CN113246139A - Mechanical arm motion planning method and device and mechanical arm - Google Patents
Mechanical arm motion planning method and device and mechanical arm Download PDFInfo
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Abstract
The application provides a mechanical arm motion planning method and device and a mechanical arm, and relates to the technical field of intelligent mechanical control. The method comprises the following steps: acquiring position data of an object to be grabbed, collision volume information and pose data of an end effector of the mechanical arm; determining the motion track of the end effector according to the pose data, the position data of the object to be grabbed and the growth steps; and moving according to the collision volume information and the barrier growth steps on the basis of the movement track to obtain a target track of the barrier growth of the end effector, wherein the target track is a target movement scheme of the mechanical arm for movement planning. The method can perform iterative calculation on the motion trail of the mechanical arm, and adjust, plan and optimize the motion trail of the mechanical arm by combining the information of the object to be grabbed and the obstacle, thereby effectively improving the stability and efficiency of the motion planning of the mechanical arm.
Description
Technical Field
The application relates to the technical field of intelligent mechanical control, in particular to a mechanical arm motion planning method and device and a mechanical arm.
Background
With the rapid development of artificial intelligence and mechanical control technology, when a robot executes complex tasks, more and more redundant-freedom-degree mechanical arms are applied to the robot to complete various complex tasks, the redundant-freedom-degree mechanical arms are mechanical arms with seven joints, the redundant-freedom-degree mechanical arms have seven degrees of freedom, and compared with mechanical arms with six joints, the additional joints allow the mechanical arms to avoid certain specific targets, so that an end effector can reach specific positions conveniently, and the redundant-freedom-degree mechanical arms can adapt to certain special working environments more flexibly.
In the current use of the redundant degree of freedom mechanical arm, because the redundant degree of freedom mechanical arm does not have an analytic inverse kinematics solution, the motion planning of the redundant degree of freedom mechanical arm is mainly based on a sampling path search algorithm, and then the motion planning is performed by using, for example, a PRM (Probabilistic Roadmap) algorithm and an RRT (rapid expansion Random Tree) algorithm, however, in the use of, for example, the PRM algorithm and the RRT algorithm, a user cannot adjust a planning result, and therefore the planning result is different every time, so that the motion planning stability and the planning efficiency of the redundant degree of freedom mechanical arm are low, and the redundant degree of freedom mechanical arm cannot enter the industrial field.
Disclosure of Invention
In view of the above, an object of the embodiments of the present application is to provide a method and an apparatus for planning a motion of a robot arm, and a robot arm, so as to solve the problem of low stability and efficiency of the motion planning of the robot arm in the prior art.
In order to solve the above problem, in a first aspect, an embodiment of the present application provides a method for planning motion of a robot arm, where the method includes:
acquiring position data of an object to be grabbed, collision volume information and pose data of an end effector of the mechanical arm;
determining the motion trail of the end effector according to the pose data, the position data of the object to be grabbed and the growth steps;
and moving according to the collision volume information and the obstacle growth steps on the basis of the motion track to obtain a target track of the end effector for obstacle growth, wherein the target track is a target motion scheme of the mechanical arm for motion planning.
In the implementation process, the mechanical arm is made to grow on the basis of the growth steps through the acquired pose data and the position data of the object to be grabbed, and the motion track of the mechanical arm during the growth of the end effector can be acquired through iterative computation. And the mechanical arm is enabled to perform obstacle growth based on the obstacle growth steps through the motion track and the collision volume information, the target track during the obstacle growth of the end effector is obtained through iterative calculation, the target track is the result of the mechanical arm motion planning, a motion planning target motion scheme is obtained, the motion track of the mechanical arm can be effectively calculated and adjusted, and the motion track of the mechanical arm is optimized by combining the information of the object to be grabbed and the obstacle, so that the stability and the efficiency of the mechanical arm motion planning are improved.
Optionally, the motion trajectory is a trajectory that ignores the collision volume information; the determining the motion trail of the end effector according to the pose data, the position data of the object to be grabbed and the growth steps comprises the following steps:
determining an initial track of the end effector according to the pose data, the position data and the growth steps;
judging whether the initial track meets collision constraint;
and if the initial track meets the collision constraint, taking the initial track as the motion track.
In the implementation process, when the motion track of the end effector is determined, the initial track is taken as the motion track by judging the collision constraint of the obtained initial track, so that the accuracy of the motion track is improved when the condition of the collision constraint is met.
Optionally, after the determining whether the initial trajectory satisfies a collision constraint, the method further includes:
if the initial track does not meet the collision constraint, adjusting the initial track to obtain an initial adjustment track;
increasing the number of growth steps to obtain an increased number of steps until the increased number of steps reaches the maximum number of growth steps of the end effector;
acquiring a track of the end effector moving on the initial adjustment track according to the increased steps to update the initial adjustment track to obtain a first updated track;
and repeating the judging, adjusting and updating steps on the first updating track until the first updating track meets the collision constraint, and taking the current first updating track meeting the collision constraint as the motion track.
In the implementation process, when the initial trajectory does not satisfy the condition of the collision constraint, the first updated trajectory obtained after the initial trajectory performs the above steps can be obtained through the steps of adjusting, increasing the number of steps and updating the initial trajectory. By judging whether the first updating track meets the collision constraint or not, taking the first updating track as the motion track when the collision constraint is met, and continuously adjusting and updating the first updating track when the collision constraint is not met, the motion track meeting the collision constraint can be finally obtained, and the motion track of the end effector is continuously adjusted and optimized, so that the most energy-saving path track meeting the collision constraint is taken as the motion track, and the accuracy and the real-time performance of the motion track are improved.
Optionally, the adjusting the initial trajectory to obtain an initial adjusted trajectory includes:
performing collision detection on a plurality of points in the initial track to obtain a first point to be adjusted which does not meet the collision detection in the initial track;
the first point to be adjusted is locally adjusted to obtain a first adjusting point which meets the collision constraint and meets the requirement of relevance between the first adjusting point and an adjacent point;
and generating the initial adjustment track according to the first adjustment point.
In the implementation process, when the initial trajectory is adjusted, each point in the initial trajectory can be detected through the collision detection, so that a plurality of first points to be adjusted which do not meet the requirement of collision detection for adjustment are obtained. The adjusted initial adjustment track is obtained by adjusting the first point to be adjusted, the initial track can be subjected to collision detection and adjustment, the initial track is optimized, the adjusted initial adjustment track meets the collision constraint, and the adaptability of the initial adjustment track is improved.
Optionally, the moving based on the motion trajectory according to the collision volume information and the obstacle growth step number to obtain a target trajectory of the end effector for obstacle growth, including:
on the basis of the motion track, determining an initial obstacle track of the end effector according to the collision volume information and the obstacle growth steps;
judging whether the initial obstacle track meets collision constraint;
and if the initial obstacle track meets the collision constraint, taking the initial obstacle track as the target track.
In the implementation process, after the motion trajectory of the end effector is determined, the initial obstacle trajectory is obtained according to the motion trajectory, the collision volume information and the obstacle growth steps, and the initial obstacle trajectory is judged through collision constraint, so that when the condition of collision constraint is met, the initial obstacle trajectory is taken as the target trajectory, and the accuracy of the target trajectory is improved.
Optionally, after the determining whether the initial obstacle trajectory satisfies a collision constraint, the method further comprises:
if the initial obstacle trajectory does not meet the collision constraint, adjusting the initial obstacle trajectory to obtain an initial obstacle adjustment trajectory;
increasing the barrier growth steps to obtain barrier increase steps until the barrier increase steps reach the maximum barrier growth steps of the end effector;
acquiring a track of the end effector growing on the initial obstacle adjusting track according to the obstacle increasing step number so as to update the initial obstacle adjusting track to obtain a second updating track;
and repeating the judging, adjusting and updating steps on the second updating track until the second updating track meets the collision constraint, and taking the current second updating track meeting the collision constraint as the target track.
In the implementation process, when the initial obstacle trajectory does not satisfy the condition of collision constraint, the second updated trajectory obtained after the initial obstacle trajectory performs the above steps can be obtained by adjusting the initial obstacle trajectory, increasing the number of obstacle growth steps, and updating the initial obstacle trajectory. By judging whether the second updating track meets the collision constraint or not, taking the second updating track as the target track when the collision constraint is met, and continuously performing the steps of repeatedly adjusting and updating on the second updating track when the collision constraint is not met, the target track meeting the collision constraint can be finally obtained, the obstacle motion track of the end effector is continuously adjusted and optimized, the most energy-saving path track meeting the collision constraint is taken as the target track, and the accuracy and the real-time performance of the target track are improved.
Optionally, the adjusting the initial obstacle trajectory to obtain an initial obstacle adjustment trajectory includes:
performing collision detection on a plurality of points in the initial obstacle track to obtain a second point to be adjusted which does not meet the collision detection in the initial obstacle track;
locally adjusting the second point to be adjusted to obtain a second adjusting point which meets the collision constraint and meets the requirement of relevance between the second point to be adjusted and an adjacent point;
and generating the initial obstacle adjusting track according to the second adjusting point.
In the implementation process, when the initial obstacle trajectory is adjusted, each point in the initial obstacle trajectory can be detected through the collision detection, so that a plurality of second points to be adjusted which do not meet the requirement of the collision detection can be obtained. The adjusted initial obstacle adjustment track is obtained by adjusting the second point to be adjusted, the initial obstacle track can be subjected to collision adjustment, the initial obstacle track is optimized, the adjusted initial obstacle adjustment track meets the collision constraint, and the adaptability of the initial obstacle adjustment track is improved.
Optionally, the collision volume information comprises first collision volume information and second collision volume information; the position data of the object to be grabbed, the collision volume information and the pose data of the end effector of the mechanical arm are acquired, and the method comprises the following steps:
calculating the position data of the object to be grabbed and the volume information of the obstacle;
calculating the first collision volume information from the position data and the end effector;
calculating the second collision volume information according to the volume information of the obstacle and the mechanical arm;
calculating a coordinate position of the end effector when the end effector grasps based on the position data;
and carrying out coordinate transformation according to the coordinate position to obtain the pose data.
In the implementation process, the collision volume information and the pose data when the end effector captures are calculated according to the capture position or the pose in the calculated position data of the object to be captured and the volume information of the obstacle in the motion process of the mechanical arm, so that the collision volume information and the target position when the mechanical arm performs motion planning can be obtained, and the aim and the accuracy of the motion planning of the mechanical arm are improved.
In a second aspect, an embodiment of the present application further provides a robot motion planning apparatus, where the apparatus includes:
the acquisition unit is used for acquiring position data of an object to be grabbed, collision volume information and pose data of an end effector of the mechanical arm;
the growth unit is used for determining the motion track of the end effector according to the pose data, the position data of the object to be grabbed and the growth steps;
and the obstacle growing unit is used for carrying out movement according to the collision volume information and the obstacle growing steps on the basis of the movement track to obtain a target track of the end effector for obstacle growth, wherein the target track is a target movement scheme for the mechanical arm to carry out movement planning.
In the implementation process, the acquisition unit acquires target data information, so that the growth unit performs growth based on the growth steps, and the motion track of the mechanical arm during the growth of the end effector is acquired through iterative computation; the mechanical arm is enabled to perform obstacle growth based on the obstacle growth steps through the obstacle growth unit based on the motion track and the collision volume information, the target track during the obstacle growth of the end effector is obtained through iterative calculation, the target track is used as a result of the mechanical arm motion planning, a motion planning target motion scheme is obtained, the motion track of the mechanical arm can be effectively calculated and adjusted, the motion track of the mechanical arm is optimized by combining information of an object to be grabbed and an obstacle, and therefore stability and efficiency of the mechanical arm motion planning are improved.
In a third aspect, an embodiment of the present application further provides a robot arm, where a readable storage medium is disposed in the robot arm, and the readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the steps in the method in any one of the first aspect are executed.
In the implementation, the robot arm may perform the steps of any one of the methods in the first aspect to perform motion planning on the robot arm.
In summary, the embodiment of the application provides a method and a device for planning the motion of a mechanical arm, and the mechanical arm, wherein the motion trajectory of the mechanical arm during growth and obstacle growth is optimized, the optimized target trajectory is a target motion scheme for planning the motion of the mechanical arm, iterative computation can be performed on the motion trajectory of the mechanical arm, and the motion trajectory of the mechanical arm is optimized, adjusted and planned by combining information of an object to be grabbed and an obstacle, so that the stability and the efficiency of the motion planning of the mechanical arm are effectively improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a method for planning a motion of a robot arm according to an embodiment of the present disclosure;
fig. 2 is a detailed flowchart of step S2 according to an embodiment of the present disclosure;
fig. 3 is a detailed flowchart of step S24 according to the embodiment of the present disclosure;
fig. 4 is a detailed flowchart of step S3 according to the embodiment of the present disclosure;
fig. 5 is a detailed flowchart of step S34 according to the embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a robot motion planning apparatus according to an embodiment of the present application.
Icon: 100-a mechanical arm motion planning device; 110-an obtaining unit; 120-a growth unit; 130-barrier growth unit.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of them. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without any creative effort belong to the protection scope of the embodiments of the present application.
The embodiment of the application provides a mechanical arm motion planning method, which is applied to various mechanical arms, such as redundant mechanical arms with various degrees of freedom, and can obtain an optimized target track after the mechanical arm performs motion planning by calculating and planning a motion track and an obstacle growth track of the mechanical arm, and the target track is used as a target motion scheme of the motion planning, so that the calculation and the planning of the motion track of the mechanical arm are realized, and the stability and the efficiency of the motion planning of the mechanical arm are improved.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for planning a motion of a robot according to an embodiment of the present disclosure, where the method includes:
step S1, position data of the object to be grasped, collision volume information, and pose data of the end effector of the robot arm are acquired.
The robot comprises a mechanical arm, an end effector and a robot body, wherein when the end effector of the mechanical arm is grabbed, position data of an object to be grabbed, collision volume information and pose data of the end effector based on the position data of the object to be grabbed are obtained.
It is worth mentioning that the method is applicable to various robots, such as a redundant robot with various degrees of freedom, which is a robot with a degree of freedom greater than that required to complete the primary task of the end effector of the robot. And the end effector of the mechanical arm is any tool which is connected to the edge (joint) of the robot and has certain functions. The end effector may comprise a robotic gripper, a robotic implement quick-change device, a robotic crash sensor, a robotic rotary connector, a robotic pressure tool, a compliance device, a robotic spray gun, a robotic burr cleaning tool, a robotic arc welding gun, a robotic electric welding gun, or the like, an accessory, or an end tool. When Motion Planning (Motion Planning) is performed on the Motion trail of the mechanical arm, a path which meets the constraint condition is found for the mechanical arm of the robot between a given position A and a given position B. This constraint may be a collision-free, shortest path, minimum mechanical work, etc. constraint determined according to the user's requirements.
Optionally, the collision volume information comprises first collision volume information and second collision volume information; the position data of the object to be grabbed, the collision volume information and the pose data of the end effector of the mechanical arm are acquired, and the method comprises the following steps:
calculating the position data of the object to be grabbed and the volume information of the obstacle;
calculating the first collision volume information from the position data and the end effector;
calculating the second collision volume information according to the volume information of the obstacle and the mechanical arm;
calculating a coordinate position of the end effector when the end effector grasps based on the position data;
and carrying out coordinate transformation according to the coordinate position to obtain the pose data.
It should be noted that first collision volume information when the gripping jaw grips the object to be gripped may be calculated according to the position data of the end effector and the object to be gripped, for example, data of the volume of the gripping jaw and the volume and position of the object to be gripped, and second collision volume information when the entire robot arm collides with the obstacle may be calculated according to the volume information of the obstacle and the volume information of the robot arm, for example, information of the radius and size of the obstacle and the length of the robot arm.
Alternatively, the position data of the object to be grasped may include data such as coordinate data and pose data of the object to be grasped, and the volume information of the obstacle may include data such as a three-dimensional space occupation map, size data, and coordinate data of the obstacle.
For example, taking a 7-degree-of-freedom mechanical arm as an example, when the pose data of the end effector is acquired, the acquired pose data is the pose data of the last joint point of the 7-degree-of-freedom mechanical arm, the calculation method may be to perform coordinate transformation from the base of the 7-degree-of-freedom mechanical arm to the last joint according to a chain rule, and the calculation method of the pose data Tf of the last joint is as follows:
wherein q1 to q7 are joint angles of seven degrees of freedom of the 7-degree-of-freedom robot arm, and the calculation mode of the pose data tse after the end effector is transformed according to the pose data Tf of the first-to-last joint is as follows:
after the execution of step S1, the execution of step S2 is continued.
And step S2, determining the motion trail of the end effector according to the pose data, the position data of the object to be grabbed and the growth steps.
The growth step number is the result of dividing the total length of the end effector by the growth step length, and when the mechanical arm moves, the movement direction is the direction from the last joint of the mechanical arm to the end effector. And performing growth with the growth step number as a base number according to the pose data when the end effector performs grabbing and the position data of the object to be grabbed, and determining the motion track of the end effector which moves when the collision volume is ignored.
It is worth to be noted that the determined motion trajectory is an initial barrier-free collision trajectory which does not consider first collision volume information when the end effector collides with the object to be grabbed and second collision volume information when the whole mechanical arm collides with the object to be grabbed in the motion process, for example, for a mechanical arm in an initial state which does not grow, a barrier-free collision trajectory is planned based on pose data when the end effector grasps and position data of the object to be grabbed, the barrier-free collision trajectory is a motion trajectory with the shortest task path or the least energy for the mechanical arm to complete, the motion trajectory is planned on the basis of the barrier-free collision trajectory, adverse effects of invalid data on the motion trajectory are reduced, and the accuracy of the motion trajectory is effectively improved.
After the execution of step S2, the execution of step S3 is continued.
And step S3, performing movement according to the collision volume information and the obstacle growth steps on the basis of the movement track to obtain a target track of the end effector for obstacle growth.
The obstacle growth step number is a growth step number determined based on the total length of the end effector and collision volume information, when the mechanical arm performs obstacle growth, the growth direction is from the last joint of the mechanical arm to the end effector, the step length of the obstacle growth step number is determined by the total length of the end effector and data such as radius data of an object to be grabbed or an obstacle in the collision volume information, and the obstacle growth step number can be changed based on the change of the collision volume information. When the target track is determined, the target track can be correspondingly adjusted according to the change of the obstacle and the object to be grabbed, and the method is suitable for various different conditions.
It is worth to be noted that when the obstacle growth is performed on the mechanical arm, the obstacle growth is performed based on the acquired motion track, the obstacle growth is performed on the basis of the motion track according to the first collision volume information of the end effector colliding with the object to be grabbed during grabbing and the second collision volume information of the mechanical arm colliding with the obstacle during the motion process, and the motion track is subjected to iterative computation updating and adjustment, so that the target track with the shortest path for bypassing the obstacle to perform collision-free grabbing on the object to be grabbed is obtained.
It should be noted that, when considering the collision volume information to perform obstacle growth and iterative computation on the motion trajectory, the growth strategy of the end effector may be considered first, the motion trajectory may be iteratively computed according to the first collision volume information of the end effector colliding with the object to be grabbed when the end effector is grabbing, and the motion trajectory may be continuously adjusted according to the growth of the end effector during the iterative computation until the end effector finishes growing the first collision volume information. And then considering the growth strategy of the obstacle, performing iterative calculation on the motion track according to second collision volume information of the collision between the mechanical arm and the obstacle in the motion process, and continuously adjusting the motion track according to the growth of the obstacle in the iterative calculation process until the mechanical arm finishes the growth of the second collision volume information.
Optionally, the obtained target trajectory is used as an optimized motion trajectory after the motion planning of the mechanical arm, and is used as a target motion scheme for the motion planning of the mechanical arm, so that the motion trajectory of the mechanical arm is effectively calculated and adjusted.
In the embodiment shown in fig. 1, the motion trajectory of the mechanical arm can be optimized by calculating and adjusting the motion trajectory of the mechanical arm and combining the information of the object to be grabbed and the obstacle, so that the stability and efficiency of the motion planning of the mechanical arm are improved.
Referring to fig. 2, fig. 2 is a detailed flowchart of step S2 according to an embodiment of the present disclosure, including:
and step S21, determining the initial track of the end effector according to the pose data, the position data and the growth steps.
The end effector determines the target position of the track according to the pose data and the position data, grows by taking the current growth step number as a base number, and determines the initial track during growth through iterative computation.
Optionally, the initial trajectory of the end effector may be obtained by growing according to the number of growing steps on the basis of the unobstructed trajectory.
After the execution of step S21, the execution of step S22 is continued.
Step S22, determining whether the initial trajectory satisfies a collision constraint.
Optionally, when judging whether the initial trajectory meets the condition of collision constraint, the judged object is the collision process between the clamping jaw in the end effector and the object to be grabbed, the collision constraint is the condition constraint in the collision, and may be based on the principle of impulse generation and gaussian minimum constraint in the collision process, that is, under an ideal constraint condition, the real motion of the system is compared with the possible motion with the same position, speed and constraint condition but different acceleration at a certain instant, and the real motion should make the "constraint" take a minimum value.
After the execution of step S22, the execution continues with step S23 or step S24.
Step S23, if the initial trajectory satisfies the collision constraint, taking the initial trajectory as the motion trajectory.
And step S24, if the initial trajectory does not meet the collision constraint, adjusting the initial trajectory to obtain an initial adjustment trajectory.
When the initial trajectory meets the condition of collision constraint, the initial trajectory is directly used as an optimal solution to obtain the motion trajectory meeting the collision constraint.
When the initial trajectory does not meet the conditions of the collision constraints, the initial trajectory needs to be adjusted, and the initial trajectory is subjected to iterative computation according to the adjustment on the basis of the original initial trajectory to obtain the adjusted initial adjustment trajectory meeting the collision constraints.
After the execution of step S24, the execution of step S25 is continued.
Step S25, increasing the number of growth steps to obtain an increased number of steps until the increased number of steps reaches the maximum number of growth steps of the end effector.
Wherein the maximum number of growth steps is the maximum number of growth steps of the end effector calculated based on the length of the end effector and the position data of the object to be grasped.
After the execution of step S25, the execution of step S26 is continued.
Step S26, obtaining a trajectory of the end effector moving on the initial adjustment trajectory according to the incremental steps, so as to update the initial adjustment trajectory, thereby obtaining a first updated trajectory.
It should be noted that, after a first update trajectory is obtained, the determining, adjusting and updating steps may be repeated for the first update trajectory until the first update trajectory satisfies the collision constraint, so that the current first update trajectory satisfying the collision constraint is the motion trajectory.
Optionally, after the initial adjustment trajectory is obtained, the number of growth steps of the mechanical arm is increased, iterative calculation is performed on the first update trajectory every time the number of growth steps is increased, the formed first update trajectory is judged according to the collision constraint condition, the first update trajectory is taken as the motion trajectory when the collision constraint is met, the steps of judgment, adjustment and update are repeated when the collision constraint is not met, until the generated first update trajectory meets the collision constraint, the corresponding motion trajectory is obtained, and the obtained motion trajectory is the optimal motion trajectory which meets the collision constraint, and has the shortest path, the fewest steps and the fewest energy consumption.
Optionally, when the number of the increase steps reaches the maximum growth step number, and the obtained first update trajectory is judged and still does not meet the collision constraint, the error of the first update trajectory is large, the motion planning on the motion trajectory of the mechanical arm fails, data can be collected again, and the motion trajectory of the mechanical arm is recalculated and planned.
In the embodiment shown in fig. 2, the motion trajectory satisfying the collision constraint can be finally obtained by judging, adjusting and updating the initial trajectory, and the motion trajectory of the end effector is continuously adjusted and optimized to use the path trajectory satisfying the collision constraint and saving the most energy as the motion trajectory, thereby improving the accuracy and the real-time performance of the motion trajectory.
Optionally, referring to fig. 3, fig. 3 is a detailed flowchart of step S24 provided in this embodiment of the present application, including:
step S241, performing collision detection on multiple points in the initial trajectory to obtain a first point to be adjusted in the initial trajectory, where the collision detection is not satisfied.
The initial trajectory includes a plurality of trajectory points, a current-based Collision Detection algorithm, that is, processing of current signals of the mechanical arm, is adopted when Collision Detection (CD) is performed on a plurality of points, and the current fluctuation is detected by using a dynamic threshold method to realize Collision Detection, and a graph-based real-time Collision Detection algorithm can be further adopted, which is mainly divided into a Hierarchical Bounding box method (which uses a Bounding box with a slightly larger volume and simple geometric characteristics to approximately describe a complex geometric object, so that only an object overlapped by the Bounding box needs to be subjected to further intersection test) and a Space division method (Space Detection) and other Collision Detection methods.
Optionally, the plurality of points in the initial trajectory include a point which does not need to be adjusted and satisfies the collision detection and a first point to be adjusted, and the plurality of first points to be adjusted are screened out according to the result of the collision detection.
After step S241 is executed, step S242 is continued.
Step S242, performing local adjustment on the first point to be adjusted to obtain a first adjustment point that satisfies the collision constraint and satisfies the requirement of relevance with an adjacent point.
The method comprises the steps of adopting a local sampling method to locally adjust a first point to be adjusted, which needs to be adjusted, until a plurality of first points to be adjusted are adjusted to be first adjusting points meeting the requirements of collision constraint and the relevance between adjacent points, presetting the first adjusting points to meet the requirements of the relevance between the first adjusting points and the adjacent points, meeting the relevance between a plurality of points, preventing the generation of error data of a farther point caused by the fact that the collision constraint is met, reducing the error of the first adjusting points, and improving the accuracy of the first adjusting points.
After step S242 is executed, step S243 is continuously executed.
Step S243, generating the initial adjustment track according to the first adjustment point.
And adjusting the initial track according to the first adjusting point to obtain the initial adjusting track meeting the collision constraint.
Optionally, when the first updated trajectory does not satisfy the collision constraint, the first updated trajectory is adjusted as described above to obtain a first updated adjusted trajectory that satisfies the collision constraint.
In the embodiment shown in fig. 3, the initial trajectory can be optimized by performing collision detection and adjustment on the initial trajectory, so that the adjusted initial adjustment trajectory satisfies collision detection, and the adaptability of the initial adjustment trajectory is improved.
Referring to fig. 4, fig. 4 is a detailed flowchart of step S3 according to the present embodiment, including:
and step S31, determining an initial obstacle track of the end effector on the basis of the motion track according to the collision volume information and the obstacle growth steps.
And on the basis of the obtained motion track, barrier growth and iterative calculation are carried out on the motion track on the basis of the collision volume information and the current barrier growth step number, and a corresponding initial barrier track is obtained through iterative calculation.
Optionally, the number of obstacle growth steps can be changed according to the change of the collision volume information, so that the mechanical arm can adapt to various different objects to be grabbed and obstacles during motion planning.
After the execution of step S31, the execution of step S32 is continued.
Step S32, determining whether the initial obstacle trajectory satisfies a collision constraint.
Optionally, when judging whether the initial obstacle trajectory meets the collision constraint condition, the judged object is a collision process between the complete mechanical arm and the obstacle and between the complete mechanical arm and the object to be grabbed, and the collision constraint condition is the same as the condition for judging whether the initial trajectory meets the collision constraint condition.
After the execution of step S32, the execution continues with step S33 or step S34.
Step S33, if the initial obstacle trajectory satisfies the collision constraint, taking the initial obstacle trajectory as the target trajectory.
And step S34, if the initial obstacle trajectory does not meet the collision constraint, adjusting the initial obstacle trajectory to obtain an initial obstacle adjustment trajectory.
When the initial obstacle trajectory meets the condition of collision constraint, the initial obstacle trajectory is directly used as an optimal solution to obtain a target trajectory meeting the collision constraint.
When the initial obstacle trajectory does not satisfy the condition of the collision constraint, the initial obstacle trajectory needs to be adjusted to obtain an adjusted initial obstacle adjustment trajectory satisfying the collision constraint.
After the execution of step S34, the execution of step S35 is continued.
And step S35, increasing the obstacle growth steps to obtain obstacle increase steps until the obstacle increase steps reach the maximum obstacle growth steps of the end effector.
The maximum obstacle growth step number is the maximum obstacle growth step number of the end effector calculated based on the length of the end effector and the collision volume information, and can be changed according to the change of the collision volume information.
After the execution of step S35, the execution of step S36 is continued.
Step S36, obtaining a trajectory in which the end effector grows on the initial obstacle adjustment trajectory according to the obstacle increase step number, so as to update the initial obstacle adjustment trajectory to obtain a second updated trajectory;
it should be noted that, after a second updated trajectory is obtained, the determining, adjusting and updating steps may be repeated for the second updated trajectory until the second updated trajectory satisfies the collision constraint, so that the current second updated trajectory satisfying the collision constraint is the target trajectory.
Optionally, after the initial obstacle adjustment trajectory is obtained, increasing obstacle growth steps of the mechanical arm, performing iterative computation on a second update trajectory every time the obstacle growth steps are increased, determining a collision constraint condition on the formed second update trajectory, taking the second update trajectory as a target trajectory when the collision constraint is met, and repeating the steps of determining, adjusting and updating when the collision constraint is not met until the generated second update trajectory meets the collision constraint to obtain a corresponding target trajectory, wherein the obtained target trajectory is an optimal target trajectory which meets the collision constraint and has the shortest path, the fewest steps and the fewest energy consumption.
Optionally, when the number of obstacle increase steps reaches the maximum number of obstacle growth steps, and the obtained second update trajectory still does not meet the collision constraint after being judged, the error of the second update trajectory is large, and the data can be collected again when the motion planning of the target trajectory of the mechanical arm fails, so that the target trajectory of the mechanical arm can be recalculated and planned.
In the embodiment shown in fig. 4, the target trajectory satisfying the collision constraint can be finally obtained by judging, adjusting and updating the initial obstacle trajectory, and the motion trajectory of the end effector is continuously adjusted and optimized to use the most energy-saving obstacle growth path trajectory satisfying the collision constraint as the target trajectory, thereby improving the accuracy and the real-time performance of the target trajectory.
Optionally, referring to fig. 5, fig. 5 is a detailed flowchart of step S34 provided in this embodiment of the present application, including:
step S341, performing collision detection on multiple points in the initial obstacle trajectory to obtain a second point to be adjusted in the initial obstacle trajectory, where the second point to be adjusted does not meet the collision detection.
The multiple points in the initial obstacle track comprise points which meet the requirement of collision detection and do not need to be adjusted and second points to be adjusted which do not meet the requirement of collision detection and need to be adjusted, and the multiple second points to be adjusted are screened out according to the result of collision detection.
After step S341 is executed, step S342 is continuously executed.
Step S342, locally adjusting the second point to be adjusted to obtain a second adjustment point that satisfies the collision constraint and satisfies the requirement of relevance with an adjacent point.
And obtaining a second adjusting point meeting the requirements of collision constraint and relevance of adjacent points by adopting the same method and requirements in the first adjusting point, reducing the error of the second adjusting point and improving the accuracy of the second adjusting point.
After step S342 is completed, step S343 is continuously executed.
Step S343, generating the initial obstacle adjustment trajectory according to the second adjustment point.
And adjusting the initial obstacle track according to the second adjusting point to obtain the initial obstacle adjusting track meeting the collision constraint.
Optionally, when the second updated trajectory does not satisfy the collision constraint, the second updated trajectory is adjusted as described above to obtain a second updated adjusted trajectory that satisfies the collision constraint.
In the embodiment shown in fig. 5, the initial obstacle trajectory can be optimized by performing collision detection and adjustment on the initial obstacle trajectory, so that the adjusted initial obstacle adjustment trajectory satisfies collision detection, and the adaptability of the initial obstacle adjustment trajectory is improved.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a robot motion planning apparatus 100 according to an embodiment of the present disclosure, including: an acquisition unit 110, a growth unit 120, and an obstacle growth unit 130.
An acquisition unit 110 configured to acquire position data of an object to be grasped, collision volume information, and pose data of an end effector of the robot arm;
the growing unit 120 is configured to determine a motion trajectory of the end effector according to the pose data, the position data of the object to be grabbed, and the growing steps;
and the obstacle growing unit 130 is configured to perform movement according to the collision volume information and the obstacle growing steps on the basis of the movement trajectory to obtain a target trajectory of the end effector for obstacle growth, where the target trajectory is a target movement scheme for movement planning of the mechanical arm.
The motion trajectory determined by the growing unit 120 is a trajectory that ignores the collision volume information, and the growing unit 120 further includes: the system comprises a first growth subunit, a first judgment subunit, a first increase subunit, a first update subunit and a first adjustment subunit;
the first growth subunit is used for determining an initial track of the end effector according to the pose data, the position data and the growth steps;
the first judgment subunit is used for judging whether the initial track meets collision constraints or not;
if the initial trajectory meets the collision constraint, taking the initial trajectory as the motion trajectory;
the first adjusting subunit is used for adjusting the initial track to obtain an initial adjusting track if the initial track does not meet the collision constraint after the judgment of the first judging subunit;
a first increasing subunit, configured to increase the number of growth steps to obtain an increased number of steps until the increased number of steps reaches a maximum number of growth steps of the end effector;
the first updating subunit is configured to acquire a trajectory of the end effector moving on the initial adjustment trajectory according to the incremental steps, so as to update the initial adjustment trajectory to obtain a first updated trajectory;
and repeating the judging, adjusting and updating steps on the first updating track until the first updating track meets the collision constraint, and taking the current first updating track meeting the collision constraint as the motion track.
The first adjusting subunit is further configured to perform collision detection on multiple points in the initial trajectory to obtain a first point to be adjusted in the initial trajectory, where the collision detection is not satisfied;
the first point to be adjusted is locally adjusted to obtain a first adjusting point which meets the collision constraint and meets the requirement of relevance between the first adjusting point and an adjacent point;
and generating the initial adjustment track according to the first adjustment point.
Barrier-growth unit 130, further comprising: a second growth subunit, a second judgment subunit, a second increase subunit, a second update subunit and a second adjustment subunit;
the second growth subunit is used for determining an initial obstacle track of the end effector on the basis of the motion track according to the collision volume information and the obstacle growth steps;
the second judgment subunit is used for judging whether the initial obstacle track meets the collision constraint;
if the initial obstacle trajectory meets the collision constraint, taking the initial obstacle trajectory as the target trajectory;
the second adjusting subunit is used for adjusting the initial obstacle track to obtain an initial obstacle adjusting track if the initial obstacle track does not meet the collision constraint after the judgment of the second judging subunit;
a second increasing subunit, configured to increase the obstacle growth step number to obtain an obstacle increase step number until the obstacle increase step number reaches the maximum obstacle growth step number of the end effector;
a second updating subunit, configured to obtain a trajectory in which the end effector grows on the initial obstacle adjustment trajectory according to the obstacle increase step number, so as to update the initial obstacle adjustment trajectory to obtain a second updated trajectory;
and repeating the judging, adjusting and updating steps on the second updating track until the second updating track meets the collision constraint, and taking the current second updating track meeting the collision constraint as the target track.
The second adjusting subunit is further configured to perform collision detection on multiple points in the initial obstacle trajectory to obtain a second point to be adjusted, which does not meet the collision detection, in the initial obstacle trajectory;
locally adjusting the second point to be adjusted to obtain a second adjusting point which meets the collision constraint and meets the requirement of relevance between the second point to be adjusted and an adjacent point;
and generating the initial obstacle adjusting track according to the second adjusting point.
The obtaining unit 110 further includes: a calculation subunit and a transformation subunit;
the calculating subunit is used for calculating the position data of the object to be grabbed and the volume information of the obstacle;
calculating the first collision volume information from the position data and the end effector;
calculating the second collision volume information according to the volume information of the obstacle and the mechanical arm;
calculating a coordinate position of the end effector of the robot arm when gripping based on the position data;
and the transformation subunit is used for carrying out coordinate transformation according to the coordinate position to obtain the pose data.
Because the principle of the apparatus in the embodiment of the present application for solving the problem is similar to that of the embodiment of the robot arm motion planning method, the apparatus in the embodiment of the present application may be implemented as described in the embodiment of the method, and repeated descriptions are omitted.
The embodiment of the present application further provides a robot arm, where a readable storage medium is disposed in the robot arm, where computer program instructions are stored in the readable storage medium, and when the computer program instructions are executed by a processor, the steps in any one of the methods for planning the motion of the robot arm provided by the embodiment are executed.
It should be understood that the robotic arm may be a wide variety of robotic arms and can be configured on a wide variety of robots to accomplish tasks as desired by the user.
In summary, the embodiment of the application provides a method and a device for planning the motion of a mechanical arm, and the mechanical arm, wherein the motion trajectory of the mechanical arm during growth and obstacle growth is optimized, the optimized target trajectory is a target motion scheme for planning the motion of the mechanical arm, iterative computation can be performed on the motion trajectory of the mechanical arm, and the motion trajectory of the mechanical arm is optimized, adjusted and planned by combining information of an object to be grabbed and an obstacle, so that the stability and the efficiency of the motion planning of the mechanical arm are effectively improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. The apparatus embodiments described above are merely illustrative, and for example, the block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices according to various embodiments of the present application. In this regard, each block in the block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams, and combinations of blocks in the block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Therefore, the present embodiment further provides a readable storage medium, in which computer program instructions are stored, and when the computer program instructions are read and executed by a processor, the computer program instructions perform the steps of any of the block data storage methods. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a RanDom Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for planning the motion of a robot arm, the method comprising:
acquiring position data of an object to be grabbed, collision volume information and pose data of an end effector of the mechanical arm;
determining the motion trail of the end effector according to the pose data, the position data of the object to be grabbed and the growth steps;
and moving according to the collision volume information and the obstacle growth steps on the basis of the motion track to obtain a target track of the end effector for obstacle growth, wherein the target track is a target motion scheme of the mechanical arm for motion planning.
2. The method of claim 1, wherein the motion trajectory is a trajectory that ignores the collision volume information; the determining the motion trail of the end effector according to the pose data, the position data of the object to be grabbed and the growth steps comprises the following steps:
determining an initial track of the end effector according to the pose data, the position data and the growth steps;
judging whether the initial track meets collision constraint;
and if the initial track meets the collision constraint, taking the initial track as the motion track.
3. The method of claim 2, wherein after said determining whether the initial trajectory satisfies a collision constraint, the method further comprises:
if the initial track does not meet the collision constraint, adjusting the initial track to obtain an initial adjustment track;
increasing the number of growth steps to obtain an increased number of steps until the increased number of steps reaches the maximum number of growth steps of the end effector;
acquiring a track of the end effector moving on the initial adjustment track according to the increased steps to update the initial adjustment track to obtain a first updated track;
and repeating the judging, adjusting and updating steps on the first updating track until the first updating track meets the collision constraint, and taking the current first updating track meeting the collision constraint as the motion track.
4. The method of claim 3, wherein the adjusting the initial trajectory to obtain an initial adjusted trajectory comprises:
performing collision detection on a plurality of points in the initial track to obtain a first point to be adjusted which does not meet the collision detection in the initial track;
the first point to be adjusted is locally adjusted to obtain a first adjusting point which meets the collision constraint and meets the requirement of relevance between the first adjusting point and an adjacent point;
and generating the initial adjustment track according to the first adjustment point.
5. The method according to claim 1, wherein the performing the movement based on the movement trajectory according to the collision volume information and the obstacle growth step number to obtain a target trajectory for the end effector to perform obstacle growth comprises:
on the basis of the motion track, determining an initial obstacle track of the end effector according to the collision volume information and the obstacle growth steps;
judging whether the initial obstacle track meets collision constraint;
and if the initial obstacle track meets the collision constraint, taking the initial obstacle track as the target track.
6. The method of claim 5, wherein after said determining whether said initial obstacle trajectory satisfies a collision constraint, said method further comprises:
if the initial obstacle trajectory does not meet the collision constraint, adjusting the initial obstacle trajectory to obtain an initial obstacle adjustment trajectory;
increasing the barrier growth steps to obtain barrier increase steps until the barrier increase steps reach the maximum barrier growth steps of the end effector;
acquiring a track of the end effector growing on the initial obstacle adjusting track according to the obstacle increasing step number so as to update the initial obstacle adjusting track to obtain a second updating track;
and repeating the judging, adjusting and updating steps on the second updating track until the second updating track meets the collision constraint, and taking the current second updating track meeting the collision constraint as the target track.
7. The method of claim 6, wherein the adjusting the initial obstacle trajectory to obtain an initial obstacle adjusted trajectory comprises:
performing collision detection on a plurality of points in the initial obstacle track to obtain a second point to be adjusted which does not meet the collision detection in the initial obstacle track;
locally adjusting the second point to be adjusted to obtain a second adjusting point which meets the collision constraint and meets the requirement of relevance between the second point to be adjusted and an adjacent point;
and generating the initial obstacle adjusting track according to the second adjusting point.
8. The method of claim 1, wherein the collision volume information comprises first collision volume information and second collision volume information; the position data of the object to be grabbed, the collision volume information and the pose data of the end effector of the mechanical arm are acquired, and the method comprises the following steps:
calculating the position data of the object to be grabbed and the volume information of the obstacle;
calculating the first collision volume information from the position data and the end effector;
calculating the second collision volume information according to the volume information of the obstacle and the mechanical arm;
calculating a coordinate position of the end effector when the end effector grasps based on the position data;
and carrying out coordinate transformation according to the coordinate position to obtain the pose data.
9. An arm motion planning apparatus, the apparatus comprising:
the acquisition unit is used for acquiring position data of an object to be grabbed, collision volume information and pose data of an end effector of the mechanical arm;
the growth unit is used for determining the motion track of the end effector according to the pose data, the position data of the object to be grabbed and the growth steps;
and the obstacle growing unit is used for carrying out movement according to the collision volume information and the obstacle growing steps on the basis of the movement track to obtain a target track of the end effector for obstacle growth, wherein the target track is a target movement scheme for the mechanical arm to carry out movement planning.
10. A robot arm, wherein the robot arm comprises a readable storage medium having computer program instructions stored thereon, which when executed by a processor, perform the steps of the method of any of claims 1-8.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113858203A (en) * | 2021-10-19 | 2021-12-31 | 杭州芯控智能科技有限公司 | Robot self-adaptive track planning and obstacle avoidance method |
| CN114851211A (en) * | 2022-07-07 | 2022-08-05 | 国网瑞嘉(天津)智能机器人有限公司 | Method, device, server and storage medium for planning boom track |
| CN114896798A (en) * | 2022-05-20 | 2022-08-12 | 梅卡曼德（北京）机器人科技有限公司 | Collision detection method, control method, capture system and computer storage medium |
| CN115576332A (en) * | 2022-12-07 | 2023-01-06 | 广东省科学院智能制造研究所 | Task-level multi-robot collaborative motion planning system and method |
| CN115870976A (en) * | 2022-11-16 | 2023-03-31 | 北京洛必德科技有限公司 | Sampling trajectory planning method and device for mechanical arm and electronic equipment |
| WO2024093532A1 (en) * | 2022-10-31 | 2024-05-10 | 腾讯科技（深圳）有限公司 | Control method and apparatus for robot, and robot and storage medium |
| CN114896798B (en) * | 2022-05-20 | 2024-05-24 | 梅卡曼德（北京）机器人科技有限公司 | Collision detection method, control method, grasping system, and computer storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0934524A (en) * | 1995-07-18 | 1997-02-07 | Kobe Steel Ltd | Automatic generation method for moving path of robot manipulator |
| CN105945942A (en) * | 2016-04-05 | 2016-09-21 | 广东工业大学 | Robot off line programming system and method |
| CN108237534A (en) * | 2018-01-04 | 2018-07-03 | 清华大学深圳研究生院 | A kind of space collision free trajectory method of continuous type mechanical arm |
| CN112518756A (en) * | 2020-12-10 | 2021-03-19 | 深圳市优必选科技股份有限公司 | Motion trajectory planning method and device for mechanical arm, mechanical arm and storage medium |
| CN112828890A (en) * | 2021-01-04 | 2021-05-25 | 武汉晴川学院 | Mechanical arm track planning method and device, electronic equipment and storage medium |
-
2021
- 2021-06-15 CN CN202110658187.4A patent/CN113246139B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0934524A (en) * | 1995-07-18 | 1997-02-07 | Kobe Steel Ltd | Automatic generation method for moving path of robot manipulator |
| CN105945942A (en) * | 2016-04-05 | 2016-09-21 | 广东工业大学 | Robot off line programming system and method |
| CN108237534A (en) * | 2018-01-04 | 2018-07-03 | 清华大学深圳研究生院 | A kind of space collision free trajectory method of continuous type mechanical arm |
| CN112518756A (en) * | 2020-12-10 | 2021-03-19 | 深圳市优必选科技股份有限公司 | Motion trajectory planning method and device for mechanical arm, mechanical arm and storage medium |
| CN112828890A (en) * | 2021-01-04 | 2021-05-25 | 武汉晴川学院 | Mechanical arm track planning method and device, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 杨亮等: "基于视觉伺服的桌面型机械臂创新实验平台研制", 《实验技术与管理》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113858203A (en) * | 2021-10-19 | 2021-12-31 | 杭州芯控智能科技有限公司 | Robot self-adaptive track planning and obstacle avoidance method |
| CN114896798A (en) * | 2022-05-20 | 2022-08-12 | 梅卡曼德（北京）机器人科技有限公司 | Collision detection method, control method, capture system and computer storage medium |
| CN114896798B (en) * | 2022-05-20 | 2024-05-24 | 梅卡曼德（北京）机器人科技有限公司 | Collision detection method, control method, grasping system, and computer storage medium |
| CN114851211A (en) * | 2022-07-07 | 2022-08-05 | 国网瑞嘉(天津)智能机器人有限公司 | Method, device, server and storage medium for planning boom track |
| CN114851211B (en) * | 2022-07-07 | 2022-09-23 | 国网瑞嘉(天津)智能机器人有限公司 | Method, device, server and storage medium for planning track of boom |
| WO2024093532A1 (en) * | 2022-10-31 | 2024-05-10 | 腾讯科技（深圳）有限公司 | Control method and apparatus for robot, and robot and storage medium |
| CN115870976A (en) * | 2022-11-16 | 2023-03-31 | 北京洛必德科技有限公司 | Sampling trajectory planning method and device for mechanical arm and electronic equipment |
| CN115576332A (en) * | 2022-12-07 | 2023-01-06 | 广东省科学院智能制造研究所 | Task-level multi-robot collaborative motion planning system and method |
| CN115576332B (en) * | 2022-12-07 | 2023-03-24 | 广东省科学院智能制造研究所 | Task-level multi-robot collaborative motion planning system and method |
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Application publication date: 20210813 Assignee: MING YANG SMART ENERGY GROUP Co.,Ltd. Assignor: University OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA, ZHONGSHAN INSTITUTE Contract record no.: X2024980000459 Denomination of invention: A Method, Device, and Robot Arm Motion Planning Granted publication date: 20210928 License type: Common License Record date: 20240110 |