preset path point position;
calculating the branch number K of the ith preset path point position i And adding K corresponding to the position of the ith preset path point in the initial mechanical arm motion scene i -1 virtual preset path point location and for K comprising the ith preset path point location i Carrying out one-to-one corresponding replacement on the position of the ith preset path point in each first path until each first path is replaced, and carrying out replacement on the initial mechanical armBacktracking the motion scene to obtain a middle mechanical arm motion scene, wherein i is a positive integer;
in the middle mechanical arm motion scene, establishing an incidence relation between each preset path point position and a corresponding virtual preset path point position;
performing linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established to obtain a linear mechanical arm motion scene;
calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
and acquiring an all-state motion strategy according to a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path, and controlling the mechanical arm according to an actual motion requirement and the all-state motion strategy.
2. The method as claimed in claim 1, wherein the calculating of the branch number K of the ith preset path point position is performed by using a virtual point state backtracking-based mechanical arm motion planning method i The method comprises the following steps:
calculating the branch number K of the position of the ith preset path point according to a first formula i Wherein the first formula is: k i ＝(K in ) i ×(K out ) i ，(K in ) i Represents: total number of first path branches directly reaching the ith preset path point position acquired from all first paths including the ith preset path point position, (K) out ) i Represents: and the total number of the second path branches directly starting from the ith preset path point position is acquired from all paths comprising the ith preset path point position.
3. The method for planning the motion of the mechanical arm based on the virtual point state backtracking according to claim 1 or 2, wherein the association relationship comprises a semi-equivalence relationship and a full-equivalence relationship, and the full-equivalence relationship represents: the ith oneSetting path point position and its corresponding K i -any two of the 1 virtual preset path point locations have the same updated second path branch, and the semi-equivalent relationship represents: position of ith preset path point and corresponding K i -at least one of the updated second path branches of any two of the 1 virtual preset path point locations is different.
4. A mechanical arm motion planning system based on virtual point state backtracking is characterized by comprising a scene preprocessing module, a calculation backtracking module, a correlation module, a linearization reconstruction module, a path planning module and a control module;
the scene preprocessing module is used for: determining an initial mechanical arm motion scene, wherein the initial mechanical arm motion scene comprises: the method comprises the following steps of starting a position, at least one preset path point position and at least one target position, and a first path from the starting position to each target position, wherein at least one first path comprises at least one preset path point position;
the calculation backtracking module is used for: calculating the branch number K of the ith preset path point position i And adding K corresponding to the position of the ith preset path point in the initial mechanical arm motion scene i -1 virtual preset path point location and for K including the ith preset path point location i Performing one-to-one corresponding replacement on the position of an ith preset path point in each first path until each first path is replaced, and performing backtracking processing on the initial mechanical arm motion scene to obtain an intermediate mechanical arm motion scene, wherein i is a positive integer;
the association module is configured to: in the middle mechanical arm motion scene, establishing an incidence relation between each preset path point position and a corresponding virtual preset path point position;
the linearized reconstruction module is to: performing linear reconstruction on all updated first paths according to the intermediate mechanical arm motion scene after the incidence relation is established to obtain a linear mechanical arm motion scene;
the path planning module is configured to: calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
the control module is used for: calculating a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path according to the linear mechanical arm motion scene expansion path planning calculation;
and acquiring an all-state motion strategy according to a virtual motion path of the mechanical arm and a motion strategy corresponding to the virtual motion path, and controlling the mechanical arm according to an actual motion requirement and the all-state motion strategy.
5. The system for planning the motion of the mechanical arm based on virtual point state backtracking according to claim 4, wherein the computation backtracking module is specifically configured to:
calculating the branch number K of the position of the ith preset path point according to a first formula i Wherein the first formula is: k i ＝(K in ) i ×(K out ) i ，(K in ) i Represents: total number of first path branches directly reaching the ith preset path point position acquired from all first paths including the ith preset path point position, (K) out ) i Represents: and the total number of the second path branches directly starting from the ith preset path point position is acquired from all paths comprising the ith preset path point position.
6. The system for planning motion of a mechanical arm based on virtual point state backtracking according to claim 4 or 5, wherein the association relationship comprises a semi-equivalent relationship and a full-equivalent relationship, and the full-equivalent relationship represents: position of ith preset path point and corresponding K i -any two of the 1 virtual preset path point locations have the same updated second path branch, and the semi-equivalent relationship represents: position of ith preset path point and corresponding K i In-1 virtual preset path point positionsAt least one of the updated second path branches of any two of the positions is different.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111479894.3A CN115431260A (en) | 2021-12-07 | 2021-12-07 | Mechanical arm motion planning method and system based on virtual point state backtracking |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111479894.3A CN115431260A (en) | 2021-12-07 | 2021-12-07 | Mechanical arm motion planning method and system based on virtual point state backtracking |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115431260A true CN115431260A (en) | 2022-12-06 |
Family
ID=84239883
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111479894.3A Pending CN115431260A (en) | 2021-12-07 | 2021-12-07 | Mechanical arm motion planning method and system based on virtual point state backtracking |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115431260A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117621087A (en) * | 2024-01-11 | 2024-03-01 | 湖北理工学院 | Automatic control method, system, terminal and medium for mechanical arm based on visual feedback |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106293648A (en) * | 2015-05-13 | 2017-01-04 | 南京理工大学 | Services Composition behavior compliance measure based on Route Dependence figure |
| CN109968354A (en) * | 2019-03-07 | 2019-07-05 | 西安理工大学 | A kind of Reconfigurable Control method of discrete type rope driving parallel robot |
| CN111338384A (en) * | 2019-12-17 | 2020-06-26 | 北京化工大学 | Self-adaptive path tracking method of snake-like robot |
| CN112532554A (en) * | 2020-10-29 | 2021-03-19 | 西安空间无线电技术研究所 | One-step backtracking method for Viterbi demodulation of GMSK system |
| 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 |
| CN113650011A (en) * | 2021-08-03 | 2021-11-16 | 中国人民解放军63920部队 | Method and device for planning splicing path of mechanical arm |
-
2021
- 2021-12-07 CN CN202111479894.3A patent/CN115431260A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106293648A (en) * | 2015-05-13 | 2017-01-04 | 南京理工大学 | Services Composition behavior compliance measure based on Route Dependence figure |
| CN109968354A (en) * | 2019-03-07 | 2019-07-05 | 西安理工大学 | A kind of Reconfigurable Control method of discrete type rope driving parallel robot |
| CN111338384A (en) * | 2019-12-17 | 2020-06-26 | 北京化工大学 | Self-adaptive path tracking method of snake-like robot |
| CN112532554A (en) * | 2020-10-29 | 2021-03-19 | 西安空间无线电技术研究所 | One-step backtracking method for Viterbi demodulation of GMSK system |
| 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 |
| CN113650011A (en) * | 2021-08-03 | 2021-11-16 | 中国人民解放军63920部队 | Method and device for planning splicing path of mechanical arm |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117621087A (en) * | 2024-01-11 | 2024-03-01 | 湖北理工学院 | Automatic control method, system, terminal and medium for mechanical arm based on visual feedback |
| CN117621087B (en) * | 2024-01-11 | 2024-04-30 | 湖北理工学院 | Automatic control method, system, terminal and medium for mechanical arm based on visual feedback |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11179843B2 (en) | Method for operating a robot in a multi-agent system, robot, and multi-agent system | |
| CN109176532B (en) | Method, system and device for planning path of mechanical arm | |
| Ma et al. | Fuzzy approximate learning-based sliding mode control for deploying tethered space robot | |
| JP5137537B2 (en) | Robot work motion optimization device | |
| Sun et al. | Self-segmentation of sequences: automatic formation of hierarchies of sequential behaviors | |
| EP3802015A1 (en) | A predictive control method of a robot and related control system | |
| CN115431260A (en) | Mechanical arm motion planning method and system based on virtual point state backtracking | |
| Mohebbi et al. | Mechatronic multicriteria profile (MMP) for conceptual design of a robotic visual servoing system | |
| Sorouri et al. | Software composition and distributed operation scheduling in modular automated machines | |
| CN112422699A (en) | Unmanned aerial vehicle cluster action scheme generation method based on dynamic adjustment | |
| Teichteil-Königsbuch et al. | A generic framework for anytime execution-driven planning in robotics | |
| Xia et al. | Virtual comissioning of manufacturing system intelligent control | |
| CN113305845B (en) | Multi-mechanical arm cooperation method | |
| CN114967465A (en) | Trajectory planning method and device, electronic equipment and storage medium | |
| CN106826814B (en) | Motion control method and motion control system of robot | |
| Kshirsagar et al. | Model based design in industrial automation | |
| Wray et al. | Generalized controllers in POMDP decision-making | |
| CN115401682A (en) | Mechanical arm motion planning method and system based on scene division | |
| Li et al. | An Efficient 2-opt Operator for the Robotic Task Sequencing Problem | |
| Fertsch | Artificial neural pseudo-network for production control purposes | |
| Dahl et al. | Sequence planner-automated planning and control for ros2-based collaborative and intelligent automation systems | |
| Santana et al. | Dynamic execution of temporal plans with sensing actions and bounded risk | |
| Kosak et al. | Ensemble programming for multipotent systems | |
| Zhukova et al. | Cognitive Monitoring of Distributed Objects | |
| Matsuoka et al. | Hierarchical planning for error recovery in automated industrial robotic systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |