A live working mechanical arm six-freedom redundancy task trajectory planning method

Abstract

The application relates to a live working mechanical arm six-freedom redundancy task trajectory planning method, which comprises the following steps: acquiring D-H joint parameters of a six-freedom mechanical arm; establishing a kinematics model of the six-freedom mechanical arm to obtain a base coordinate system and an end coordinate system of the six-freedom mechanical arm; based on the base coordinate system and the end coordinate system, planning a joint motion trajectory into a first section, a second section and a third section to perform first constraint on the motion trajectory, wherein the first section is planned by using a cubic polynomial, the second section is planned by using a quintic polynomial, and the third section is planned by using a cubic polynomial; through a chaos mapping strategy, the joint motion trajectory is mapped to a solution space to obtain a sparrow initial population, wherein each sparrow in the sparrow initial population represents an angle solution of the joint; based on the sparrow initial population, a sparrow search algorithm is used to perform second constraint on the joint motion trajectory; and based on the first constraint and the second constraint, an optimal motion trajectory of the joint is determined.

Description

Technical Field

[0001] This application relates to the field of robotic arm motion trajectory technology, and in particular to a method for planning the six-degree-of-freedom redundant task trajectory of a robotic arm for live-line work. Background Technology

[0002] In recent years, industrial robotic arms have been increasingly used in industrial production and in complex environments. As the basis for motion control of live-line working robotic arms, trajectory planning has also received widespread attention. Its performance is of decisive significance to the working efficiency, motion stability and energy consumption of live-line working industrial robots.

[0003] With technological advancements, construction difficulties are constantly increasing, and workers' ability to perceive and judge their surroundings, as well as their ability to operate robotic arms, will decline. Especially in the context of live-line work, the low intelligence of robotic arms seriously affects construction efficiency and work quality.

[0004] Currently, traditional live-line working industrial robotic arms suffer from low intelligence and are greatly affected by human subjective factors. There is an urgent need for a six-degree-of-freedom redundant task trajectory planning method for live-line working robotic arms. Summary of the Invention

[0005] This application provides a six-degree-of-freedom redundant task trajectory planning method for a robotic arm used in live-line work, which can solve the problems in the prior art where the operation is greatly affected by human subjective factors and the low intelligence of the robotic arm affects the construction efficiency and work quality.

[0006] The first aspect of this application provides a six-DOF redundant task trajectory planning method for a robotic arm performing live-line work, including:

[0007] Obtain the DH joint parameters of a six-DOF robotic arm;

[0008] Based on the DH joint parameters, a kinematic model of a six-degree-of-freedom manipulator is established, and the base coordinate system and end effector coordinate system of the six-degree-of-freedom manipulator are obtained.

[0009] Based on the base coordinate system and the end coordinate system, the motion trajectory of the joint is planned into a first segment, a second segment and a third segment, and a first constraint is applied to the motion trajectory respectively. The first segment is planned using a cubic polynomial, the second segment is planned using a quintic polynomial and the third segment is planned using a cubic polynomial.

[0010] By using a chaotic mapping strategy, the motion trajectory of the joint is mapped to the solution space to obtain an initial population of sparrows, where each sparrow in the initial population represents the angle solution of the joint;

[0011] Based on the initial sparrow population, the movement trajectory of the joints is subject to a second constraint using the sparrow search algorithm.

[0012] Based on the first constraint and the second constraint, the optimal motion trajectory of the joint is determined.

[0013] In one feasible approach, the step of establishing a kinematic model of a six-DOF manipulator based on DH joint parameters to obtain the base coordinate system and end effector coordinate system of the six-DOF manipulator includes:

[0014] Based on the position and orientation of the base coordinate system of the six-degree-of-freedom robotic arm, the homogeneous transformation matrix from coordinate system i-1 to coordinate system i between adjacent coordinate systems is obtained.

[0015]

[0016] in, This indicates that the coordinate system rotates about the X-axis by α. i-1 , This indicates a translation along the x-axis. i-1 θ1 represents the joint angle, d i Indicates the link offset;

[0017] Multiply the six homogeneous transformation matrices of the six-degree-of-freedom robotic arm to determine the homogeneous transformation matrix of the end-effector coordinate system relative to the base coordinate system:

[0018]

[0019] Wherein, n, o, a represent the unit vectors of the attitude of the end coordinate system X, Y, Z relative to the base coordinate system, respectively, and p represents the position vector of the end coordinate system relative to the base coordinate system.

[0020] In one feasible embodiment, the step of planning the motion trajectory of the joint into a first segment, a second segment, and a third segment based on the base coordinate system and the end effector coordinate system, and applying a first constraint to the motion trajectory respectively, includes:

[0021] Obtain the motion trajectory of the i-th joint;

[0022] Based on the motion trajectory of the i-th joint, the starting and ending trajectories of the first segment of the i-th joint using a cubic polynomial are obtained by the following formula:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] Where, θ(0), θ e These are the joint angles at the start and end points, respectively; These are the joint velocities at the starting and ending points, obtained by differentiation, respectively; Let t0 and t' be the joint accelerations at the starting and ending points obtained from the second derivative, respectively. e These represent the start and end times, respectively, and a0 to a5 are the coefficients on the trajectory of the joints.

[0030] In one feasible embodiment, the step of planning the motion trajectory of the joint into a first segment, a second segment, and a third segment based on the base coordinate system and the end effector coordinate system, and applying a first constraint to the motion trajectory respectively, includes:

[0031] The starting and ending trajectories of the second segment of the i-th joint, calculated using a fifth-order polynomial, are obtained by the following formula:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] Where, θ(0), θ e These are the joint angles at the start and end points, respectively; These are the joint velocities at the starting and ending points, obtained by differentiation, respectively; Let t0 and t' be the joint accelerations at the starting and ending points obtained from the second derivative, respectively. e These represent the start and end times, respectively.

[0039] In one feasible embodiment, the step of planning the motion trajectory of the joint into a first segment, a second segment, and a third segment based on the base coordinate system and the end effector coordinate system, and applying a first constraint to the motion trajectory respectively, includes:

[0040] The third segment of the i-th joint is calculated using the same formula as the cubic polynomial of the first segment to plan the starting and ending trajectories.

[0041] In one feasible embodiment, the step of planning the motion trajectory of the joint into a first segment, a second segment, and a third segment based on the base coordinate system and the end effector coordinate system, and applying a first constraint to the motion trajectory respectively, includes:

[0042] Based on the first segment, the second segment, and the third segment, the optimization function and constraints for the i-th joint are as follows:

[0043]

[0044]

[0045]

[0046] max{|V i |}≤V imax

[0047] Among them, V i V represents the velocity corresponding to the polynomial interpolation of the first segment, the second segment, and the third segment in the i-th joint. imax This represents the maximum speed allowed for the i-th joint.

[0048] In one feasible approach, the step of mapping the motion trajectory of the joints to a solution space using a chaotic mapping strategy to obtain the initial sparrow population includes:

[0049] Determine the chaotic variable x using a chaotic mapping strategy. k :

[0050] x k+1 =μx k (1-x k ), 0≤μ≤4, 0<x k <1

[0051] Where μ is the Logistic chaotic mapping parameter.

[0052] In one feasible embodiment, the step of applying a second constraint to the motion trajectory of the joint based on the initial sparrow population and using the sparrow search algorithm includes:

[0053] Based on the initial sparrow population, determine the number of sparrows in the initial sparrow population, the ratio between discoverers and followers among the sparrows, and the maximum number of iterations;

[0054] Based on the maximum number of iterations, calculate the fitness of each sparrow, and find the best and worst sparrows based on the fitness.

[0055] Based on the ratio of the discoverer to the follower, the number of the discoverer and the follower is determined, and the positions of the discoverer, the followers, and the monitors are updated, wherein the monitors are converted from a preset number of the followers;

[0056] Iterate successively, calculate the fitness value of each sparrow and the average fitness value of the sparrow population, and compare them;

[0057] If the new position of the sparrow is better than the original position, then update the original position; otherwise, keep the original position of the sparrow unchanged.

[0058] Once the maximum number of iterations is reached, the position of the sparrow is output, forming the second constraint.

[0059] A second aspect of this application provides a data processing system, comprising:

[0060] One or more processors;

[0061] Storage device for storing one or more programs.

[0062] When the one or more programs are executed by the one or more processors, the one or more processors execute the aforementioned six-degree-of-freedom redundant task trajectory planning method for live-line operations.

[0063] A third aspect of this application provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the aforementioned six-degree-of-freedom redundant task trajectory planning method for a robotic arm performing live operations.

[0064] The beneficial effects of this application are:

[0065] The DH joint parameters of a six-DOF robotic arm are obtained. Based on these parameters, a kinematic model of the six-DOF robotic arm is established, resulting in its base coordinate system and end-effector coordinate system. Based on these coordinate systems, the joint motion trajectories are planned into first, second, and third segments, with each segment applying a first constraint. Then, using a chaotic mapping strategy, the joint motion trajectories are mapped to a solution space, obtaining an initial sparrow population. Based on this initial population, a sparrow search algorithm is used to apply a second constraint to the joint motion trajectories. Finally, based on the first and second constraints, the optimal motion trajectory of the joints is determined. By applying multiple constraints to the six-DOF robotic arm, the trajectory is optimized with the goal of minimizing the time required, thereby improving the robotic arm's intelligence and enhancing construction efficiency and work quality. Attached Figure Description

[0066] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0067] Figure 1 A flowchart of a six-DOF redundant task trajectory planning method for a robotic arm performing live-line work;

[0068] Figure 2 A schematic diagram of the adjacent joint link coordinate system for a six-DOF redundant task trajectory planning method for a robotic arm performing live-line work.

[0069] Figure 3 A second constraint is applied to the motion trajectory of the joints in a six-DOF redundant task trajectory planning method for a robotic arm performing live-line work.

[0070] Figure 4 The fitness convergence curve of joint 1 constraining the motion trajectory is given in a six-DOF redundant task trajectory planning method for a robotic arm performing live operations.

[0071] Figure 5 The fitness convergence curve of joint 6 constraining the motion trajectory of a six-DOF redundant task trajectory planning method for a robotic arm performing live-line work is shown.

[0072] Figure 6 The motion planning curve for constraining the motion trajectory of joint 1 in a six-DOF redundant task trajectory planning method for a robotic arm performing live-line work;

[0073] Figure 7 This is a motion planning curve for joint 6 constraining the motion trajectory of a six-DOF redundant task trajectory planning method for a robotic arm performing live operations. Detailed Implementation

[0074] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] See Figures 1 to 7 This application discloses a six-degree-of-freedom redundant task trajectory planning method for a robotic arm performing live-line work, comprising the following steps:

[0076] like Figure 1 As shown, S101: Obtain the DH joint parameters of the six-DOF robotic arm.

[0077] like Figure 2 As shown in Table 1, the DH parameters of the six-DOF robotic arm are as follows:

[0078]

[0079] Table 1

[0080] Table 1 provides examples of DH parameters and the range of joint rotation angles, but does not limit the joint rotation angles.

[0081] Where i is the link, α i-1 Let a be the torsion angle of the connecting rod. i-1 Let θ be the length of the link. i Let d be the joint angle θ. i is the link offset, and Offset is the offset amount.

[0082] The DH joint parameters, according to predefined rules, establish a coordinate system for each link, describing the transformation relationship from one link's coordinate system to the next adjacent link's coordinate system. The transformation between adjacent coordinate systems is decomposed into several steps, each with only one parameter. The combination of corresponding transformations of the link coordinate systems achieves the transformation between adjacent coordinate systems.

[0083] S102: Based on the DH joint parameters, establish the kinematic model of the six-degree-of-freedom manipulator to obtain the base coordinate system and end effector coordinate system of the six-degree-of-freedom manipulator.

[0084] Based on the position and orientation of the base coordinate system of the six-degree-of-freedom robotic arm, the homogeneous transformation matrix from coordinate system i-1 to coordinate system i between adjacent coordinate systems is obtained.

[0085]

[0086] in, This indicates that the coordinate system rotates about the X-axis by α. i-1 , This indicates a translation along the x-axis. i-1 θ1 represents the joint angle, d i Indicates the link offset;

[0087] Multiply the six homogeneous transformation matrices of the six-degree-of-freedom robotic arm to determine the homogeneous transformation matrix of the end-effector coordinate system relative to the base coordinate system:

[0088]

[0089] Wherein, n, o, a represent the unit vectors of the attitude of the end coordinate system X, Y, Z relative to the base coordinate system, respectively, and p represents the position vector of the end coordinate system relative to the base coordinate system.

[0090] The initial and final positions of the joints of the six-degree-of-freedom robotic arm can be determined by the base coordinate system and the end-effector coordinate system. Based on the initial and final positions, the trajectory of the robotic arm can be optimized with the goal of minimizing the time required.

[0091] S103: Based on the base coordinate system and the end coordinate system, the motion trajectory of the joint is planned into a first segment, a second segment and a third segment, and the motion trajectory is subject to a first constraint respectively. The first segment is planned using a cubic polynomial, the second segment is planned using a quintic polynomial and the third segment is planned using a cubic polynomial.

[0092] The formulas for cubic, quintic, and cubic polynomials are as follows:

[0093] θ i1 (t)=a i13 t 3 +a i12 t 2 +a i11 t+a i10

[0094] θ i2 (t)=a i25 t 5 +a i23 t 3 +a i22 t 2 +a i24 t 4 +a i20

[0095] θ i3 (t)=a i33 t 3 +a i32 t 2 +a i31 t+a i30

[0096] In the formula: θ i1 (t) is the cubic interpolation polynomial of the first segment of the i-th joint; θ i2 (t) is the 5th-order interpolation polynomial for the second segment of the i-th joint; θ i3 (t) is the cubic interpolation polynomial for the third segment of the i-th joint; the time intervals corresponding to the first, second, and third segments are t0~t1, t1~t2, and t2~t3, respectively; the coefficient a i1j ai2j a i3j These are the j-th coefficients of the polynomials for the first, second, and third segments on the trajectory of the i-th joint, respectively, where i = 1, 2, 3, ..., n, representing the i-th joint.

[0097] For example, the motion trajectory gauge of the i-th joint is used as an example for illustration. The motion trajectory gauge of the i-th joint is obtained, and the motion trajectory gauge of the i-th joint is used for...

[0098] The starting and ending trajectories of the first segment of the i-th joint, calculated using a cubic polynomial, are obtained by the following formula:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] Where, θ(0), θ e These are the joint angles at the start and end points, respectively; These are the joint velocities at the starting and ending points, obtained by differentiation, respectively; Let t0 and t' be the joint accelerations at the starting and ending points obtained from the second derivative, respectively. e These represent the start and end times, respectively, and a0 to a5 are the coefficients on the trajectory of the joints.

[0106] Based on the trajectory function equation of the first segment of the i-th joint and the constraints such as displacement, angle, angular velocity, and angular acceleration of the six-degree-of-freedom robotic arm, the trajectory function equations of the second segment are derived as 5th-order polynomials for the starting and ending points.

[0107] The starting and ending trajectories of the second segment of the i-th joint, calculated using a fifth-order polynomial, are obtained by the following formula:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Where, θ(0), θ e These are the joint angles at the start and end points, respectively; These are the joint velocities at the starting and ending points, obtained by differentiation, respectively; Let t0 and t' be the joint accelerations at the starting and ending points obtained from the second derivative, respectively. e These represent the start and end times, respectively.

[0115] The third segment of the i-th joint is calculated using the same formula as the cubic polynomial of the first segment to plan the starting and ending trajectories.

[0116] Based on the first segment, the second segment, and the third segment, the optimization function and constraints for the i-th joint are as follows:

[0117]

[0118]

[0119]

[0120] max{|V i |}≤V imax

[0121] Among them, V i V represents the velocity corresponding to the polynomial interpolation of the first segment, the second segment, and the third segment in the i-th joint. imax This represents the maximum speed allowed for the i-th joint.

[0122] Based on the first, second, and third segments, the first constraint on the i-th joint is formed.

[0123] As can be seen from the above, if the order of the interpolation function is too high during the construction of the trajectory function of a six-degree-of-freedom robotic arm, it will lead to problems such as poor convex hull property and computational complexity; if the order is too low, it will lead to problems such as unsmooth velocity and acceleration curves and sudden acceleration changes. Therefore, this application uses polynomial functions of the first, second and third segments, which can solve both the problem of poor convex hull property and the problem of unsmooth velocity and acceleration curve changes.

[0124] S104: By using a chaotic mapping strategy, the motion trajectory of the joint is mapped to the solution space to obtain an initial population of sparrows, wherein each sparrow in the initial population of sparrows represents the angle solution of the joint.

[0125] In this process, the first constraint is formed by the kinematic model of the six-degree-of-freedom robotic arm and multi-source heterogeneity such as polynomial functions. To ensure the time optimization in the trajectory planning of the six-degree-of-freedom robotic arm, an improved sparrow search algorithm is used for solving the problem. A Logistic chaotic mapping strategy is introduced into the trajectory generation method to improve the optimization selection degree of the six-degree-of-freedom robotic arm trajectory and realize the trajectory planning of the six-degree-of-freedom redundant task of the industrial robotic arm.

[0126] Specifically, the chaotic variable x is determined through a chaotic mapping strategy. k :

[0127] x k+1 =μx k (1-x k ), 0≤μ≤4, 0<x k <1

[0128] Where μ is the parameter of the Logistic chaotic mapping. k When μ ∈ (0,1), the Logistic mapping is in a chaotic state, and when μ = 4, the Logistic mapping is in a completely chaotic state, at which point a complete search of nodes in the chaotic space can be achieved.

[0129] Understandably, while the Sparrow Search algorithm possesses strong global search capabilities and convergence speed in the trajectory planning process of a six-DOF robotic arm, it still suffers from problems such as easily getting trapped in local optima in the later stages of evolution. This leads to useless searches in space, deviating from the optimal planning direction and incurring significant time costs. Therefore, to reduce time costs and space complexity, this application utilizes Logistic chaotic mapping to improve the Sparrow Search algorithm, avoiding the problem of easily getting trapped in local optima in the later stages of evolution, thus preventing useless searches in space, deviations from the optimal planning direction, and significant time costs.

[0130] S105: Based on the initial sparrow population, the movement trajectory of the joint is subject to a second constraint using the sparrow search algorithm.

[0131] The sparrow search algorithm is an intelligent optimization algorithm derived from the foraging and anti-predation behaviors of sparrow populations. During foraging, sparrows are divided into finders and followers. Finders are responsible for searching for food and providing foraging areas and directions for the entire population, while followers utilize the finders to obtain food. To obtain food, sparrows typically employ both finder and follower strategies. Individuals in the population monitor the behavior of other individuals, and predators compete with high-intaker companions for food resources to increase their predation rate. Furthermore, when the sparrow population is attacked by predators, it will exhibit anti-predation behaviors. The algorithm simulates these sparrow behaviors to solve for optimal function results.

[0132] like Figure 3 As shown, specifically, the second constraint on the motion trajectory of the joint includes steps S1051 to S1056.

[0133] S1051: Based on the initial sparrow population, determine the number of sparrows in the initial sparrow population, the ratio between discoverers and followers among the sparrows, and the maximum number of iterations.

[0134] This involves initializing the sparrow population, setting the initial sparrow population to include the number of sparrows, the ratio between discoverers and joiners, and the maximum number of iterations.

[0135] In this context, the sparrow represents the angular solution of a joint. We know that each joint has a different angular solution at different positions, and these angular solutions correspond to the sparrow. Depending on the joint position at different times, there will be different angular solutions. By treating these angular solutions as the sparrow and determining the sparrow's position, we can determine the joint's motion trajectory.

[0136] The number of sparrows was determined based on the initial sparrow population. The number of sparrows was divided into discoverers and joiners, and the ratio of discoverers to joiners was obtained. Then, the maximum number of iterations or a threshold was determined, where the threshold is the range of iterations.

[0137] S1052: Based on the maximum number of iterations, calculate the fitness of each sparrow, and find the best and worst sparrows based on the fitness.

[0138] The process involves calculating the fitness of each sparrow, and then using the inheritance of fitness to determine the best and worst sparrows. The calculation of sparrow fitness does not exceed the set maximum number of iterations.

[0139] S1053: Determine the number of discoverers and followers based on the ratio of discoverers to followers, and update the positions of discoverers, followers, and monitors, wherein the monitors are converted from a preset number of followers.

[0140] Among them, the location of the discoverer has been updated:

[0141]

[0142] Where t is the current iteration number; T is the maximum iteration number; α is a uniform random number in (0,1]; Q is a random number that satisfies the standard normal distribution; L is a 1×d matrix with all elements being 1; R2∈[0,1] is the warning value; ST represents the safety value.

[0143] Follower position update:

[0144]

[0145] A + =A T (AA T ) -1

[0146] in: This represents the position of the sparrow in the i-th dimension during the (t+1)-th iteration. This represents the worst position of the sparrow in the d-th dimension during the t-th iteration. Let A be the optimal position of the sparrow in the d-th dimension at generation t+1; A is a 1×d matrix where each element is randomly assigned a value of -1 or 1; L is a 1×d matrix where all elements are 1.

[0147] Monitor location update:

[0148]

[0149] Where: β is the step size control parameter, which follows N(0,1) random numbers; K is a random number and K∈[-1,1], representing the direction of the sparrow's movement, and is also the step size control parameter; e is a very small number to avoid a denominator of 0; f i f represents the fitness value of the i-th sparrow. i and f g These are the best and worst fitness values ​​for the current sparrow population, respectively.

[0150] S1054: Iterate successively, calculate the fitness value of each sparrow and the average fitness value of the sparrow population, and compare them.

[0151] In one iteration, the fitness value of each sparrow and the average fitness value of the sparrow population are calculated. The fitness value of each sparrow is then compared with the average fitness value of the sparrow population to determine whether the fitness value of the sparrow is greater than the average fitness value of the sparrow population.

[0152] S1055: If the newly generated position of the sparrow is better than the original position, then update the original position; otherwise, keep the original position of the sparrow unchanged.

[0153] If the sparrow's fitness value is greater than the average fitness value of the sparrow population, the sparrow's location is updated; otherwise, the sparrow's location remains unchanged.

[0154] S1056: When the maximum number of iterations is reached, output the position of the sparrow to form the second constraint.

[0155] The process involves iteratively calculating the fitness value of sparrows and the average fitness value of the sparrow population. Once the maximum number of iterations is reached, the calculation of the fitness value of sparrows and the average fitness value of the sparrow population is stopped, i.e., the sparrows' positions are no longer updated.

[0156] In the process of calculating the sparrow, the second constraint on the solution of the joint angle is realized.

[0157] S106: Based on the first constraint and the second constraint, determine the optimal motion trajectory of the joint.

[0158] In this application, the time-optimal trajectory planning optimizes the trajectory of a six-degree-of-freedom (DOF) robotic arm under given constraints, aiming to minimize the time required. This process involves two stages: first, interpolating the path using a polynomial function interpolation algorithm; and second, interpolating the path using a sparrow search algorithm. By inputting the initial and final positions of the motion trajectory, the algorithm plans the trajectory and ultimately outputs the trajectory via motors.

[0159] like Figure 4 and Figure 5 As shown, the fitness convergence curve based on the constraints of joints 1 and 6 on the motion trajectory, compared with existing related motion trajectory algorithms, shows that the optimal motion trajectory of this application is superior to the existing related fitness convergence curves.

[0160] like Figure 6 and Figure 7 As shown, the motion planning curve constrained by joints 1 and 6, compared with existing related motion planning curves, shows that the optimal motion trajectory of the joints in this application is superior to that of existing related motion planning curves.

[0161] This application provides a data processing system including:

[0162] One or more processors;

[0163] Storage device for storing one or more programs.

[0164] When the one or more programs are executed by the one or more processors, the one or more processors execute the aforementioned six-degree-of-freedom redundant task trajectory planning method for live-line operations.

[0165] This application provides a computer-readable storage medium storing executable instructions thereon, which, when executed by a processor, cause the processor to perform the aforementioned six-degree-of-freedom redundant task trajectory planning method for a robotic arm performing live operations.

[0166] The above embodiments merely illustrate specific implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and arbitrary combinations are possible without conflict between the embodiments, all of which fall within the protection scope of the present invention.

Claims

1. A method for planning the six-degree-of-freedom redundant task trajectory of a robotic arm for live-line work, characterized in that, include: Obtain the DH joint parameters of a six-DOF robotic arm; Based on the DH joint parameters, a kinematic model of a six-degree-of-freedom manipulator is established, and the base coordinate system and end effector coordinate system of the six-degree-of-freedom manipulator are obtained. Based on the base coordinate system and the end coordinate system, the motion trajectory of the joint is planned into a first segment, a second segment and a third segment, and a first constraint is applied to the motion trajectory respectively. The first segment is planned using a cubic polynomial, the second segment is planned using a quintic polynomial and the third segment is planned using a cubic polynomial. By using a chaotic mapping strategy, the motion trajectory of the joint is mapped to the solution space to obtain an initial population of sparrows, where each sparrow in the initial population represents the angle solution of the joint; The step of mapping the motion trajectory of the joints to the solution space using a chaotic mapping strategy to obtain the initial sparrow population includes: Determining chaotic variables through a chaotic mapping strategy : ； in, For Logistic chaotic mapping parameters; Based on the initial sparrow population, a second constraint is applied to the motion trajectory of the joint using a sparrow search algorithm, including: Based on the initial sparrow population, determine the number of sparrows in the initial sparrow population, the ratio between discoverers and followers among the sparrows, and the maximum number of iterations; Based on the maximum number of iterations, calculate the fitness of each sparrow, and find the best and worst sparrows based on the fitness. Based on the ratio of the discoverer to the follower, the number of the discoverer and the follower is determined, and the positions of the discoverer, the follower, and the monitor are updated, wherein the monitor is converted from a preset number of the followers; Iterate successively, calculate the fitness value of each sparrow and the average fitness value of the sparrow population, and compare them; If the new position of the sparrow is better than the original position, then update the original position; otherwise, keep the original position of the sparrow unchanged. Once the maximum number of iterations is reached, the position of the sparrow is output, forming the second constraint. Based on the first constraint and the second constraint, the optimal motion trajectory of the joint is determined.

2. The six-degree-of-freedom redundant task trajectory planning method for a robotic arm performing live-line work according to claim 1, characterized in that, The steps of establishing a kinematic model of a six-degree-of-freedom manipulator based on DH joint parameters, and obtaining the base coordinate system and end effector coordinate system of the six-degree-of-freedom manipulator, include: Based on the position and orientation of the base coordinate system of the six-degree-of-freedom robotic arm, the homogeneous transformation matrix from coordinate system i-1 to coordinate system i between adjacent coordinate systems is obtained. : ； in, Indicates the rotation of the coordinate system around the X-axis , Indicates along Axis translation , Indicates joint angle, Indicates the link offset; Multiply the six homogeneous transformation matrices of the six-degree-of-freedom robotic arm to determine the homogeneous transformation matrix of the end-effector coordinate system relative to the base coordinate system: ； Wherein, n, o, a represent the unit vectors of the attitude of the end coordinate system X, Y, Z relative to the base coordinate system, respectively, and p represents the position vector of the end coordinate system relative to the base coordinate system.

3. The six-degree-of-freedom redundant task trajectory planning method for a robotic arm performing live-line work according to claim 1, characterized in that, The step of planning the motion trajectory of the joint into a first segment, a second segment, and a third segment based on the base coordinate system and the end-effector coordinate system, and applying a first constraint to the motion trajectory respectively, includes: Obtain the motion trajectory of the i-th joint; Based on the motion trajectory of the i-th joint, the starting and ending trajectories of the first segment of the i-th joint using a cubic polynomial are obtained by the following formula: i （0） = + + + ; （0） =3 + + ； （0） =6 + ； = + + + ； ； ； Where, θ （0） , These are the joint angles at the start and end points, respectively; （0） , These are the joint velocities at the starting and ending points, obtained by differentiation, respectively; （0） , The joint accelerations at the starting and ending points, obtained by calculating the second derivative, are respectively the joint accelerations at the starting and ending points. , These are the start and end times, respectively. These are the coefficients on the trajectory of the joints.

4. The six-degree-of-freedom redundant task trajectory planning method for a robotic arm performing live-line work according to claim 3, characterized in that, The step of planning the motion trajectory of the joint into a first segment, a second segment, and a third segment based on the base coordinate system and the end-effector coordinate system, and applying a first constraint to the motion trajectory respectively, includes: The starting and ending trajectories of the second segment of the i-th joint, calculated using a fifth-order polynomial, are obtained by the following formula: i （20） = + + + + + ; （20） =5 + + + + ； （20） =20 +12 +6 +2 ； = + + + + + ； 2e =5 + + + + ； 2e =20 +12 +6 +2 ； Where, θ （20） , These are the joint angles at the start and end points, respectively; （20） , 2e These are the joint velocities at the starting and ending points, obtained by differentiation, respectively; （20） , 2e The joint accelerations at the starting and ending points, obtained by calculating the second derivative, are respectively the joint accelerations at the starting and ending points. , These are the start and end times, respectively. These are the coefficients on the trajectory of the joints.

5. The six-degree-of-freedom redundant task trajectory planning method for a robotic arm performing live-line work according to claim 4, characterized in that, The step of planning the motion trajectory of the joint into a first segment, a second segment, and a third segment based on the base coordinate system and the end-effector coordinate system, and applying a first constraint to the motion trajectory respectively, includes: The third segment of the i-th joint is calculated using the same formula as the cubic polynomial of the first segment to plan the starting and ending trajectories.

6. The six-degree-of-freedom redundant task trajectory planning method for a robotic arm performing live-line work according to claim 5, characterized in that, The step of planning the motion trajectory of the joint into a first segment, a second segment, and a third segment based on the base coordinate system and the end-effector coordinate system, and applying a first constraint to the motion trajectory respectively, includes: Based on the first segment, the second segment, and the third segment, the optimization function and constraints for the i-th joint are as follows: ； = ； = 20； = ； ； ； ； ； in, Let be the velocity corresponding to the polynomial interpolation of the first segment, the second segment, and the third segment in the i-th joint. θ represents the maximum permissible velocity value for the i-th joint. （20） =θ 20 The goal is to optimize the trajectory of the robotic arm in the shortest possible time.

7. A data processing system, comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors execute the six-degree-of-freedom redundant task trajectory planning method for live-line operation of a robotic arm according to any one of claims 1 to 6.

8. A computer-readable storage medium having stored executable instructions thereon, which, when executed by a processor, cause the processor to perform a six-degree-of-freedom redundant task trajectory planning method for live-line work of a robotic arm according to any one of claims 1 to 6.

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