CN112975939A - Dynamic trajectory planning method for cooperative mechanical arm - Google Patents

Dynamic trajectory planning method for cooperative mechanical arm Download PDF

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CN112975939A
CN112975939A CN201911275074.5A CN201911275074A CN112975939A CN 112975939 A CN112975939 A CN 112975939A CN 201911275074 A CN201911275074 A CN 201911275074A CN 112975939 A CN112975939 A CN 112975939A
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mechanical arm
vector
human body
human
obstacle
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于海斌
王洪光
姜勇
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Shenyang Institute of Automation of CAS
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Shenyang Institute of Automation of CAS
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning

Abstract

The invention relates to a dynamic trajectory planning method for a collaborative mechanical arm, and belongs to the field of dynamic trajectory planning. The method comprises the following steps: step one, finishing secondary development of a human body detection program based on a Kinect library file, and realizing position detection of 26 joint points of a human body under a Kinect camera coordinate system; and step two, establishing a kinematic model of the cooperative mechanical arm, and realizing the online detection of the human-computer distance in the same working space by using a Kinect camera and homogeneous coordinate transformation. And step three, aiming at the complexity and unpredictability of the human body barrier in the human-computer cooperation scene, an APF-RV dynamic trajectory planning algorithm is provided. For static human body obstacles, the method utilizes an improved artificial potential field method to plan the track. For dynamic human body obstacle, the method utilizes an improved rejection vector method to avoid the obstacle. And finally, the processing methods of the static barrier and the dynamic barrier are fused, an APF-RV algorithm is provided, and the trajectory planning is carried out on the human body barriers in different motion states in a time-sharing manner.

Description

Dynamic trajectory planning method for cooperative mechanical arm
Technical Field
The invention relates to the field of dynamic trajectory planning, in particular to a dynamic trajectory planning method for a collaborative mechanical arm.
Background
In a human-computer cooperation environment, a worker and the mechanical arm are located in the same working space, and the movement of the worker may block the normal working state of the mechanical arm. When touching human body or other obstacles, the mechanical arm will pause the working process, if the obstacle exists all the time, the mechanical arm will keep the quasi-static state all the time, and the working efficiency of the mechanical arm will be greatly influenced.
The international and important research institutions invest a great deal of manpower and material resources to research, and the trajectory planning problem is an important part in the robot research, and the emphasis is to make the robot autonomously and safely move from an initial position posture to a target position posture. Path planning of a robot is mainly divided into two large aspects, namely local path planning and global path planning. The local trajectory planning only considers the environment information which can be obtained by the robot at present, the response is rapid, the operation speed is high, and the method is very suitable for a dynamic environment. The defects are that the local trajectory planning algorithm may not be converged, the robot cannot be guaranteed to reach a target point certainly, and the found path may deviate from the shortest path. Common local trajectory planning algorithms include fuzzy logic methods, neural network methods, genetic algorithms, artificial potential field methods, and the like. The global trajectory planning algorithm is a method for planning a trajectory by using global information, and generally stores surrounding obstacle information in a map, and finds a feasible path therein by using the map. The global trajectory planning has the advantages that a global feasible solution and an optimal solution can be found conveniently, and the defects that the operation time is long and the method is not suitable for a rapidly-changing dynamic environment are overcome. Common global trajectory planning algorithms include a free space method, a grid method, a visual graph method, a topological method and the like.
Disclosure of Invention
The invention mainly solves the problem of dynamic track planning of a mechanical arm in a man-machine cooperation environment under the condition of human body obstacle, wherein a worker is used as an obstacle for planning the track of the mechanical arm, the motion of the worker has unpredictable performance, and the worker has two conditions of motion and static state. Aiming at static human body obstacles, the method utilizes an improved artificial potential field method to carry out a track planning method aiming at the static obstacles. Aiming at dynamic human body obstacles, the method utilizes an improved rejection vector method to avoid the obstacles. And finally, the processing methods of the static barrier and the dynamic barrier are fused, and an APF-RV algorithm is provided for planning the dynamic track of the mechanical arm in a man-machine cooperation environment.
The invention discloses a dynamic trajectory planning method of a cooperative mechanical arm, which comprises the following steps:
step 1: detecting the positions of 26 human joint points under a Kinect camera coordinate system and displaying human skeleton model images by using a Kinect camera based on a Kinect library file;
step 2: establishing a kinematic model under a mechanical arm working coordinate system, converting the two coordinate systems to the same working space by utilizing a Kinect camera coordinate system and homogeneous coordinate transformation, and carrying out human-computer distance online detection;
and step 3: and aiming at the position and motion speed relation between the human body barrier and the mechanical arm end effector in the human-computer cooperation scene, an APF-RV dynamic trajectory planning algorithm is adopted to generate a rejection vector on line, and the dynamic collision avoidance planning of the human body barrier in different motion states in different time intervals is realized.
The human skeleton model image display adopts OpenCV.
The method for detecting the positions of 26 joint points of a human body under a Kinect camera coordinate system comprises the following steps:
step 1-1: outputting a command to open the Kinect camera;
step 1-2: acquiring Kinect data, and acquiring each frame of data and corresponding time; the Kinect data are positions of human body joint points and a human body skeleton model image;
step 1-3: and acquiring the mapping relation between the position coordinates of the human joint point and the working coordinate system of the mechanical arm.
The man-machine distance online detection comprises the following steps:
through the Kinect library function GetJoints () and the space vector method, the actual distance between the human and the computer is calculated as follows:
Figure BDA0002315343790000021
wherein x isl、yl、zlAs three-dimensional position coordinates, x, of a human body obstacler、yr、zrIs the three-dimensional position coordinates of the end effector of the mechanical arm.
The method is characterized in that an APF-RV dynamic track planning algorithm is adopted to generate rejection vectors on line aiming at the pose and motion speed relation of the human body barrier and the mechanical arm end effector in a human-computer cooperation scene, so that dynamic collision avoidance planning of the human body barrier in different motion states in different time intervals is realized, and the method comprises the following steps:
step 3-1: for static human body obstacles, an improved artificial potential field method is utilized to carry out a track planning method aiming at the static obstacles;
step 3-2: aiming at dynamic human body obstacles, the obstacle avoidance of the obstacles is carried out by utilizing an improved rejection vector method.
The improved artificial potential field method comprises the following steps:
when the human body barrier is converted into a static state from motion, firstly, a repulsive force F of the barrier to the mechanical arm end effector is obtained by utilizing a repulsive force fieldrep(X) is represented by the formula:
Figure BDA0002315343790000031
wherein the content of the first and second substances,
Figure BDA0002315343790000032
meaning to derive the X vector, Urep(X) is a repulsive force field, krepρ is the distance between the target point and the obstacle, ρ is the repulsive gain factor0Is the effective distance of the repulsive force.
Then, the gravity F of the target point to the end effector of the mechanical arm is obtained through the gravity fieldatt(X) is represented by the formula:
Figure BDA0002315343790000033
wherein x isG，yG，zGIs three-dimensional of target pointCoordinates, Uatt(X) is a gravitational field, faxIs the gravitational force in the direction of the x-axis, fayGravitation in the direction of the y-axis, fazIs gravitational force in the z-axis direction, kattIs a gravitational gain coefficient;
and calculating the resultant force of the attraction force and the repulsion force, and determining the direction and the speed of the trajectory planning of the mechanical arm under the static obstacle, thereby controlling the end effector of the mechanical arm to move towards the target direction.
The improved exclusion vector method comprising:
calculate the initial rejection vector equation:
Figure BDA0002315343790000041
wherein a is a shape factor, beta represents an included angle between the rejection vector and the rejection vector variation, rho is a distance between the target point and the obstacle, and V islimitThe maximum speed limited by the end effector of the mechanical arm, and D (P, O) is the distance between the end effector and the obstacle;
β is calculated according to the following equation:
Figure BDA0002315343790000042
β＝arccos(αTγ); judging the possibility of collision through the value of beta;
if the value of beta is larger than pi/2, the barrier moves in the direction far away from the control point, and the initial repulsion vector is utilized to plan the track under the condition;
when beta is smaller than pi/2, the human body barrier moves towards the direction of the mechanical arm, the distance between the human body and the machine is continuously reduced, and in order to ensure that no human-machine collision occurs, the repulsion vector needs to be adjusted, specifically, the repulsion vector is changed in direction through an intermediate variable theta, wherein the theta is obtained through the following formula:
Figure BDA0002315343790000043
wherein n is orthogonal to the repulsion vector Vr(P, O) and repulsive vector variationChemical quantity
Figure BDA0002315343790000044
A unit vector of the plane where v is a unit vector orthogonal to the plane where n and the repulsive vector change amount are located,
Figure BDA0002315343790000045
is the maximum value of the variation of the repulsion vector, c is the gain factor, and the theta function is determined by the magnitude of the variation of the repulsion vector when
Figure BDA0002315343790000046
Approaching the maximum value of the set repulsion vector variation
Figure BDA0002315343790000047
When, the value of theta tends to 0; as it approaches 0, the value of θ tends to be π/2;
by means of the intermediate variables obtained above, an improved repulsion vector V can be obtainedrpThe formula (P, O) is shown below:
Vrp(P,O)＝||Vr(P,O)||(αcosθ+γsinθ)
and determining the direction of the obstacle avoidance track motion of the mechanical arm end effector through the improved repulsion vector, wherein the speed of the mechanical arm end effector is the absolute value of the repulsion vector.
The invention has the beneficial effects that: aiming at the complexity and unpredictability of the human body barrier, an APF-RV algorithm is provided, the track planning is carried out on the human body barrier in different motion states in a time-sharing mode, and under the condition that the static barrier exists in the man-machine cooperation environment, the track planning with the static barrier exists is realized by adopting an improved artificial potential field method. Then, aiming at the situation that the human body obstacle may have movement, an improved repulsion vector method is adopted, so that the human body obstacle can better cope with the dynamic obstacle, and the human-computer collision is avoided. By adopting the method, the dynamic collision avoidance planning of the human body barriers in different motion states at different time intervals is realized.
Drawings
FIG. 1 is a flowchart of a human joint point position detection and human skeleton model image display process;
FIG. 2 is a flowchart of a human-machine distance acquisition procedure;
FIG. 3 is a flow chart of an APF-RV dynamic trajectory planning algorithm;
fig. 4 is a flowchart of a dynamic trajectory planning procedure.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention, but are not intended to limit the scope of the invention.
The invention discloses a dynamic trajectory planning method of a cooperative mechanical arm, which comprises the following steps:
step 1: finishing secondary development of a human body detection program based on a Kinect library file, and realizing position detection of 26 joint points of a human body under a Kinect camera coordinate system and human body skeleton model image display based on OpenCV;
step 2: establishing a kinematic model of the cooperative mechanical arm, and realizing human-computer distance online detection in the same working space by using a Kinect camera and homogeneous coordinate transformation;
and step 3: aiming at the complexity and unpredictability of the human body barriers in a human-computer cooperation scene, an APF-RV dynamic trajectory planning algorithm is provided, exclusion vectors are generated on line according to the position and motion speed relation of a human body and a mechanical arm end effector in a working space, and dynamic collision avoidance planning of the human body barriers in different motion states in different time intervals is achieved.
Fig. 1 shows a flowchart of a human joint point position detection and human skeleton model image display procedure, which realizes position detection of 26 joint points of a human body in a Kinect camera coordinate system and human skeleton model image display based on OpenCV.
The human body joint point position detection and human body skeleton model image display comprises the following specific processes:
step 1-1: opening a Kinect sensor;
hr＝GetDefaultKinectSensor(&m_pKinectSensor)；
hr＝m_pKinectSensor->Open()；
step 1-2: displaying the positions of human joint points and human skeleton model images and providing a way for a Reader (Reader) (color data is taken as an example, ColorFrameSource)
hr＝m_pKinectSensor->get_ColorFrameSource(&pColorFrameSource)；
Step 1-3: acquiring the positions of human body joint points and each frame data of a human body skeleton model image;
hr＝pColorFrameSource->OpenReader(&m_pColorFrameReader)；
step 1-4: acquiring a corresponding frame time parameter and acquiring data of a latest frame;
hr＝m_pColorFrameReader->AcquireLatestFrame(&pColorFrame)；
this data can then be used for corresponding processing, without the aid of the SDK.
Step 1-5: and closing the Kinect sensor variable, releasing the reader variable and the like. The coordinate mapper is also used for obtaining the coordinate at the level of the Kinect sensor, for example:
hr＝m_pKinectSensor->get_CoordinateMapper(&m_pCoordinateMapper)。
fig. 2 is a flowchart of the man-machine distance acquisition procedure. The calculation of the distance is relatively simple compared with the implementation of the drawing, and the distance between the two hands is calculated by taking the above-mentioned flow chart as an example. The positions of the left hand and the right hand in the camera coordinate system are respectively JointLefthand.position and JointRighthand.position (the data can be directly obtained through a GetJoins () function), and the actual distance can be calculated through a space vector method as follows:
Figure BDA0002315343790000071
on the basis of realizing left-right hand distance measurement, the position of the right hand is replaced by the position of the mechanical arm end effector, and the man-machine distance can be detected.
The human-computer distance online detection process specifically comprises the following steps:
step 2-1: the distance between the two hands is calculated. The positions of the left hand and the right hand in the camera coordinate system are respectively JointLefthand.position and JointRighthand.position (the data can be directly obtained through a GetJoins () function), and the actual distance can be calculated through a space vector method as follows:
Figure BDA0002315343790000072
step 2-2: on the basis of realizing left-right hand distance measurement, the position of the right hand is replaced by the position of the mechanical arm end effector, and the man-machine distance can be detected.
Fig. 3 is a flow chart of the APF-RV dynamic trajectory planning algorithm. The method is a time-sharing processing mode, an improved artificial potential field method and a repulsion vector method are fused for obstacle avoidance of an obstacle, when the obstacle appears in the range of a mechanical arm, a Kinect camera is used for judging whether the obstacle is in a moving state or not, if the obstacle is static, a motion track is directly generated by using the improved artificial potential field method, so that an obstacle avoidance process of the mechanical arm is completed, if the obstacle is in the moving state, the repulsion vector method is used for obstacle avoidance, the man-machine safety is kept to the maximum extent, and the generation of collision accidents is avoided.
The APF-RV dynamic trajectory planning algorithm is concretely as follows:
judging whether the obstacle is in a moving state by using a Kinect camera, if the obstacle is static, directly generating a moving track by using an improved artificial potential field method so as to complete an obstacle avoidance process of the mechanical arm, and if the obstacle is in the moving state, preferentially avoiding the obstacle by using a repulsion vector method;
step 3-1: when the human body obstacle is converted into a static state from motion, the trajectory is replanned by using an improved artificial potential field method, and firstly, a repulsive force F of the obstacle to the mechanical arm end effector is obtained by using a repulsive force fieldrep(X) is represented by the formula:
Figure BDA0002315343790000081
wherein the content of the first and second substances,
Figure BDA0002315343790000082
meaning to derive the X vector, Urep(X) is a repulsive force field, krepρ is the distance between the target point and the obstacle, ρ is the repulsive gain factor0Is the effective distance of repulsion;
then, the gravity F of the target point to the end effector of the mechanical arm is obtained through the gravity fieldatt(X) is represented by the formula:
Figure BDA0002315343790000083
wherein x isG，yG，zGAs three-dimensional coordinates of the target point, Uatt(X) is a gravitational field, faxIs the gravitational force in the direction of the x-axis, fayGravitation in the direction of the y-axis, fazIs gravitational force in the z-axis direction, kattIs the gravity gain coefficient.
And finally, calculating the resultant force of the attraction force and the repulsion force, and determining the direction and the speed of the trajectory planning of the mechanical arm under the static obstacle, thereby controlling the end effector of the mechanical arm to move towards the target direction.
Step 3-2: as shown in fig. 4, dynamic trajectory planning is performed, and if a human body obstacle appears in the motion process of the mechanical arm, obstacle avoidance behavior planning of the mechanical arm is performed.
The initial repulsion vector equation RU is shown below, where D (P, O) is the distance between the end effector and the obstacle:
Figure BDA0002315343790000084
wherein a is a shape factor, beta represents an included angle between the rejection vector and the rejection vector variation, rho is a distance between the target point and the obstacle, and V islimitThe maximum speed limited by the end effector of the mechanical arm, and D (P, O) is the distance between the end effector and the obstacle;
first by calculationTo a and gamma, where Vr(P, O) is the repulsion vector,
Figure BDA0002315343790000091
then, the values of alpha and gamma are used to obtain the value of beta through the following formula: beta is arccos (alpha)Tγ), the probability that a collision will occur can be derived from the value of β.
If the value of beta is greater than pi/2, the obstacle is moved in a direction away from the control point in this case, and the trajectory is planned using the initial repulsion vector in this case.
When beta is smaller than pi/2, the human body barrier moves towards the direction of the mechanical arm, the distance between the human body and the machine is continuously reduced, and in order to ensure that no human-machine collision occurs, the repulsion vector needs to be adjusted, specifically, the direction of the repulsion vector is changed through an intermediate variable theta, wherein the theta can be obtained through the following formula:
Figure BDA0002315343790000092
wherein n is orthogonal to the repulsion vector Vr(P, O) and repulsive vector variation
Figure BDA0002315343790000093
A unit vector of the plane where v is a unit vector orthogonal to the plane where n and the repulsive vector change amount are located,
Figure BDA0002315343790000094
is the maximum value of the variation of the repulsion vector, c is the gain factor, and the theta function is determined by the magnitude of the variation of the repulsion vector when
Figure BDA0002315343790000095
Approaching the maximum value of the set repulsion vector variation
Figure BDA0002315343790000096
When, the value of theta tends to 0; as it approaches 0, the value of θ tends to be π/2;
through the above obtained intermediate variables, an improved repulsion vector can be obtained as shown in the following formula:
Vrp(P,O)＝||Vr(P,O)||(αcosθ+γsinθ)
the direction of the obstacle avoidance track motion of the mechanical arm end effector can be obtained through the improved repulsion vector, and the speed of the mechanical arm end effector is the absolute value of the repulsion vector.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (7)

1. A dynamic trajectory planning method of a collaborative mechanical arm is characterized by comprising the following steps:
step 1: detecting the positions of 26 human joint points under a Kinect camera coordinate system and displaying human skeleton model images by using a Kinect camera based on a Kinect library file;
step 2: establishing a kinematic model under a mechanical arm working coordinate system, converting the two coordinate systems to the same working space by utilizing a Kinect camera coordinate system and homogeneous coordinate transformation, and carrying out human-computer distance online detection;
and step 3: and aiming at the position and motion speed relation between the human body barrier and the mechanical arm end effector in the human-computer cooperation scene, an APF-RV dynamic trajectory planning algorithm is adopted to generate a rejection vector on line, and the dynamic collision avoidance planning of the human body barrier in different motion states in different time intervals is realized.
2. The method of claim 1, wherein the human skeletal model image display is OpenCV.
3. The method for planning the dynamic trajectory of the collaborative robot according to claim 1, wherein the detecting the positions of 26 joint points of the human body in the Kinect camera coordinate system comprises:
step 1-1: outputting a command to open the Kinect camera;
step 1-2: acquiring Kinect data, and acquiring each frame of data and corresponding time; the Kinect data are positions of human body joint points and a human body skeleton model image;
step 1-3: and acquiring the mapping relation between the position coordinates of the human joint point and the working coordinate system of the mechanical arm.
4. The method for dynamic trajectory planning of a collaborative robot according to claim 1, wherein the online human-machine distance detection comprises:
through the Kinect library function GetJoints () and the space vector method, the actual distance between the human and the computer is calculated as follows:
Figure FDA0002315343780000011
wherein x isl、yl、zlAs three-dimensional position coordinates, x, of a human body obstacler、yr、zrIs the three-dimensional position coordinates of the end effector of the mechanical arm.
5. The dynamic trajectory planning method for the collaborative type mechanical arm according to claim 1, wherein an APF-RV dynamic trajectory planning algorithm is adopted for online generation of rejection vectors according to the pose and motion speed relationship between the human body barrier and the mechanical arm end effector in a human-computer collaborative scene, so that dynamic collision avoidance planning for the human body barrier in different motion states in different time intervals is realized, and the method comprises the following steps:
step 3-1: for static human body obstacles, an improved artificial potential field method is utilized to carry out a track planning method aiming at the static obstacles;
step 3-2: aiming at dynamic human body obstacles, the obstacle avoidance of the obstacles is carried out by utilizing an improved rejection vector method.
6. The method of claim 5, wherein the improved artificial potential field method comprises:
when the human body barrier is converted into a static state from motion, firstly, a repulsive force F of the barrier to the mechanical arm end effector is obtained by utilizing a repulsive force fieldrep(X) is represented by the formula:
Figure FDA0002315343780000021
wherein the content of the first and second substances,
Figure FDA0002315343780000022
meaning to derive the X vector, Urep(X) is a repulsive force field, krepρ is the distance between the target point and the obstacle, ρ is the repulsive gain factor0Is the effective distance of repulsion;
then, the gravity F of the target point to the end effector of the mechanical arm is obtained through the gravity fieldatt(X) is represented by the formula:
Figure FDA0002315343780000023
wherein x isG，yG，zGAs three-dimensional coordinates of the target point, Uatt(X) is a gravitational field, faxIs the gravitational force in the direction of the x-axis, fayGravitation in the direction of the y-axis, fazIs gravitational force in the z-axis direction, kattIs a gravitational gain coefficient;
and calculating the resultant force of the attraction force and the repulsion force, and determining the direction and the speed of the trajectory planning of the mechanical arm under the static obstacle, thereby controlling the end effector of the mechanical arm to move towards the target direction.
7. The method of claim 5, wherein the improved rejection vector method comprises:
calculate the initial rejection vector equation:
Figure FDA0002315343780000031
wherein a is a shape factor, beta represents an included angle between the rejection vector and the rejection vector variation, rho is a distance between the target point and the obstacle, and V islimitThe maximum speed limited by the end effector of the mechanical arm, and D (P, O) is the distance between the end effector and the obstacle;
β is calculated according to the following equation:
Figure FDA0002315343780000032
β＝arccos(αTγ); judging the possibility of collision through the value of beta;
if the value of beta is larger than pi/2, the barrier moves in the direction far away from the control point, and the initial repulsion vector is utilized to plan the track under the condition;
when beta is smaller than pi/2, the human body barrier moves towards the direction of the mechanical arm, the distance between the human body and the machine is continuously reduced, and in order to ensure that no human-machine collision occurs, the repulsion vector needs to be adjusted, specifically, the repulsion vector is changed in direction through an intermediate variable theta, wherein the theta is obtained through the following formula:
Figure FDA0002315343780000033
wherein n is orthogonal to the repulsion vector Vr(P, O) and repulsive vector variation
Figure FDA0002315343780000034
A unit vector of the plane where v is a unit vector orthogonal to the plane where n and the repulsive vector change amount are located,
Figure FDA0002315343780000035
is the maximum value of the variation of the repulsion vector, c is the gain coefficient, and the function theta is from repulsion toThe amount of the change in the amount is determined when
Figure FDA0002315343780000036
Approaching the maximum value of the set repulsion vector variation
Figure FDA0002315343780000037
When, the value of theta tends to 0; as it approaches 0, the value of θ tends to be π/2;
by means of the intermediate variables obtained above, an improved repulsion vector V can be obtainedrpThe formula (P, O) is shown below:
Vrp(P,O)＝||Vr(P,O)||(αcosθ+γsinθ)
and determining the direction of the obstacle avoidance track motion of the mechanical arm end effector through the improved repulsion vector, wherein the speed of the mechanical arm end effector is the absolute value of the repulsion vector.
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Publication number Priority date Publication date Assignee Title
CN114055471A (en) * 2021-11-30 2022-02-18 哈尔滨工业大学 Mechanical arm online motion planning method combining neural motion planning algorithm and artificial potential field method
CN114055471B (en) * 2021-11-30 2022-05-10 哈尔滨工业大学 Mechanical arm online motion planning method combining neural motion planning algorithm and artificial potential field method
CN114918923A (en) * 2022-06-20 2022-08-19 山东大学 Method and system for avoiding human body by mechanical arm in near-distance man-machine cooperation environment
CN114918923B (en) * 2022-06-20 2023-09-22 山东大学 Method and system for avoiding human body by mechanical arm in near-distance man-machine cooperation environment
CN116983086A (en) * 2023-09-26 2023-11-03 北京长木谷医疗科技股份有限公司 Autonomous joint replacement surgery robot navigation positioning system
CN116983086B (en) * 2023-09-26 2024-01-09 北京长木谷医疗科技股份有限公司 Autonomous joint replacement surgery robot navigation positioning system

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Application publication date: 20210618