CN113885535B - Impact constraint robot obstacle avoidance and time optimal track planning method - Google Patents
Impact constraint robot obstacle avoidance and time optimal track planning method Download PDFInfo
- Publication number
- CN113885535B CN113885535B CN202111410601.6A CN202111410601A CN113885535B CN 113885535 B CN113885535 B CN 113885535B CN 202111410601 A CN202111410601 A CN 202111410601A CN 113885535 B CN113885535 B CN 113885535B
- Authority
- CN
- China
- Prior art keywords
- robot
- node
- obstacle
- sphere
- new
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 241000283153 Cetacea Species 0.000 claims abstract description 57
- 238000005457 optimization Methods 0.000 claims abstract description 26
- 230000001133 acceleration Effects 0.000 claims abstract description 19
- 238000001514 detection method Methods 0.000 claims abstract description 15
- 239000002775 capsule Substances 0.000 claims abstract description 10
- 230000036461 convulsion Effects 0.000 claims abstract description 9
- 238000003466 welding Methods 0.000 claims abstract description 6
- 239000012636 effector Substances 0.000 claims description 29
- 230000033001 locomotion Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 8
- 238000009499 grossing Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000008602 contraction Effects 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 claims description 3
- 108010074864 Factor XI Proteins 0.000 claims description 2
- 230000001154 acute effect Effects 0.000 claims description 2
- 230000005540 biological transmission Effects 0.000 claims description 2
- 239000003638 chemical reducing agent Substances 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims description 2
- 230000002596 correlated effect Effects 0.000 claims description 2
- 230000007423 decrease Effects 0.000 claims description 2
- 238000009826 distribution Methods 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 claims description 2
- 230000019637 foraging behavior Effects 0.000 claims description 2
- 238000012804 iterative process Methods 0.000 claims description 2
- 238000012886 linear function Methods 0.000 claims description 2
- 238000013178 mathematical model Methods 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 claims description 2
- 230000001502 supplementing effect Effects 0.000 claims description 2
- 239000002699 waste material Substances 0.000 claims description 2
- 230000009191 jumping Effects 0.000 description 7
- 230000002068 genetic effect Effects 0.000 description 4
- 230000004888 barrier function Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 241000965478 Darksidea epsilon Species 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Numerical Control (AREA)
- Manipulator (AREA)
Abstract
The invention relates to an industrial robot track planning method, in particular to an impact constrained robot obstacle avoidance and time optimal track planning method. The track planning system can autonomously plan a smooth track meeting the speed, acceleration and jerk limitations under the condition of inputting limited parameters. The system mainly comprises a path planning module, a spline interpolation module, an obstacle avoidance processing module and a time optimization track planning module. The path planning module introduces the ideas of target bias and target preference under the obstacle repulsive force field, the spline interpolation module adopts a quintic spline interpolation function to interpolate and smooth the path key nodes, the obstacle avoidance processing module utilizes a geometric fine detection method to execute collision avoidance processing on the industrial robot and the obstacle, wherein a robot connecting rod and a welding gun utilize a capsule body envelope, the obstacle utilizes a sphere envelope, the time optimization track module combines a grading system and a greedy strategy with a traditional whale optimization algorithm, and the global searching capability of the algorithm is improved.
Description
Technical Field
The invention belongs to the field of industrial robot track planning, and in particular relates to an impact constrained robot obstacle avoidance and time optimal track planning method.
Background
The industrial robot is a machine device organically combined by a machine, an electronic device and a computer, realizes the processing and manufacturing functions of various industries, and is widely applied to various industrial fields such as flexible production lines, automatic factories and the like. In the industrial field, the robot can complete corresponding actions through a plurality of operation points by only giving an operation starting point for the movement of the industrial robot, so that the flexibility of the movement between the starting point and the ending point of the robot is ensured, the abrasion of a mechanical structure is reduced, the robot can rapidly act on the premise of meeting various indexes of a servo motor, and the action process of the robot is required to be subjected to track planning.
The motion trail of the industrial robot directly influences the energy consumption and the working time of the industrial robot, and is a key technology for guaranteeing the execution efficiency and the running performance of the robot. Generally, as the number of obstacles in the working space increases and the size of the obstacles increases, the solving space between the robot working points is also rapidly complicated, and the time optimal track planning problem can be converted into a complicated nonlinear optimizing process for obtaining the interval time of each control node, and the method for solving the problem comprises a genetic algorithm, an artificial bee colony algorithm, a frog-leaping algorithm, a Memetic algorithm and the like. Because of the numerous and complex factors considered in trajectory planning, it is difficult to manually obtain a reasonable trajectory.
In summary, the industrial robot and the structural barriers are fully considered in the track planning, so that an ideal track which meets the requirements of the operation task and has no collision and short movement time is planned, and the improvement of the industrial robot work efficiency is important.
Disclosure of Invention
Aiming at the problem that obstacles exist in the track planning of the industrial robot, the invention provides an integrated solution for obstacle avoidance and time optimal track planning of the industrial robot.
The aim of the invention is realized by the following technical scheme: the system adopts a modularized method to complete the track planning of the industrial robot, and mainly comprises a path planning module, a spline interpolation module, an obstacle avoidance processing module and a time optimization track planning module. The path planning module introduces target bias and target preference ideas based on the safety constraint of the obstacle repulsive force field on the basis of an RRT algorithm, compresses a search space and accelerates the search speed of the shortest path of the industrial robot; the spline interpolation module carries out interpolation smoothing on the path key nodes by adopting a cubic spline interpolation function, and builds an AT matrix by taking initial and termination speeds and initial and termination accelerations as fixed boundary conditions; the obstacle avoidance processing module performs collision avoidance processing on the industrial robot and the obstacle by using a geometric fine detection method, and obtains a robot joint and a base TCP point by using a forward and backward kinematics of the robot, wherein a robot connecting rod and a welding gun are enveloped by using a capsule body, and the obstacle is enveloped by using a sphere; the time optimization track module combines a hierarchical system and a greedy strategy with a traditional whale optimization algorithm, so that the global searching capability of the algorithm is improved.
An impact constrained robot obstacle avoidance and time optimal track planning method comprises the following steps:
s1: initializing an environment model, an obstacle environment and a robot model, and initializing the number of whales in a whale population, the iteration number and an initial population solution;
s2: setting the posture of a welding gun and the initial step length of an improved RRT algorithm, setting the initial and termination speeds of a robot Cartesian space to be 0m/s and the initial and termination acceleration to be 0m/s2;
s3: inputting X, Y, Z axis range of working environment, inputting DH model parameters of robot, and inputting initial node q init With target node q goal Inputting the limit values of the speed, acceleration and jerk of the TCP point of the robot;
s4: from obstacle to target node q goal Calculating the repulsive force field range by the distance and the number of the obstacles;
s5: judging whether the initial node and the termination node meet the specification, namely whether the initial node and the termination node are in an observation range, whether the robot inverse solution at the initial node exists or not and whether the robot at the initial node and the termination node are out of an obstacle repulsive force field range or not, and whether the robot at the initial node and the obstacle collide;
s6: if the node is not in the observation range or in the range of the obstacle repulsive force field or the robot inverse solution does not exist at the node, the robot collides with the obstacle, the step S2 is repeated, otherwise, the step S7 is performed;
s7: searching for a random node q in a random search tree rand Nearest node q nearest ；
S8: determination of new generation node q by target bias and target preference ideas new ；
S9: judging new generation node q new Whether the robot collides with an obstacle or not, and whether the robot at the node has inverse solution or not;
s10: if node q is newly generated new The robot collides with the obstacle, or the robot does not have inverse solution, the node is abandoned, a new node with a low dynamic step length coefficient is saved, a random search tree is added, the step S11 is skipped, and otherwise, the step S11 is skipped directly;
s11: re-selecting and regenerating node q in Near field new The nearest node is the parent node q of the newly generated node parent ；
S12: computing slave initial node q init Father node q parent New generation node q new To other q in Near neighborhood near Is determined from the original initial node q init To node q in Near neighborhood near Whether or not the distance of (a) is shorter than the distance of (b) the path newly generated node q new Longer distances of (2);
s13: if from original initial node q init To node q in Near neighborhood near Is used for generating a node q newly by a distance ratio path new Is longer, then the node is regenerated to node q near Adding the child nodes of (a) to the random search tree, namely re-wiring in the Near neighborhood;
s14: judging new generation node q new With target node q goal Whether the distance of (2) is less than a prescribed distance;
s15: if node q is newly generated new With target node q goal If the distance of (a) is smaller than the prescribed distance, the target node q is determined goal Adding the search result into a random search tree, terminating the search process, otherwise jumping to the step S7, and entering the next search process;
s16: backtracking and extracting all nodes in a TCP point path from the initial node to the termination node;
s17: taking the time interval between two adjacent nodes of the robot as the position dimension of whales in an improved whale optimization algorithm;
s18: calculating a cost function value of a whale optimization algorithm through a penalty function;
s19: judging random number xi 3 < 0.5 is true:
s20: if random number xi 3 If the value less than 0.5 is met, performing contracted circle search, jumping to the step S21, otherwise performing spiral search, and jumping to the step S25;
s21: judgment of determinantWhether or not to establish;
s22: if determinantIf so, jumping to the step S23, otherwise, surrounding the current optimal whale position in the current whale shrink circle searching mode, and jumping to the step S25;
s23: introducing a level system strategy to obtain the average position dimension of the optimal whale, the suboptimal whale and the suboptimal whale in the current population, wherein the current whale surrounds the average position dimension in a contracted circle searching mode;
s24: introducing a greedy strategy idea, wherein the updating position of whales only selects the current cost optimal value;
s25: judging whether the maximum iteration times are reached, if so, jumping to the step S26, otherwise, entering the next iteration search, and jumping to the step S18;
s26: and outputting the obstacle avoidance and time optimal smooth track of the industrial robot.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
the invention establishes a comprehensive industrial robot track optimization model, and inputs limited parameters including X, Y, Z axis range of working environment, robot DH model parameters and initial node q init With target node q goal The limit values of the speed, the acceleration and the jerk of the TCP point of the robot can be used for independently planning the obstacle avoidance and the time optimal smooth track of the robot offline. The method provided by the invention has good reference and application values for engineering machinery automation and industrial robot technology, can limit the impact of the tail end of the robot, prolongs the service life of the robot, improves the working efficiency of the robot, and greatly shortens the preliminary planning and debugging time of engineers.
Drawings
Fig. 1 is a schematic diagram of the RRT reselection optimal parent process of the present invention
Fig. 2 shows the RRT rerouting procedure of the present invention
FIG. 3 is a target bias and target preference extension strategy based on the repulsive field
FIG. 4 is a schematic diagram of a robot collision detection strategy based on geometric fine detection according to the present invention
FIG. 5 shows a position update strategy for a seesaw circle of the seesaw circle surrounding a prey of the invention
FIG. 6 is a time-optimal trajectory planning flowchart of the present invention
FIG. 7 is an X-axis position of a robotic end effector of the present invention
FIG. 8 is an illustration of X-axis velocity of a robotic end effector of the present invention
FIG. 9 is an illustration of X-axis acceleration of a robotic end effector of the present invention
FIG. 10 is an illustration of X-axis jerk of a robotic end effector of the present invention
FIG. 11 is a Y-axis position of a robotic end effector of the present invention
FIG. 12 is a Y-axis speed of a robotic end effector of the present invention
FIG. 13 is a Y-axis acceleration of a robotic end effector of the present invention
FIG. 14 is a Y-axis jerk of a robotic end effector of the present invention
FIG. 15 is a Z-axis position of a robotic end effector of the present invention
FIG. 16 is a graph showing Z-axis velocity of a robotic end effector of the present invention
FIG. 17 is a Z-axis acceleration of a robotic end effector of the present invention
FIG. 18 is a Z-axis jerk of a robotic end effector of the present invention
FIG. 19 is an iterative evolution curve of algorithm cost values according to the present invention
Fig. 20 is a comparison of RRT, RRT and modified RRT algorithms of the present invention
Detailed Description
The invention is further described in detail below with reference to the accompanying drawings, and the impact constraint robot obstacle avoidance and time optimal track planning method comprises four parts, namely a path planning module, a spline interpolation module, an obstacle avoidance processing module and a time optimal track planning module.
The path planning module introduces the target bias and target bias expansion concept to solve the problem of large operand when the RRT algorithm expands nodes, and newly generated node q new The method has strong randomness, although the method can be gradually optimized, the blind expansion strategy causes the waste of calculation resources and greatly influences the convergence rate. The target bias expansion idea is to add a target node q goal Component in direction, new node q new Q direction goal and qrand Is directed towards the target node q with minimal probability goal The direction expands to more closely approximate the ideal path. Target bias policy and probability of selection p of target bias policy t The random tree T expansion direction is guided, and the target is expanded at a higher speed while the randomness of the expansion tree is reserved.
Wherein eta is the initial step length of RRT growth,is a neighbor node q near To random node q rand Vector of->Is a neighbor node q near To target node q goal Dynamic adjustment coefficient of RRT is adopted as ρ, ensuring new node q new Approach to target node q in direction and step length goal 。
Q specified in repulsive field new The point cannot be added into the random tree T, d is the minimum distance between the current node and the target node, and if the minimum distance is satisfied new -q goal The < d constraint, then an attempt is made to approach the target node.
wherein ,the repulsive force field coefficient is positively correlated with the environmental scale. ρ is at R c Shortest distance between target node and obstacle in space, ρ 0 The value of the coefficient is related to the number of the barriers for the maximum influence range of the barriers.
When the repulsive force field action range covers the initial node q init The repulsive force field is narrowed until the initial node q init Outside the repulsive force field, when the repulsive force field covers the target node q goal And clearing the repulsive force field range.
The state space refers to the set of all possible states in a system, defined as X ε R d ，R d Represents d-dimensional space, d.epsilon.N, and typically has d.gtoreq.2, X free Representing a collision-free configuration space. Let the directed graph g= (V, E) of the extended random tree T consist of a vertex set V and an edge set E, V being X free E is a V x V subset, directed on GThe path is a sequence of vertices (v 1 ,v 2 ,…,v n ) Sum edge set (v) i ,v i+1 ) E, wherein i is greater than or equal to 1 and less than or equal to n-1.
The Cartesian space planning can intuitively display the robot planning process, an environment information model of the Cartesian space needs to be established in the early stage of motion planning, 20 multiplied by 20 faces are used for representing envelope balls of obstacles, and a set formed by the faces is zeta k Representation, i.e.
An unobstructed path in state space X may be defined as a list of forms σ: [0, t ]]Wherein t ε N, for any τε R, there is σ (τ) ε X free And given a starting state of σ (0) =x init And the target state is sigma (t) =q goal 。
Set X free The set of all directed graphs on is represented as wherein ,/>Vertex X ε X free And the sequence number n.epsilon.N, the Near function is defined as +.>Returning a node set V' close to x, where->(G, x, n) is defined as the set of all vertices of a closed hypersphere of radius r centered on x.
Wherein gamma is a radius constant, ζ d Representing the volume of a unit sphere in d-dimensional euclidean space.
At newly generated node q new Nearby toSearching for neighboring nodes q within a defined radius r near As an alternative q new Alternatives to the parent node. Return distance q by Near function new Node set X on random tree T not exceeding radius r near . Then at X near Find q new I.e. sampling point q for all circular regions near ∈X near Sequentially comparing q init To q new Q of minimum cost near Let it be q new Is the parent node q of (2) parent And q is as follows new RRT reselection parent node addition to the random tree T is shown in fig. 1.
At q new Reselection of parent node q parent And then, rewiring the random tree T to further reduce the cost of connection among the nodes of the random tree T as much as possible. For X near Except for parent node q parent All q besides near Rewiring, i.e. for all q near ∈X near -q parent If q init To q new To q near Distance is less than q init To q parent To q near Is cut off q near Connection to the original parent node will (q new ,q near ) The original path is changed as a new node relationship. In order to facilitate the collection of information of spaces and obstacles when using expansion nodes, target bias and target preference strategies are adopted, and the information collected in the exploration process is adapted to the expansion of the tree.
The spline interpolation module is a functional module for carrying out interpolation smoothing on some path key nodes which are close to the obstacle and are generated by the path planning module. On the premise of taking obstacle avoidance into consideration, in order to ensure the smoothness, speed and acceleration continuity of the actual running track of the robot, a quintic spline interpolation function is adopted to carry out path smoothing treatment.
For S (x) ∈S 5 (pi) given interval [ a, b]The upper n spline nodes a=x 1 ＜x 1 ＜…＜x n In d-dimensional state space X, a collision-free path may be defined as a list of forms σ: [0, t]For any τ ε R, there is σ(τ) ∈X, therefore σ (τ) is in dimension X 1 ,X 2 ,…,X d The lower interpolation can obtain d-dimensional vector X uniquely determined by system state τ 。
Introduction of a marking M i ＝S (4) (x i )，A i ＝S″(x i ) I epsilon {1,2, …, n }, record h i ＝x i+1 -x i ，α i ＝x-x i I.e {1,2, …, n-1} is due to s (4) (x) At [ x ] i ,x i+1 ]The upper is a linear function, so
Integrate equation (4) twice and substitute s "(x i )＝A i ，s″(x i+1 )＝A i+1 In interval [ x i ,x i+1 ]On is provided with
Integrate equation (5) twice and substitute s (x i )＝y i ，s(x i+1 )＝y i+1 In interval [ x i ,x i+1 ]On is provided with
The first derivative of x is found on both sides of equation (6)
wherein ,s[xi ,x i+1 ]＝(f(x i+1 )-f(x i ))/(x i+1 -x i ) The two sides of the formula (7) have the second derivative of x
From s' "(x) i +0)＝s″′(x i -0) giving the relation between M and A and letting lambda i ＝h i /6，μ i ＝2(λ i-1 +λ i )，l i ＝-(j i+1 -j i ) Then get
λ i-1 M i-1 +μ i M i +λ i M i+1 +j i-1 A i-1 +l i A i +j i A i+1 ＝0 (9)
From s' (x i +0)＝s′(x i -0) giving the relation between M and A, and lettingq i ＝8(p i-1 +p i )/7，g i ＝f[x i ,x i+1 ]-f[x i ,x i-1 ]Then get
p i-1 M i-1 +q i M i +p i M i+1 +λ i-1 T i-1 +μ i T i +λ i T i+1 ＝g i (10)
Wherein i=1, 2, …, n-1, and the equation (9) and the equation (10) are combined to obtain a system of underdetermined equations including 2n-4 equations with 2n unknowns.
To solve for 2n unknown parameters, 4 fixed boundary conditions are given, resulting in a definite solution to the system of equations.
wherein ,V1 m ， and />Corresponding dimension X of robot end effector in X state space 1 ,X 2 ,…,X d Velocity and acceleration values of m e {1,2, …, d }.
Obtaining M about 2n-2 unknowns 1 ,M 2 ,A 2 ,…,A n-1 ,M n And a linear system of 2n-2 equations.
Wherein, k epsilon {2,3, …, n-3}, each element value of row 2k of matrix C is as follows:
the values of the elements of 2k+1 rows of matrix C are as follows:
the values of the elements of matrix G are as follows:
the other element values of matrix G are as follows:
supplementing 4 fixed boundary conditions V 1 m ， and />Then, solving M by using a fourth-order continuously-conductive conditional construction equation at the end point of the subinterval i ,A i (i∈{0,1,…,n-1})。
The obstacle avoidance processing module is a key link of track planning, and adds a new node q in the random tree T every time the RRT algorithm is improved new Collision detection is required to generate obstacle avoidance paths. Robot geometry modeling is a precondition for collision detection and distance calculation, which are precondition for collision avoidance path planning. Aiming at the three-dimensional characteristics of the obstacle in the actual engineering environment, the robot connecting rod and the end effector are enveloped by the capsule body, and the obstacle is enveloped by the sphere. The method not only enables the robot model and the obstacle model to be simpler, but also greatly reduces the calculated amount.
Setting the safety distance between the robot and the obstacle as d safe And solving the angle value of the joint q through inverse kinematics of the robot, and defaulting to be unreachable if no solution exists. If the robot is at a new node q new Where there are multiple sets of inverse solutions, then pass through parent node q parent To new node q new Cost value of (a).
wherein ,Cpn Is the father node q parent To new node q new Cost of (1), where i.e. [1, s ]]The robot inverse kinematics solution has s groups.
The end point coordinates of each connecting rod and the end effector of the robot are solved through positive kinematics. The robot connecting rod L and the obstacle O are defined as a point set, the collision detection problem of the connecting rod is converted into whether collision occurs between the capsule body and the sphere, and the radius of the sphere is the radius R of the range of the repulsive force field of the obstacle rep And the radius r of the capsule body of the connecting rod s And (3) summing.
R＝R rep +r s (18)
Collision detection of the robot enveloping capsule body and the obstacle sphere is converted into judgment of the position relationship between the line segment and the sphereSphere center O and line segment L MN Shortest distance d min Represented as
Wherein O is the sphere center of the range of the repulsive force field of the obstacle, and M, N is the end point of the robot connecting rod.
Line segment L MN The expression of the included angles of the end point M, N of the (E) and the sphere center O of the sphere is
New node q in Cartesian space new Joint state and parent node q of (2) parent The distance cost between the joint states of (a) is defined as the euclidean distance of their joint space vectors.
The robot envelope column and the range of the obstacle repulsive force field can be approximately seen as the position relation between a line segment and a ball, and the line segment and />Enumerating line segment L MN Relationship with sphere O:
s1: judging whether the initial node and the termination node meet the specification, namely whether the initial node and the termination node are in the observation range, whether the inverse solution of the robot at the initial node and the termination node are in the range of the repulsive force field of the obstacle, and if the initial node and the termination node are not in the observation range, the inverse solution is not in the range of the initial node and the termination node or collide with the obstacleWhen a collision occurs, the initial node and the termination node are rearranged. The inverse solution group number n of the robot is obtained through inverse kinematics of the robot, n is less than or equal to 8, and if n=0, the robot does not have inverse solution at the node, the node is abandoned. Calculating a new node q new Is inverse to the parent node q parent And finding the joint inverse solution nearest to the father node according to the distance cost of each joint angle value. By positive kinematics to find the coordinates of the robot base origin, the robot joint origin and the end effector origin, the robot link is approximately considered as a line segment L MN The line segment end points are positive motions to obtain the original points.
S2: judging line segment L MN Shortest distance d from sphere center of sphere min A magnitude relation to the sum of the repulsive force field range radius and the safe distance,representing line segment->Shortest distance to the center of sphere, if +.>The robot does not collide with the obstacle; otherwise, S2 and S3 of collision detection are performed.
S3: judging line segment L MN Whether the end point M, N of (c) is within the sphere,representing line segment->Endpoint N of (2) 2 Distance from the centre of sphere O of the sphere, +.>Representing line segment->Endpoint M of (2) 2 Distance from the centre of sphere O of the sphere, if segment +.>Either end point is inside the sphere, < >>The robot collides. If line segment->Both end points are in the sphere,the robot would collide. If line segment->And S3, where neither end point is within the sphere, performing collision detection.
S4: line segment L MN Judging the line segment L under the condition that both end points are out of sphere range MN Whether or not to pass through the sphere, if the segmentIs provided with two end points M 3 、N 3 Angle OM between the ball and the center O of the ball 3 N 3 And +. 3 M 3 Acute angles are adopted, the angle OMN is less than or equal to pi/2 n, the angle ONM is less than or equal to pi/2, and then the robot collides with the obstacle. If line segment->Is provided with two end points M 4 、N 4 Angle OM between the ball and the center O of the ball 4 N 4 And +. 4 M 4 When an obtuse angle exists, the angle OMN is more than pi/2U & lt ONM is more than pi/2, and the robot does not collide with the obstacle.
The time optimization track planning module sets the start-stop speed and the start-stop acceleration of the end effector of the robot and limits the speed, the acceleration and the jerk of the robot. The phenomena of impact damage to the end effector and the transmission mechanism, the motor and the speed reducer at the joints of the robot at the end of the robot should be more preferentially considered, and the optimal robot time track planning based on the robot motion constraint penalty function is described as follows:
wherein ,ti Is the time interval between two nodes, n is the number of nodes, k j Is a penalty function that associates a penalty with a violation of a constraint, c j Is a scalar function that involves the implementation of constraints imposed by the mechanical limits of the robot, L representing the total number of robot motion constraints.
The motion constraints of the robot are as follows:
where sup (V) is the maximum speed of the robot end effector, sup (A) is the maximum acceleration of the robot end effector, and sup (J) is the maximum impact of the robot end effector.
The whale optimization algorithm is a random group intelligent optimization algorithm constructed by simulating search foraging behaviors of seat head whale contraction surrounding and spiral updating positions. The whale position information of the invention is the time interval between nodes, and the coefficient vector A i And C i The results are given by formulas (25) to (27).
a＝2-2t/t max (25)
A i ＝2a·ξ 1 -a (26)
C i ＝2·ξ 2 (27)
wherein ,ξ1 ，ξ 2 Is interval [0,1 ]]In (a) decreases linearly from 2 to 0, t in an iterative process max Representing the maximum number of iterations.
In the development stage of the whale algorithm, whales were seated to shrink around the prey and a "9" bubble network was located close to the prey. ThenThe location update of (2) can be described by a mathematical model of equation (28):
setting xi 3 Is [0,1 ]]Two position updating strategies of contracting circle surrounding and contracting spiral approaching prey are based on xi 3 The probability of 50% each of the random value distributions of (c) is selected. X is X p (t) represents the current whale population position-optimal individual, X i (t) is the current individual position of whales, A i ||C i X p (t)-X i (t) | is the shrink wrap step size. b is a constant defining a logarithmic spiral shape, l is [ -1,1]To adjust the spiral direction to better search the optimal solution space. Zeta type toy 3 Selecting a shrink wrap prey at < 0.5 such that the algorithm converges to an optimal solution; when xi 3 And selecting a shrink helix to approach the prey when the shrinkage factor is more than or equal to 0.5, and preventing the shrinkage factor from being used only to sink into a locally optimal solution.
The solution obtained in the development stage of the whale optimization algorithm may be a local optimal solution, so that a global optimal solution needs to be found by exploring other feasible solutions, namely the exploration stage of the whale optimization algorithm. The search stage randomly searches for the target to prevent falling into the locally optimal solution, as can be seen from the formula (28), A i ∈[-a,a]When |A i And when the I is more than or equal to 1, forcing the search target to be far away from the optimal solution. Current whale randomly selects other whale (non-current optimal solution) positions X rand For the target position, current whale X i The location update formula of (2) is:
X i (t+1)＝X rand (t)-A i ·||C i X rand (t)-X i (t)|| (29)
the optimization algorithm of whales does not consider the suboptimal positions of other whales in the population, a level system strategy is introduced, and the optimal positions { X ] in the first 3 whale populations are selected 1st ,X 2nd ,X 3rd [ to obtain potential whale species ]The group optimal individual position can obtain a new position updating function:
wherein ,ftot (t)＝f(X 1st (t))+f(X 2nd (t))+f(X 3rd (t))。
The hierarchical system expands the search range of the whale optimization algorithm, but the cost value of the updated position is not necessarily superior to that of the current position in the position updating process of whale individuals. In order to enable the algorithm to converge to the optimal solution, a greedy strategy is introduced to update the positions of individuals with the optimal cost value. The location update function is
Interval [0,1 ]]Random decision probability factor xi 4 Determining whether whale position is updated, only when xi 4 < 0.5 and f tmp ＞f cur Then, the group is searched towards the optimal direction using a greedy strategy.
In order to verify the performance of the impact constrained robot obstacle avoidance and time optimal track planning method, the base coordinates of the robot are [0,0 ]]The robot workstation has an x-axis range of [ -6,6]The y-axis range is [ -6,6]Z value range of [1.5,5.5 ]]. Setting the coordinates of the sphere center of the enveloping sphere of the obstacle to [ -0.1,1.6,2.2]And [0.4,1.5,2.2 ]]The radius is 0.15m, and the safety distance between the obstacle and the robot is set to 0.05m. The attitude of the robot welding gun is a deflection angle of-172.5463 degrees rotating around the x axis, a pitch angle of-35.9320 degrees rotating around the y axis and a roll angle of 77.4537 degrees rotating around the z axis. The initial position coordinate of the welding gun is [0.9,1.5,2.3 ]]The coordinates of the target position are [ -0.5,1.6,2.3]. Default initial velocity V of cubic spline interpolation 1 m And termination speedDefault initial addition of 0m/sSpeed->And terminate acceleration->Is 0m/s 2 The crossover probability of the genetic algorithm is p c =0.1, variation probability p m =0.05. The number of whales and individuals in the genetic algorithm population is 100, and the evolution algebra is 600 generations.
The improved RRT algorithm of the impact constraint robot obstacle avoidance and time optimal track planning method is 15.96% shorter than the RRT algorithm, and compared with the progressive optimal RRT algorithm, the improved RRT algorithm has small-amplitude improvement and great improvement on convergence rate. The improved whale optimization algorithm is superior to the traditional genetic algorithm, the track running time is reduced by 43.65%, and compared with the whale optimization algorithm, the track running time is reduced by 28.49%. The impact of the robot working system is effectively restrained, the robot obstacle avoidance track with optimal time and continuous jerk is obtained, and an effective reference can be provided for robot track planning.
Claims (5)
1. The method is characterized by comprising a path planning module, a spline interpolation module, an obstacle avoidance processing module and a time optimization trajectory planning module, wherein the path planning module introduces target bias and target preference ideas based on the safety constraint of an obstacle repulsive force field on the basis of an RRT algorithm, compresses a search space and quickens the search speed of the shortest path of the industrial robot, the spline interpolation module carries out interpolation smoothing on a path key node by adopting a quintic spline interpolation function, an AT matrix is constructed by taking initial and termination speeds and initial and termination accelerations as fixed boundary conditions, the obstacle avoidance processing module carries out collision avoidance processing on the industrial robot and the obstacle by utilizing a geometric fine detection method, a robot joint and a base TCP point are obtained by utilizing forward and backward kinematics of the robot, the robot connecting rod and a welding gun utilize a capsule envelope, the obstacle utilizes a sphere envelope, and finally, the time optimization trajectory module combines a level and greedy strategy with a traditional whale optimization algorithm, so that the global search capacity of the algorithm is improved.
2. The method for planning the obstacle avoidance and time optimal trajectory of the robot under the impact constraint of claim 1, wherein the path planning module introduces the target bias and target preference expansion ideas to solve the problem of large calculation amount when the RRT algorithm expands the nodes, and the newly generated node q new The method has strong randomness, although the method can gradually optimize, the blind expansion strategy causes the waste of calculation resources, greatly influences the convergence speed, and the target bias expansion idea is that the target node q is added goal Component in direction, new node q new Q direction goal and qrand Is directed towards the target node q with minimal probability goal Directional expansion, thereby closer to ideal path, target bias strategy and selection probability p of target bias strategy t Guiding the expansion direction of the random tree T, and expanding the random tree T to a target at a higher speed while keeping the randomness of the expansion tree;
wherein eta is the initial step length of RRT growth,is a neighbor node q near To random node q rand Vector of->Is a neighbor node q near To target node q goal Dynamic adjustment coefficient of RRT is adopted as ρ, ensuring new node q new Approach to target node q in direction and step length goal；
Q specified in repulsive field new The point cannot be added to the random tree T, d is the minimum of the current node and the target nodeDistance, if meeting the specification q new -q goal The constraint of < d, attempting to approach the target node;
wherein ,for the repulsive force field coefficient, the repulsive force field coefficient is positively correlated with the environmental scale, and ρ is R c Shortest distance between target node and obstacle in space, ρ 0 For the maximum influence range of the obstacle, the coefficient is related to the number of the obstacle, and when the repulsive force field action range covers the initial node q init The repulsive force field is narrowed until the initial node q init Outside the repulsive force field, when the repulsive force field covers the target node q goal And clearing the repulsive force field range.
3. The method for planning the obstacle avoidance and time optimal track of the robot under the impact constraint of claim 1 is characterized in that the spline interpolation module is a functional module for carrying out interpolation smoothing on a plurality of path key nodes close to the obstacle, which are generated by the path planning module;
for S (x) ∈S 5 (pi) given interval [ a, b]The upper n spline nodes a=x 1 ＜x 1 ＜…＜x n In d-dimensional state space X, a collision-free path may be defined as a list of forms σ: [0, t]For any τ ε R, there is σ (τ) ∈X, so σ (τ) is in dimension X 1 ,X 2 ,…,X d The lower interpolation can obtain d-dimensional vector X uniquely determined by system state τ；
Introduction of a marking M i ＝S (4) (x i )，A i ＝S″(x i ) I epsilon {1,2, …, n }, record h i ＝x i+1 -x i ，α i ＝x-x i I.e {1,2, …, n-1} is due to s (4) (x) At [ x ] i ,x i+1 ]The upper is a linear function, so
Integrate equation (3) twice and substitute s "(x i )＝A i ，s″(x i+1 )＝A i+1 In interval [ x i ,x i+1 ]On is provided with
Integrate equation (4) twice and substitute s (x i )＝y i ，s(x i+1 )＝y i+1 In interval [ x i ,x i+1 ]On is provided with
The first derivative of x is found on both sides of equation (5)
wherein ,s[xi ,x i+1 ]＝(f(x i+1 )-f(x i ))/(x i+1 -x i ) The two sides of the formula (6) have the second derivative of x
From s' "(x) i +0)＝s″′(x i -0) giving the relation between M and A and letting lambda i ＝h i /6，μ i ＝2(λ i-1 +λ i )，l i ＝-(j i+1 -j i ) Then get
λ i-1 M i-1 +μ i M i +λ i M i+1 +j i-1 A i-1 +l i A i +j i A i+1 ＝0 (8)
From s' (x i +0)＝s′(x i -0) giving the relation between M and A, and lettingq i ＝8(p i-1 +p i )/7，g i ＝f[x i ,x i+1 ]-f[x i ,x i-1 ]Then get
p i-1 M i-1 +q i M i +p i M i+1 +λ i-1 T i-1 +μ i T i +λ i T i+1 ＝g i (9)
Wherein i=1, 2, …, n-1, after the equation (8) and the equation (9) are combined, a system of underequations containing 2n unknown quantities 2n-4 equations is obtained, and in order to calculate 2n unknown parameters, 4 fixed boundary conditions are given, so that a determination solution of the system of equations is obtained;
wherein ,V1 m ， and />Corresponding dimension X of robot end effector in X state space 1 ,X 2 ,…,X d Velocity and acceleration values of M e {1,2, …, d }, to obtain M for 2n-2 unknowns 1 ,M 2 ,A 2 ,…,A n-1 ,M n And 2n-2 equationsA group;
wherein, k epsilon {2,3, …, n-3}, each element value of row 2k of matrix C is as follows:
the values of the elements of 2k+1 rows of matrix C are as follows:
the values of the elements of matrix G are as follows:
the other element values of matrix G are as follows:
supplementing 4 fixed boundary conditions V 1 m ， and />Then, solving M by using a fourth-order continuously-conductive conditional construction equation at the end point of the subinterval i ,A i (i∈{0,1,…,n-1})。
4. The method for planning obstacle avoidance and time optimal trajectory of impact-constrained robot according to claim 1, wherein the obstacle avoidance processing module sets a safe distance between the robot and the obstacle as d safe Solving the angle value of the joint q through inverse kinematics of the robot, defaulting to be unreachable if no solution exists, and if the robot is at a new node q new Where there are multiple sets of inverse solutions, then pass through parent node q parent To new node q new Cost value of (2);
wherein ,Cpn Is the father node q parent To new node q new Cost of (1), where i.e. [1, s ]]The inverse kinematics solution of the robot is provided with s groups, the end point coordinates of each connecting rod and the end effector of the robot are solved through the forward kinematics, the connecting rod L of the robot and the obstacle O are defined as a point set, the collision detection problem of the connecting rod is converted into whether collision occurs between the capsule body and the sphere, and the radius of the sphere is the radius R of the field range of the repulsive force of the obstacle rep And the radius r of the capsule body of the connecting rod s And (3) summing;
R＝R rep +r s (17)
the collision detection of the robot enveloping capsule body and the obstacle sphere is converted into judging the position relationship between the line segment and the sphere, and the sphere center O and the line segment L MN Shortest distance d min Represented as
Wherein O is the sphere center of the range of the repulsive force field of the obstacle, M, N is the end point of the connecting rod of the robot, and line segment L MN The expression of the included angles of the end point M, N of the (E) and the sphere center O of the sphere is
New node q in Cartesian space new Joint state and parent node q of (2) parent The distance cost between the joint states of (a) is defined as the Euclidean distance of their joint space vectors;
the robot envelope column and the range of the obstacle repulsive force field can be approximately seen as the position relation between a line segment and a ball, and the line segment and />Enumerating line segment L MN Relationship with sphere O:
s1: judging whether the initial node and the termination node meet the specification, namely whether the initial node and the termination node are in an observation range, whether a robot inverse solution exists at the initial node and the termination node are in an obstacle repulsive force field range, if the initial node and the termination node are not in the observation range, the inverse solution does not exist or collide with an obstacle, rearranging the initial node and the termination node, obtaining a robot inverse solution group number n through a robot inverse kinematics, n is less than or equal to 8, if n is less than 0, discarding the node if the robot does not have an inverse solution at the node, and calculating a new node q new Is inverse to the parent node q parent Finding the inverse solution of the joint nearest to the father node at the distance cost of each joint angle value, obtaining the coordinates of the origin of the robot base, the origin of the robot joint and the origin of the end effector through forward kinematics, and enabling the robot connecting rod to be approximately regarded as a line segment L MN The line segment end points are positive motions to obtain the original points;
s2: judging line segment L MN Shortest distance d from sphere center of sphere min A magnitude relation to the sum of the repulsive force field range radius and the safe distance,representing line segment->Shortest distance to the center of sphere, if +.>The robot does not collide with the obstacle, otherwise, S2 and S3 of collision detection are executed;
s3: judging line segment L MN Whether the end point M, N of (c) is within the sphere,representing line segment->Endpoint N of (2) 2 Distance from the centre of sphere O of the sphere, +.>Representing line segment->Endpoint M of (2) 2 Distance from the centre of sphere O of the sphere, if segment +.>Either end point is within the sphere of the ball,the robot collides, if the line segment +.>Both end points are in the sphere,the robot will collide if the line segment +.>S3, if both end points are not in the sphere, performing collision detection;
s4: line segment L MN Both ends being outside the sphereIn the case of judging line segment L MN Whether or not to pass through the sphere, if the segmentIs provided with two end points M 3 、N 3 Angle OM between the ball and the center O of the ball 3 N 3 And +. 3 M 3 Are acute angles, the angle OMN is less than or equal to pi/2 n, collision occurs between the robot and the obstacle, if the line section is less than or equal to pi/2 n>Is provided with two end points M 4 、N 4 Angle OM between the ball and the center O of the ball 4 N 4 And +. 4 M 4 When an obtuse angle exists, the angle OMN is more than pi/2U & lt ONM is more than pi/2, and the robot does not collide with the obstacle.
5. The method for planning the obstacle avoidance and time optimal trajectory of the robot under the impact constraint of claim 1, wherein the time optimal trajectory planning module sets the initial speed, the initial acceleration, the final speed and the final acceleration of the end effector of the robot, limits the speed, the acceleration and the jerk of the robot at the same time, and the phenomena that the end effector and the transmission mechanism, the motor and the speed reducer at the joints of the robot are damaged by the impact of the end of the robot are more prioritized, and the time optimal trajectory planning of the robot based on the motion constraint penalty function of the robot is described as follows:
wherein ,ti Is the time interval between two nodes, n is the number of nodes, k j Is a penalty function that associates a penalty with a violation of a constraint, c j Is a scalar function, which relates to the implementation of constraints imposed by the mechanical limits of the robot, L represents the total number of robot motion constraints, which are as follows:
where sup (V) is the maximum speed of the robot end effector, sup (A) is the maximum acceleration of the robot end effector, and sup (J) is the maximum impact of the robot end effector;
the whale optimizing algorithm is a random group intelligent optimizing algorithm constructed by simulating search foraging behaviors of seat head whale contraction surrounding and spiral updating positions, and the whale position information is the time interval among nodes and the coefficient vector A i And C i Derived from formulas (24) to (26);
a＝2-2t/t max (24)
A i ＝2a·ξ 1 -a (25)
C i ＝2·ξ 2 (26)
wherein ,ξ1 ，ξ 2 Is interval [0,1 ]]In (a) decreases linearly from 2 to 0, t in an iterative process max Representing the maximum number of iterations, at the stage of development of the whale algorithm, the whale sits with a constriction surrounding the prey and a network of "9" bubbles approaching the preyIs described as a location update mathematical model of (a)
Setting xi 3 Is [0,1 ]]Two position updating strategies of contracting circle surrounding and contracting spiral approaching prey are based on xi 3 The probability of 50% each of the random value distributions of (a) is selected, X p (t) represents the current whale population position-optimal individual, X i (t) is the current individual position of whales, A i ||C i X p (t)-X i (t) is the contraction bounding step, b is a constant defining a logarithmic spiral shape, and l is [ -1,1]Random number in (a) and adjusting the screwThe optimal solution space, xi, is better searched in the direction of the spiral line 3 Selecting a contraction bounding prey at < 0.5 so that the algorithm converges to an optimal solution when ζ 3 Selecting a shrink spiral to approach the prey when the shrinkage is more than or equal to 0.5, and preventing the shrinkage from being used only to surround and sink into a local optimal solution;
the solution obtained in the development stage of the whale optimization algorithm is a local optimal solution, so that the global optimal solution is also required to be searched by searching other feasible solutions, namely the exploration stage of the whale optimization algorithm is the exploration stage, the random search target of the exploration stage prevents the whale optimization algorithm from falling into the local optimal solution, and the formula (27) shows that A i ∈[-a,a]When |A i When the I is not less than 1, forcing the search target to be far away from the optimal solution, and randomly selecting other whales (non-current optimal solution) from the current whales to obtain the position X rand For the target position, current whale X i The location update formula of (2) is:
X i (t+1)＝X rand (t)-A i ·||C i X rand (t)-X i (t)|| (28)
the optimization algorithm of whales does not consider the suboptimal positions of other whales in the population, a level system strategy is introduced, and the optimal positions { X ] in the first 3 whale populations are selected 1st ,X 2nd ,X 3rd To obtain the optimal individual location of the potential whale population, a new location update function may be obtained:
wherein ,ftot (t)＝f(X 1st (t))+f(X 2nd (t))+f(X 3rd (t)) the hierarchical system expands the search range of the whale optimization algorithm, but the cost value of the update position is not necessarily better than the cost value of the current position in the position update process of whale individuals, in order to enable the algorithm to converge to an optimal solution, a greedy strategy is introduced to update the position of the individuals with the cost optimal value, and the position update function is that
Interval [0,1 ]]Random decision probability factor xi 4 Determining whether whale position is updated, only when xi 4 < 0.5 and f tmp ＞f cur Then, the group is searched towards the optimal direction using a greedy strategy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111410601.6A CN113885535B (en) | 2021-11-25 | 2021-11-25 | Impact constraint robot obstacle avoidance and time optimal track planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111410601.6A CN113885535B (en) | 2021-11-25 | 2021-11-25 | Impact constraint robot obstacle avoidance and time optimal track planning method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113885535A CN113885535A (en) | 2022-01-04 |
| CN113885535B true CN113885535B (en) | 2023-09-12 |
Family
ID=79015605
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111410601.6A Active CN113885535B (en) | 2021-11-25 | 2021-11-25 | Impact constraint robot obstacle avoidance and time optimal track planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113885535B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114712151B (en) * | 2022-03-01 | 2023-09-19 | 南京伟思医疗科技股份有限公司 | Virtual obstacle avoidance method and system for upper limb rehabilitation robot |
| CN115002788B (en) * | 2022-03-30 | 2024-04-09 | 西安电子科技大学 | Directional sensor network coverage optimization method for road health detection |
| CN115570566A (en) * | 2022-09-28 | 2023-01-06 | 长春工业大学 | Robot obstacle avoidance path planning method for improving APF-RRT algorithm |
| CN115302520B (en) * | 2022-10-12 | 2023-01-03 | 深圳市智绘科技有限公司 | Robot path optimization method and device and electronic equipment |
| CN116009421A (en) * | 2022-12-29 | 2023-04-25 | 中电普信(北京)科技发展有限公司 | Universal simulation method for full-freedom fixed-wing aircraft |
| CN116909293B (en) * | 2023-09-13 | 2023-12-12 | 宁德思客琦智能装备有限公司 | Robot path planning method and device, electronic equipment and computer readable medium |
| CN116989797B (en) * | 2023-09-26 | 2023-12-15 | 北京理工大学 | Unmanned aerial vehicle track optimization method and device, electronic equipment and storage medium |
| CN117340890A (en) * | 2023-11-22 | 2024-01-05 | 北京交通大学 | Robot motion trail control method |
| CN117387634B (en) * | 2023-12-13 | 2024-02-27 | 江西啄木蜂科技有限公司 | Color-changing wood forest zone unmanned aerial vehicle path multi-target planning method based on user preference |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106695802A (en) * | 2017-03-19 | 2017-05-24 | 北京工业大学 | Improved RRT<*> obstacle avoidance motion planning method based on multi-degree-of-freedom mechanical arm |
| CN110228069A (en) * | 2019-07-17 | 2019-09-13 | 东北大学 | A kind of online avoidance motion planning method of mechanical arm |
| CN110962130A (en) * | 2019-12-24 | 2020-04-07 | 中国人民解放军海军工程大学 | Heuristic RRT mechanical arm motion planning method based on target deviation optimization |
| WO2021065196A1 (en) * | 2019-10-03 | 2021-04-08 | Mitsubishi Electric Corporation | Method and system for trajectory optimization for nonlinear robotic systems with geometric constraints |
| CN112904869A (en) * | 2021-01-29 | 2021-06-04 | 华中科技大学 | Unmanned ship weighted iteration path planning method and device based on multi-tree RRT |
| CN112987799A (en) * | 2021-04-16 | 2021-06-18 | 电子科技大学 | Unmanned aerial vehicle path planning method based on improved RRT algorithm |
| CN113084811A (en) * | 2021-04-12 | 2021-07-09 | 贵州大学 | Mechanical arm path planning method |
| CN113103236A (en) * | 2021-04-22 | 2021-07-13 | 山东大学 | Rapid and gradual optimal mechanical arm obstacle avoidance path planning method |
| CN113325799A (en) * | 2021-02-08 | 2021-08-31 | 长春工业大学 | Spot welding robot operation space smooth path planning method for curved surface workpiece |
-
2021
- 2021-11-25 CN CN202111410601.6A patent/CN113885535B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106695802A (en) * | 2017-03-19 | 2017-05-24 | 北京工业大学 | Improved RRT<*> obstacle avoidance motion planning method based on multi-degree-of-freedom mechanical arm |
| CN110228069A (en) * | 2019-07-17 | 2019-09-13 | 东北大学 | A kind of online avoidance motion planning method of mechanical arm |
| WO2021065196A1 (en) * | 2019-10-03 | 2021-04-08 | Mitsubishi Electric Corporation | Method and system for trajectory optimization for nonlinear robotic systems with geometric constraints |
| CN110962130A (en) * | 2019-12-24 | 2020-04-07 | 中国人民解放军海军工程大学 | Heuristic RRT mechanical arm motion planning method based on target deviation optimization |
| CN112904869A (en) * | 2021-01-29 | 2021-06-04 | 华中科技大学 | Unmanned ship weighted iteration path planning method and device based on multi-tree RRT |
| CN113325799A (en) * | 2021-02-08 | 2021-08-31 | 长春工业大学 | Spot welding robot operation space smooth path planning method for curved surface workpiece |
| CN113084811A (en) * | 2021-04-12 | 2021-07-09 | 贵州大学 | Mechanical arm path planning method |
| CN112987799A (en) * | 2021-04-16 | 2021-06-18 | 电子科技大学 | Unmanned aerial vehicle path planning method based on improved RRT algorithm |
| CN113103236A (en) * | 2021-04-22 | 2021-07-13 | 山东大学 | Rapid and gradual optimal mechanical arm obstacle avoidance path planning method |
Non-Patent Citations (1)
| Title |
|---|
| 基于PSO算法的双臂机器人时间最优轨迹规划;占涛;杨光友;中国农机化学报(第006期);82-88，108 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113885535A (en) | 2022-01-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113885535B (en) | Impact constraint robot obstacle avoidance and time optimal track planning method | |
| CN111752306B (en) | Unmanned aerial vehicle route planning method based on fast-expanding random tree | |
| CN112462803B (en) | Unmanned aerial vehicle path planning method based on improved NSGA-II | |
| Abu-Dakka et al. | A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments | |
| Liu et al. | Online time-optimal trajectory planning for robotic manipulators using adaptive elite genetic algorithm with singularity avoidance | |
| CN111610786A (en) | Mobile robot path planning method based on improved RRT algorithm | |
| CN112809665B (en) | Mechanical arm motion planning method based on improved RRT algorithm | |
| CN112692826A (en) | Industrial robot track optimization method based on improved genetic algorithm | |
| CN113435025B (en) | Robot high-performance track automatic generation method combined with multistage optimization model | |
| Jiang et al. | Path planning for robotic manipulator in complex multi-obstacle environment based on improved_RRT | |
| CN110543727A (en) | Improved particle swarm algorithm-based omnidirectional mobile intelligent wheelchair robot parameter identification method | |
| CN112828889A (en) | Six-axis cooperative mechanical arm path planning method and system | |
| CN114545921B (en) | Unmanned vehicle path planning algorithm based on improved RRT algorithm | |
| CN116117822A (en) | RRT mechanical arm track planning method based on non-obstacle space probability potential field sampling | |
| Tang et al. | An improved PSO for path planning of mobile robots and its parameters discussion | |
| Liu et al. | Robotic arm trajectory optimization based on multiverse algorithm | |
| Sun et al. | Co-optimization of Morphology and Behavior of Modular Robots via Hierarchical Deep Reinforcement Learning. | |
| Chen et al. | Path planning of redundant series manipulators based on improved RRT algorithms | |
| CN115056222A (en) | Mechanical arm path planning method based on improved RRT algorithm | |
| Song et al. | A TC-RRT-based path planning algorithm for the nonholonomic mobile robots | |
| CN113011516A (en) | Three-dimensional mesh model classification method and device based on graph topology and storage medium | |
| CN117047751A (en) | Method and system for planning path of bi-directional expansion mechanical arm under pose constraint | |
| Prakash et al. | Path planning of UGV using sampling-based method and PSO in 2D map configuration: a comparative analysis | |
| Duan et al. | Research on welding robot path planning based on genetic algorithm | |
| Chung et al. | Using ALC-PSO algorithm with particle growing method path planning in dynamic environments |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |