CN110125943B - Obstacle avoidance path planning method for multi-degree-of-freedom mechanical arm - Google Patents
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Abstract
The invention discloses a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method, which comprises the following steps: obtaining an initial path by adopting a mechanical arm path planning method; acquiring real-time information of each joint in an initial path at each preset moment; to the beginningPerforming mathematical modeling and mapping on real-time information in the initial path to obtain QecAt QecRandomly selecting q paths, calculating the feasibility and the fitness according to preset constraint conditions, and marking the path with the maximum fitness as a qualified path; repeating until the number of qualified paths reaches G, and recording G qualified paths as a second generation path set Q'ec(ii) a Set Q 'to second generation path'ecPerforming crossing, mutation and fusion; obtaining a third generation path set QecThe obtained third generation path set Q ″)ecAs QecRepeating until the iteration number is D; and taking the path with the maximum fitness obtained in the last iteration as a final target path, and further optimizing the initial path by using the method to obtain a more efficient mechanical arm running path.
Description
Technical Field
The invention relates to a mechanical arm obstacle avoidance path planning and optimizing method, in particular to a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method.
Background
In the industrial robot field, it has important research significance to ensure the safety and the rationality of the motion trail of the mechanical arm, and the existing mechanical arm path planning method comprises the following steps: a, D, an artificial potential field method, a particle swarm algorithm, an ant colony algorithm and the like, for example, an industrial mechanical arm collision-free path planning method based on the A star algorithm is disclosed in the patent CN107953334A, a collision detection algorithm based on a separation axis is adopted to judge whether the mechanical arm collides with the environment in each step of searching, and searching is carried out in a six-dimensional joint space of the mechanical arm according to the principle that the sum of the rotation angles of the joints is minimum and no collision occurs to obtain a collision-free path;
an improved D-x mechanical arm dynamic obstacle avoidance path planning method is disclosed in patent CN106166750A, and a path search algorithm based on an heuristic function is obtained according to the characteristics of a space mechanical arm; improving the path planning of obstacle avoidance applied to the seven-degree-of-freedom mechanical arm D to complete the dynamic obstacle avoidance path planning of the seven-degree-of-freedom mechanical arm;
patent CN104029203A discloses a path planning method for realizing obstacle avoidance of a space manipulator, which finds an obstacle avoidance path at the end of the manipulator by establishing a window-type obstacle and using an artificial potential field method;
these methods exist: the modeling and solving needs long time, and the scene change needs modeling again; the method is based on space position consideration, and does not consider the conditions of joint speed, acceleration, moment and the like when the mechanical arm executes a path.
Disclosure of Invention
Therefore, the invention provides a planning method for an obstacle avoidance path of a multi-degree-of-freedom mechanical arm, which is used for further optimizing an initial path by setting an optimization index (a driving system performance index) of mechanical arm operation to obtain a more efficient mechanical arm operation path.
A multi-degree-of-freedom mechanical arm obstacle avoidance path planning method comprises the following steps:
s1, planning a path of the mechanical arm by adopting a mechanical arm path planning method to obtain an initial path, wherein the number of joints of the mechanical arm is m;
s2, the mechanical arm moves along an initial path, and real-time information of each joint at each preset moment is collected;
the real-time information includes: current moment of motion tijJoint space information ZijVelocity vijAcceleration aijAnd driving torque information tauijI is 1,2,3 … … m; j denotes the current time, j is 1,2,3 … … n; n is the total number of the preset time; the joint space information ZijAngle information of the current joint;
s3, performing mathematical modeling and mapping on the real-time information in the initial path to obtain a set containing a plurality of pieces of path information, wherein the single piece of path information comprises the speed, the acceleration and the driving moment of each joint at a specific moment, which are obtained through calculation based on preset motion time and the angle of each joint;
s4, recording the set containing multiple pieces of path information as QecE corresponds to the fourth path and c corresponds to the fourth iteration; e is 1,2,3 … … r, r is the total number of paths; c is 1,2,3 … … D, D is a preset iteration total number; qecThe contained information is: time t 'corresponding to ith joint of ith path at jth moment in the c-th iteration'eijcAn angle Z'eijcV 'speed'eijcAnd acceleration a'eijcAnd drive torque information τ'eijc；
At QecRandomly selecting q paths, and calculating according to preset constraint conditionsIt corresponds to QecWhen the feasibility degree does not meet a preset feasibility degree threshold value, the current path is rejected; traversing q paths, and recording a set formed by all the remaining paths as q'; calculating the fitness of each path in q', and marking the path with the maximum fitness as a qualified path;
will QecTaking the set of the rejected medium qualified paths as a new QecScreening qualified paths by adopting the same method until the number of the qualified paths reaches G, and marking a set formed by the G qualified paths as a second generation path set Q'ec(ii) a Wherein G is 10 to 40 percent r;
s5, set Q 'to the second generation path'ecRandomly carrying out cross treatment to obtain a hybridized path, and carrying out mutation treatment on the hybridized path; at the same time, fusing mutated and non-mutated paths; to obtain a third generation path set Q'ecThe number of the paths is 90-110% r;
preferably, the crossover processing adopts a simulated binary crossover method, and the variation processing adopts a polynomial variation method.
S6, collecting Q 'of the obtained third generation paths'ecAs Q in step S4ecScreening Q paths, and acquiring a second generation path set Q 'by using the same method'ecAnd obtaining a third generation path set Q by using the method of the step S5 "ecRepeating the step until the iteration number is D; the third generation path set Q obtained in the last iteration "ecAnd taking the path with the maximum fitness as a final target path.
Preferably, D is 15 to 30.
And further comprising S7, moving the mechanical arm along the target path, performing collision detection, resetting the reachable working space of the robot under the constraint condition of S4 if the mechanical arm collides with the environment, and performing the step S4 again.
Further, the model mapping adopts Logistic mapping or McDill-amateis model;
further, when the Logistic mapping is adopted, calculating an input parameter of the Logistic mapping: chaos variable betaj+1Path population space fluctuation range delta tj；
βj+1＝μβj(1-βj)
Manually setting: initial chaotic variable beta1Taking values: beta is not less than 01Less than or equal to 1, chaotic coefficient mu, value: mu is more than or equal to 0 and less than or equal to 4; beta is ajIs a chaotic variable, beta, of the current time on the initial pathj+1Chaotic variables of the initial path at the next moment;
Further, in step S4, q is [ 10% to 30% r ═ q]Wherein r is c ═ 1, QecTotal number of paths of (1);
degree of feasibility
Is calculated as follows:
x’eijcis a parameter in real-time information, min {0, g (x'eijc) Denotes: when inputting parameter xeijcWhen the constraint condition is met, 0 is output, and x 'is output otherwise'eijcAnd a preset upper limit value x 'in the constraint condition'eijc-maxA difference of (d);
hk(Z’eijc) Watch (A)The following steps: when the parameter Z 'is input'eijcSatisfy Z'eijc∈Z’eijc-maxWhen it is output, 0, otherwise, infinity, Z'eijc-maxRepresenting the upper limit of the angle which can be reached by each joint, which is the intrinsic parameter of the mechanical arm;
when the difference value is less than 0.2-0.5 x'eijc-maxAnd h isk(Z’eijc) When equal to 0, degree of feasibility
And meeting a preset feasibility threshold.
Further, the constraint conditions are as follows: according to the model of the robot and the operation requirement, the upper limit value of the real-time information is manually set: v'eijc-max＝1.2～1.5vij、a′eijc-max＝1.2～1.5aij、τ′eijc-max＝1.2～1.5τij。
Namely: joint moment: tau'eijc≤τ′eijc-maxAnd joint acceleration: a'eijc≤a′eijc-maxJoint jerk: a ″)eijc≤a″eijc-maxAnd joint velocity: v'eijc≤v′eijc-max。
Further, the fitness f of step S4 is calculated as follows:
Spijcfor optimizing the indexes, calculating according to the real-time information of the path population q', wherein p is 1,2,3 … … L, and L is the number of the optimizing indexes: k is a radical ofpWeight, k, for each optimization index set manuallyp0 means that the index does not participate in the optimization.
Preferably, L ═ 6, and the optimization index includes:
index of exercise time S1ijcAnd the method is used for measuring the motion efficiency of the robot:
average acceleration index S of joint2ijAnd the method is used for measuring the energy consumption of the robot joint:
average fluctuation index S of joint3ijFor the measurement, the smoothness of the trajectory is:
mean moment index S of joint4ij:
Energy index S of joint average consumption5ij:
Moment index S6ijAnd is used for measuring the stability of the actuating mechanism:
wherein T is the total time of the mechanical arm moving along the initial path, A'eijcThe derivative of the acceleration, jerk.
Further, the preset time is set in the following manner: setting a time interval t, and acquiring real-time information of the current moment at each time interval t from the initial position of the initial path;
preferably, t is 0.5ms to 4 ms;
further, in step S1, acquiring an environment model of a robot motion space by using a depth camera, simplifying the environment model by using a mixed level bounding box method, and performing path planning on a mechanical arm by using a mechanical arm path planning method to obtain an initial path;
the mechanical arm path planning method comprises the following steps: a, D, RRT, PRM, artificial potential field method, genetic algorithm, particle swarm algorithm or ant colony algorithm.
The method of the invention is used for optimizing the initial path gauge obtained by the existing path planning method, and the result is as follows:
the above table shows that the optimization of the scheme of the invention shortens the path length of the mechanical arm, reduces the operation time and plans a more optimized path.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solution of the present invention will be described in detail with reference to the specific embodiments.
A multi-degree-of-freedom mechanical arm obstacle avoidance path planning method comprises the following steps:
s1, acquiring an environment model of a robot motion space by using a depth camera, simplifying the environment model by using an OBB bounding box method, and planning a path of the mechanical arm by using PRM to obtain an initial path;
recording the number of joints of the mechanical arm as m;
s2, enabling the mechanical arm to move along an initial path, and collecting real-time information of each joint at each preset moment;
the setting mode of the preset time is as follows: setting a time interval of 0.1ms, and acquiring real-time information of the current moment once every 0.1ms from the initial position of the initial path;
the real-time information includes: current moment of motion tijJoint space information ZijVelocity vijAcceleration aijAnd driving torque information tauijI is 1,2,3 … … m; j denotes the current time, j is 1,2,3 … … n; n is the total number of the preset time; joint space information ZijAngle information of the current joint;
s3, performing mathematical modeling on the real-time information in the initial path, and calculating an input parameter of a Logistic mapping by adopting the Logistic mapping: chaos variable betaj+1Path population space fluctuation range delta tj；
βj+1＝μβj(1-βj)
Manually setting: initial chaotic variable beta11, 4 is the chaos coefficient mu; beta is ajIs a chaotic variable, beta, at the current moment on the initial pathj+1Chaotic variables of the initial path at the next moment;
Obtaining a set containing 500 pieces of path information through model mapping, wherein the single piece of path information comprises the speed, the acceleration and the driving moment of each joint at a specific moment, which are obtained through calculation based on preset motion time and the angle of each joint;
s4, recording the set containing multiple pieces of path information as QecE corresponds to the fourth path and c corresponds to the fourth iteration; e-1, 2,3 … … 500; c is 1,2,3 … … 20, 20 is presetThe total number of iterations of (c); qecThe contained information is: time t 'corresponding to ith joint of ith path at jth moment in the c-th iteration'eijcAn angle Z'eijcV 'speed'eijcAnd acceleration a'eijcAnd drive torque information τ'eijc；
At QecIn the method, Q is randomly selected to be 100 paths, and the corresponding Q is calculated according to preset constraint conditionsecDegree of feasibility of
When the feasibility does not meet a preset feasibility threshold, rejecting the current path; traversing q paths, and recording a set formed by all the remaining paths as q'; calculating the fitness of each path in q', and marking the path with the maximum fitness as a qualified path;
will QecTaking the set of the rejected medium qualified paths as a new QecScreening qualified paths by the same method until the number of the qualified paths reaches G-150, and marking a set formed by the 150 qualified paths as a second generation path set Q'ec；
S5, set Q 'to the second generation path'ecRandomly carrying out cross treatment to obtain a hybridized path, and carrying out mutation treatment on the hybridized path; at the same time, fusing mutated and non-mutated paths; to obtain a third generation path set Q'ec480, comprising a number of paths;
preferably, the crossover processing is performed by an analog binary crossover method, and the variation processing is performed by a polynomial variation method.
S6, collecting the obtained third generation path Q'ecAs Q in step S4ecAnd screening Q ═ 100 paths, and acquiring a second generation path set Q'ecAnd obtaining a third generation path set Q by using the method of the step S5 "ecRepeating the step until the iteration number is 20; the third generation path set Q obtained in the last iteration "ecAnd taking the path with the maximum fitness as a final target path.
And S7, the mechanical arm moves along the target path to perform collision detection, if the mechanical arm collides with the environment, the reachable working space of the robot under the constraint condition of S4 is reset, and the step S4 is performed again.
Wherein the feasibility degree
Is calculated as follows:
x’eijcis a parameter in real-time information, min {0, g (x'eijc) Denotes: when inputting parameter xeijcWhen the constraint condition is met, 0 is output, and x 'is output otherwise'eijcAnd a preset upper limit value x 'in the constraint condition'eijc-maxA difference of (d); in this embodiment, the constraint conditions are: artificially set upper limit value of real-time information: v'eijc-max＝1.3vij、a′eijc-max＝1.3aij、τ′eijc-max＝1.3τij；
Namely: joint moment: tau'eijc≤τ′eijc-maxAnd joint acceleration: a'eijc≤a′eijc-maxJoint jerk: a ″)eijc≤a″eijc-maxAnd joint velocity: v'eijc≤v′eijc-max。
hk(Z’eijc) Represents: when the parameter Z 'is input'eijcSatisfy Z'eijc∈Z’eijc-maxWhen it is output, 0, otherwise, infinity, Z'eijc-maxRepresenting the upper limit of the angle which can be reached by each joint, which is the intrinsic parameter of the mechanical arm;
when the difference value is less than 0.3 x'eijc-maxAnd h isk(Z’eijc) When equal to 0, degree of feasibility
And meeting a preset feasibility threshold.
Wherein, the fitness f is calculated as follows:
Spijcfor optimizing the indexes, calculating according to the real-time information of the path population q', wherein p is 1,2,3 … … L, and L is the number of the optimizing indexes: k is a radical ofpWeight, k, for each optimization index set manuallyp0 means that the index does not participate in the optimization.
In this embodiment, L is 6, k1The value is 120, k2A value of 30, k3A value of 50, k4The value is 100, k5A value of 30, k6The value is 60, and the optimization indexes comprise:
index of exercise time S1ijcAnd the method is used for measuring the motion efficiency of the robot:
average acceleration index S of joint2ijAnd the method is used for measuring the energy consumption of the robot joint:
average fluctuation index S of joint3ijFor the measurement, the smoothness of the trajectory is:
mean moment index S of joint4ij:
Energy index S of joint average consumption5ij:
Moment index S6ijAnd is used for measuring the stability of the actuating mechanism:
wherein T is the total time of the mechanical arm moving along the initial path, A'eijcThe derivative of the acceleration, jerk.
Experiments show that: the initial path length planned by the RRT method is 1576.8mm, the single-time operation time of the mechanical arm is 3476ms, and the path length optimized by the method is as follows: 1512.2mm, and the single operation time of the mechanical arm is 3019 ms; the optimized path length is shortened, and the consumed time is short.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. The foregoing description is not intended to be exhaustive or to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable others skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications thereof. It is intended that the scope of the invention be defined by the following claims and their equivalents.
Claims (10)
1. A multi-degree-of-freedom mechanical arm obstacle avoidance path planning method is characterized by comprising the following steps:
s1, planning a path of the mechanical arm by adopting a mechanical arm path planning method to obtain an initial path, wherein the number of joints of the mechanical arm is m;
s2, the mechanical arm moves along an initial path, and real-time information of each joint at each preset moment is collected;
the real-time information includes:current moment of motion tijJoint space information ZijVelocity vijAcceleration aijAnd driving torque information tauijI is 1,2,3 … … m; j denotes the current time, j is 1,2,3 … … n; n is the total number of the preset time; the joint space information ZijAngle information of the current joint;
s3, performing mathematical modeling and mapping on the real-time information in the initial path to obtain a set containing a plurality of pieces of path information, wherein the single piece of path information comprises the speed, the acceleration and the driving moment of each joint at a specific moment, which are obtained through calculation based on preset motion time and the angle of each joint;
s4, recording the set containing multiple pieces of path information as QecE corresponds to the fourth path and c corresponds to the fourth iteration; e is 1,2,3 … … r, r is the total number of paths; c is 1,2,3 … … D, D is a preset iteration total number; qecThe contained information is: time t 'corresponding to ith joint of ith path at jth moment in the c-th iteration'eijcAn angle Z'eijcV 'speed'eijcAnd acceleration a'eijcAnd drive torque information τ'eijc；
At QecRandomly selecting Q paths, and calculating corresponding Q according to preset constraint conditionsecWhen the feasibility degree does not meet a preset feasibility degree threshold value, the current path is rejected; traversing q paths, and recording a set formed by all the remaining paths as q'; calculating the fitness of each path in q', and marking the path with the maximum fitness as a qualified path;
will QecTaking the set of the rejected medium qualified paths as a new QecScreening qualified paths by adopting the same method until the number of the qualified paths reaches G, and marking a set formed by the G qualified paths as a second generation path set Q'ec(ii) a Wherein G is 10 to 40 percent r;
s5, set Q 'to the second generation path'ecRandomly carrying out cross treatment to obtain a hybridized path, and carrying out mutation treatment on the hybridized path; at the same time, fusing mutated and non-mutated paths; to obtain a third generation circuitDiameter set Q ″)ecThe number of the paths is 90-110% r;
s6, collecting the obtained third generation path Q ″ecAs Q in step S4ecScreening Q paths, and acquiring a second generation path set Q 'by using the same method'ecThen, the method of step S5 is used to obtain the third generation path set Q ″ecRepeating the step until the iteration number is D; the third generation path set Q' obtained in the last iterationecAnd taking the path with the maximum fitness as a final target path.
2. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as recited in claim 1, characterized in that: and S7, the mechanical arm moves along the target path to perform collision detection, if the mechanical arm collides with the environment, the reachable working space of the robot under the constraint condition of S4 is reset, and the step S4 is performed again.
3. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as recited in claim 1, characterized in that: the model mapping adopts Logistic mapping or McDill-amateis model.
4. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as claimed in claim 3, wherein: the model mapping adopts Logistic mapping, and the input parameters of the Logistic mapping are calculated as follows: chaos variable betaj+1Path spatial fluctuation range Deltatj；
βj+1＝μβj(1-βj)
Manually setting: initial chaotic variable beta1Taking values: beta is not less than 01Less than or equal to 1, chaotic coefficient mu, value: mu is more than or equal to 0 and less than or equal to 4; beta is ajIs a chaotic variable, beta, of the current time on the initial pathj+1Chaotic variables of the initial path at the next moment;
5. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as recited in claim 1, characterized in that: q in step S4 is [ 10% to 30% r]Wherein r is c ═ 1, QecTotal number of paths of (1);
degree of feasibility
Is calculated as follows:
x’eijcis a parameter in real-time information, min {0, g (x'eijc) Denotes: when inputting parameter xeijcWhen the constraint condition is met, 0 is output, and x 'is output otherwise'eijcAnd a preset upper limit value x 'in the constraint condition'eijc-maxA difference of (d);
hk(Z’eijc) Represents: when the parameter Z 'is input'eijcSatisfy Z'eijc∈Z’eijc-maxWhen it is output, 0, otherwise, infinity, Z'eijc-maxRepresenting the upper limit of the angle which can be reached by each joint, which is the intrinsic parameter of the mechanical arm; when the difference value is less than 0.2-0.5 x'eijc-maxAnd h isk(Z’eijc) When equal to 0, degree of feasibility
And meeting a preset feasibility threshold.
6. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as recited in claim 1 or 5, characterized in that: the constraint conditions are as follows: manually setting the upper limit value of the real-time information according to the model and the operation requirement of the robot: v'eijc-max＝1.2～1.5vij、a′eijc-max＝1.2～1.5aij、τ′eijc-max＝1.2～1.5τij。
7. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as recited in claim 4, wherein: the fitness f of step S4 is calculated as follows:
Spijcfor optimizing the indexes, calculating according to the real-time information of the path population q', wherein p is 1,2,3 … … L, and L is the number of the optimizing indexes: k is a radical ofpWeight, k, for each optimization index set manuallyp0 means that the index does not participate in the optimization.
8. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as recited in claim 7, wherein: and L is 6, and the optimization indexes comprise:
index of exercise time S1ijcAnd the method is used for measuring the motion efficiency of the robot:
average acceleration index S of joint2ijAnd the method is used for measuring the energy consumption of the robot joint:
average fluctuation index S of joint3ijFor the measurement, the smoothness of the trajectory is:
mean moment index S of joint4ij:
Energy index S of joint average consumption5ij:
Moment index S6ijAnd is used for measuring the stability of the actuating mechanism:
wherein T is the total time of the mechanical arm moving along the initial path, A'eijcThe derivative of the acceleration, jerk.
9. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as recited in claim 1, characterized in that: the setting mode of the preset time is as follows: setting a time interval t, and acquiring real-time information of the current moment at each time interval t from the initial position of the initial path; t is 0.5ms to 4 ms.
10. The obstacle avoidance path planning method for the multi-degree-of-freedom mechanical arm as recited in claim 1, characterized in that: in the step S1, a depth camera is used for obtaining an environment model of a robot motion space, the environment model is simplified by adopting a mixed level bounding box method, and a mechanical arm path planning method is used for planning a path of a mechanical arm to obtain an initial path;
the mechanical arm path planning method comprises the following steps: a, D, RRT, PRM, artificial potential field method, genetic algorithm, particle swarm algorithm or ant colony algorithm.
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