Space mining robot motion control method based on african vulture optimization algorithm

Abstract

The application discloses a space mining robot motion control method based on an African vulture optimization algorithm, which comprises the following steps: establishing a kinematic model of a space mining robot according to the actual situation of the space mining robot; considering the rugged and changeable actual situation of an asteroid surface, adding a correction network link in the process of establishing a motor transfer function to improve the overall performance of the space mining robot control system; the improved African vulture optimization algorithm is used to optimize and select system parameters; on the basis of the African vulture algorithm, a nonlinear self-adaptive incremental inertia weight factor and a reverse learning competition strategy are added, the local search ability and the global search ability of the algorithm are effectively enhanced, the local optimal phenomenon of the algorithm is avoided, and the running time and the robustness of the algorithm are improved; the improved algorithm is used for optimizing and selecting PID control system parameters, the local optimal problem is avoided, and the control and trajectory tracking effect of the space mining robot system are enhanced.

Description

Technical Field

[0001] This invention relates to a motion control method for a space mining robot, specifically a motion control method for a space mining robot based on an African vulture optimization algorithm, belonging to the field of aerospace technology. Background Technology

[0002] Mineral resources and other usable resources are the material foundation for our development and activities, and a fundamental guarantee for the advancement of global science and technology. With the intensive exploitation of resources and ever-increasing demand, the gradual depletion of mineral resources will become a severe crisis facing humanity in the future.

[0003] Therefore, to address the growing problem of depleted Earth's mineral resources, designing space mining robots and developing and exploring space resources is imperative. For near-Earth asteroids, space mining robots need to adapt to various terrains and complex and changing environments such as near-vacuum, which places high demands on the structure and reliability of their movement.

[0004] Currently, China has made some progress in research on rovers for Mars and the Moon, but research on space mining robots targeting near-Earth asteroids is still in its early stages, with very little research on space mining robots and related control technologies. Given the rugged and soft surface environment of near-Earth asteroids, many current coordinated motion control models are quite complex, with low control accuracy and motion reliability. Therefore, it is necessary to design a simple and efficient control method.

[0005] Based on this, this application proposes a motion control method for space mining robots based on the African vulture optimization algorithm. Summary of the Invention

[0006] The purpose of this invention is to provide a motion control method for a space mining robot based on an African vulture optimization algorithm to solve at least one of the above-mentioned technical problems, thereby enabling the space mining robot to operate with high accuracy on the surface of an asteroid and improving its operational performance and control safety.

[0007] The present invention achieves the above objectives through the following technical solution: a motion control method for a space mining robot based on the African vulture optimization algorithm, comprising a kinematic model of a space mining robot constructed based on a six-wheeled space mining robot, wherein the six-wheeled space mining robot includes a vehicle body, main suspensions are connected to both sides of the vehicle body, a secondary suspension is connected to one end of the main suspension, wheels are rotatably connected to both the main suspension and the secondary suspension, and a control module is installed on the vehicle body;

[0008] Its control method includes the following steps:

[0009] Step 1: Taking the six-wheeled space mining robot as the research object, we first establish the kinematic model of the space mining robot and calculate its position parameters;

[0010] Step 2: Add a calibration network to improve the overall performance of the space mining robot control system;

[0011] Step 3: Based on the African vulture algorithm, propose corresponding improvements to enhance its performance;

[0012] Step 4: Adaptively optimize the control system of the space mining robot using an improved African vulture optimization algorithm;

[0013] Step 5: Using the above optimization methods, design an improved African vulture optimization algorithm to optimize the selection of control system parameters for the space mining robot.

[0014] As a further aspect of the present invention: in step one, the parameters at each position are determined by the following formula:

[0015]

[0016] in, , , These represent the initial angle, x-coordinate, and y-coordinate of the space mining robot, respectively. This indicates the operating speed of the space mining robot;

[0017] The space mining robot's travel time during its movement is Then, its motion distance deviation in the global coordinate system is described as follows:

[0018]

[0019] Angular deviation can be described as:

[0020]

[0021] set up , ;when At that time, it can be obtained

[0022]

[0023]

[0024] After performing the Laplace transform, we can obtain

[0025]

[0026] The relationship between the left and right wheel drive motors of the space mining robot and the linear velocity of the robot itself is as follows:

[0027]

[0028]

[0029] in, This represents the transfer function of the drive motor of the space mining robot. and These represent the control voltages of the left and right motors of the space mining robot, respectively.

[0030] The deviation in the movement distance of the space mining robot can be expressed as:

[0031]

[0032] in, This is the Laplace transform of the control voltage difference between the left and right motors;

[0033] Based on the motor system of the space mining robot, the following functional relationship can be obtained after Laplace transformation:

[0034]

[0035] in, This indicates the voltage used to control the motor. Indicates the resistance of the control circuit. Inductance of the control circuit For circuit current, This represents the back electromotive force caused by the angular velocity of the motor;

[0036] Then by The generated motor output torque for:

[0037]

[0038] in, The electromagnetic time constant of the motor. This is the motor torque coefficient;

[0039] back potential Caused by the rotation of the motor, it is represented as:

[0040]

[0041] in, This is the back EMF coefficient of the motor;

[0042] Through the above derivation and analysis of the motor model, the motor's transfer function can be obtained. for

[0043]

[0044] in, This indicates the wheel radius of the space mining robot.

[0045] As a further aspect of the present invention: in step two, the transfer function of the lead compensation stage is as follows:

[0046]

[0047] in, The maximum leading phase factor, The period during which the maximum leading phase angle occurs;

[0048] In summary, the corrected transfer function of the motor system is as follows:

[0049]

[0050] Therefore, the transfer function of the space mining robot control system is as follows:

[0051]

[0052] The transfer function of the space mining robot control system is established using the above formula, and a PID control system is built based on this.

[0053] As a further aspect of the present invention: in step four, the African vulture optimization algorithm... Based on the lifestyle of African vultures, the foraging and navigation behaviors of African vultures were simulated;

[0054] First, initialize the population, calculate the fitness of all solutions, select the best solution as the best vulture in the first group, select the second best solution as the best vulture in the second group, and move other solutions to the best solutions in the first and second groups. In each fitness iteration, the entire population is recalculated.

[0055]

[0056] In the formula, For the positions of vultures other than the best and second-best vultures; , These are the optimal and second-optimal vulture positions, respectively. , These are the parameters to be measured, which are between 0 and 1, and their sum is 1. To determine the probability of selecting the best vulture; For the fitness of other vultures; use a roulette wheel to obtain the probability of selecting the best solution, and select each best solution for each group;

[0057]

[0058] Calculating the hunger rate of vultures; inspired by the rate at which vultures become full or hungry, mathematical modeling was used, which was also employed to transition from the exploration phase to the development phase; the satiety rate shows a decreasing trend, and the formula simulating this behavior is as follows:

[0059]

[0060]

[0061] in, Indicates the vulture's hunger rate. Indicates the current iteration number. Indicates the maximum number of iterations. It is a random number between -1 and 1 that changes with each iteration. It is a random number between -2 and 2. It is a random number between 0 and 1. When When the value drops below 0, it indicates that the vulture is hungry. A value increasing to 0 indicates that the vultures have eaten their fill. The proportion of the total vulture population is decreasing, and the decrease is greater with each repetition. When When the value is greater than 1, the vulture searches for food in different areas, entering the exploration phase; if... When the value is less than 1, the vulture enters the development phase and searches for food near the optimal solution.

[0062] During the exploration phase, vultures can examine different random areas throughout the population, which can be based on two different strategies and using a method called... The parameter is used to select either strategy. This parameter must be assigned a value between 0 and 1 before the search operation to determine how to use either strategy. To select which strategy to use during the exploration phase, a random number between 0 and 1 needs to be generated. The process is as follows:

[0063]

[0064]

[0065] In the formula, vultures randomly search for food within a random distance of one of the two optimal groups, where... It is the vulture's position vector in the next iteration. It is the vulture's satiety rate obtained in the current iteration. These are areas where vultures randomly move to protect their food from other vultures. This is used as a coefficient vector to increase the random motion, which changes in each iteration, and is expressed using the formula... Obtain, among which It is a random number between 0 and 1; This is the vulture's current vector position:

[0066]

[0067] In the formula, and A random value between 0 and 1. and Indicate the upper and lower bounds of the variable; create a highly random coefficient at the search environment scale to increase diversity and search different search space regions, thereby simulating the movement of vultures;

[0068] After entering the development phase, when When the value is between 0.5 and 1, the vulture population enters the first stage of development. In this first stage, two different rotational flight and encirclement strategies are employed. This is used to determine the choice of each strategy, and the value should be between 0 and 1; at the beginning of this phase, the following is generated: It is a random number between 0 and 1; if the number is greater than or equal to the parameter If the random number is less than the parameter, the siege strategy will be implemented slowly; however, if the random number is less than the parameter... If so, a rotational flight strategy is executed; the process is shown in the following formula:

[0069]

[0070] when At this time, vultures are relatively energetic; when many vultures gather at a food source, they may cause serious conflicts over food acquisition; in such cases, the stronger vultures prefer not to share food with other vultures.

[0071] On the other hand, weaker vultures attempt to tire out healthier vultures by gathering around them and instigating minor conflicts in order to obtain food from the healthier vultures; this step is modeled as follows:

[0072]

[0073] In the formula, These are random numbers between 0 and 1, used to increase the randomness factor; This is the current position vector of the vulture, through which the distance between the current vulture and one of the best vultures in the two groups can be obtained;

[0074] Vultures frequently engage in spiral flight to simulate helical motion; a spiral model has been used for mathematical modeling of spiral flight. In this method, a spiral equation is established between all vultures and one of the two best vultures; spiral flight is expressed as follows:

[0075]

[0076] In the formula, and It is a random number between 0 and 1; and The position of the vultures is updated through calculation;

[0077] if If this happens, then this phase of the algorithm is executed; at the beginning of this phase, the following steps are taken: This is a random number between 0 and 1; the vulture population launched an aggressive struggle involving sieges and competition for food, as shown in the following formula:

[0078]

[0079] All vulture movements toward food sources were examined; sometimes, vultures would go hungry, and there would be intense competition for food, potentially leading to the accumulation of several vulture species at a single food source; this movement of vultures is described by the following formula:

[0080]

[0081]

[0082] Meanwhile, the lead vulture becomes hungry and weak, lacking the energy to fight the other vultures; on the other hand, the other vultures become aggressive in their search for food; moving in different directions toward the lead vulture, the following equation is used to model this movement:

[0083]

[0084] In the formula, The Levy flight mechanism is used to improve the effectiveness of the vulture population in the formula. Its step direction is completely random and isotropic, and the step size is a heavy-tailed distribution.

[0085] As a further aspect of the present invention: In step three, the improvement to the African vulture algorithm specifically includes:

[0086] First, a nonlinear adaptive incremental inertia weight factor is added during the location update process of the vulture population. The calculation formula is as follows:

[0087]

[0088] in, and The selection factors for the initial optimal and suboptimal vultures. for Random numbers;

[0089] Subsequently, a nonlinear adaptive incremental inertia weighting factor was introduced into the vulture position update formula during the exploration and development phases. The process is shown in the following formula:

[0090]

[0091]

[0092]

[0093] The position of the vulture is optimized and updated using the above formula;

[0094] Meanwhile, a reverse learning competition strategy is added to the output position of the vulture in each iteration. The reverse learning solution is obtained by using the optimal output solution each time. The calculation formula is as follows:

[0095]

[0096] in, for Random numbers;

[0097] Through calculation and The population fitness of both species is used to optimize the optimal position of the vulture in this iteration.

[0098] The beneficial effects of this invention are as follows: First, a kinematic model of the space mining robot is established based on its actual conditions. Considering the rugged and varied surface of the asteroid, a correction network is added during the establishment of the motor transfer function to improve the overall performance of the space mining robot control system.

[0099] Meanwhile, unlike the manual selection of parameters in traditional control systems, this invention uses an improved African vulture optimization algorithm to optimize the selection of system parameters. Based on the African vulture algorithm, this invention adds a nonlinear adaptive incremental inertia weight factor and a reverse learning competition strategy, which effectively enhances the algorithm's local and global search capabilities, avoids local optima, and improves the algorithm's running time and robustness. By optimizing the selection of PID control system parameters through the improved algorithm, the local optima problem is avoided, and the control and trajectory tracking effects of the space mining robot system are enhanced. Attached Figure Description

[0100] Figure 1 The subject of this invention is a three-dimensional model of a space mining robot.

[0101] Figure 2 This is a schematic diagram of the kinematic model of the space mining robot of the present invention;

[0102] Figure 3 This is a schematic diagram of the motor system of the space mining robot of the present invention;

[0103] Figure 4 This is a structural diagram of the motion control method for a space mining robot based on the African vulture optimization algorithm of the present invention;

[0104] Figure 5 This is a flowchart of the basic African vulture algorithm in the space mining robot control system of the present invention;

[0105] Figure 6 This is a schematic diagram illustrating the food competition behavior of a vulture population in the African vulture optimization algorithm of this invention;

[0106] Figure 7 This is a flowchart of the improved African vulture algorithm in the space mining robot control system of the present invention;

[0107] Figure 8 This is a simulation diagram of the convergence curve of the first test function in this embodiment of the invention under multiple intelligent optimization algorithms;

[0108] Figure 9 This is a simulation diagram of the convergence curve of the second test function under multiple intelligent optimization algorithms in an embodiment of the present invention;

[0109] Figure 10 This is a simulation diagram of the convergence curve of the third test function in this embodiment of the invention under multiple intelligent optimization algorithms;

[0110] Figure 11 This is a simulation diagram of the convergence curve of the fourth test function in this embodiment of the invention under multiple intelligent optimization algorithms.

[0111] In the diagram: 1. Vehicle body, 2. Main suspension, 3. Subsidiary suspension, 4. Wheels, 5. Control module. Detailed Implementation

[0112] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0113] Example 1

[0114] like Figures 1 to 7 As shown, the motion control method for a space mining robot based on the African vulture optimization algorithm includes a kinematic model of a space mining robot constructed based on a six-wheeled space mining robot. The six-wheeled space mining robot includes a vehicle body 1, with main suspensions 2 connected to both sides of the vehicle body 1, and a secondary suspension 3 connected to one end of the main suspensions 2. Wheels 4 are rotatably connected to both the main suspensions 2 and the secondary suspensions 3. A control module 5 is installed on the vehicle body 1.

[0115] Its control method includes the following steps:

[0116] Step 1: Taking the six-wheeled space mining robot as the research object, we first establish the kinematic model of the space mining robot and calculate its position parameters;

[0117] Step 2: Add a calibration network to improve the overall performance of the space mining robot control system;

[0118] Step 3: Based on the African vulture algorithm, propose corresponding improvements to enhance its performance;

[0119] Step 4: Adaptively optimize the control system of the space mining robot using an improved African vulture optimization algorithm;

[0120] Step 5: Using the above optimization methods, design an improved African vulture optimization algorithm to optimize the selection of control system parameters for the space mining robot.

[0121] Example 2

[0122] In addition to all the technical features included in Embodiment 1, this embodiment also includes:

[0123] In step one, the parameters at each position are determined by the following formula:

[0124]

[0125] in, , , These represent the initial angle, x-coordinate, and y-coordinate of the space mining robot, respectively. This indicates the operating speed of the space mining robot;

[0126] The space mining robot's travel time during its movement is Then, its motion distance deviation in the global coordinate system can be described as:

[0127]

[0128] Angular deviation can be described as:

[0129]

[0130] set up , ;when At that time, it can be obtained

[0131]

[0132] After performing the Laplace transform, we can obtain

[0133]

[0134] The relationship between the left and right wheel drive motors of the space mining robot and the linear velocity of the robot itself is as follows:

[0135]

[0136] in, This represents the transfer function of the drive motor of the space mining robot. and These represent the control voltages of the left and right motors of the space mining robot, respectively.

[0137] The deviation in the movement distance of the space mining robot can be expressed as:

[0138]

[0139] in, This is the Laplace transform of the control voltage difference between the left and right motors;

[0140] Based on the motor system of the space mining robot, the following functional relationship can be obtained after Laplace transformation:

[0141]

[0142] in, This indicates the voltage used to control the motor. Indicates the resistance of the control circuit. Inductance of the control circuit For circuit current, This represents the back electromotive force caused by the angular velocity of the motor;

[0143] Then by The generated motor output torque for:

[0144]

[0145] in, The electromagnetic time constant of the motor. This is the motor torque coefficient;

[0146] back potential Caused by the rotation of the motor, it is represented as:

[0147]

[0148] in, This is the back EMF coefficient of the motor;

[0149] Through the above derivation and analysis of the motor model, the motor's transfer function can be obtained. for

[0150]

[0151] in, This indicates the wheel radius of the space mining robot.

[0152] In step two, the transfer function of the lead compensation stage is as follows:

[0153]

[0154] in, The maximum leading phase factor, The period during which the maximum leading phase angle occurs;

[0155] In summary, the corrected transfer function of the motor system is as follows:

[0156]

[0157] Therefore, the transfer function of the space mining robot control system is as follows:

[0158]

[0159] The transfer function of the space mining robot control system is established using the above formula, and a PID control system is built based on this.

[0160] In step four, the African vulture optimization algorithm... Based on the lifestyle of African vultures, the foraging and navigation behaviors of African vultures were simulated;

[0161] First, initialize the population, calculate the fitness of all solutions, select the best solution as the best vulture in the first group, select the second best solution as the best vulture in the second group, and move other solutions to the best solutions in the first and second groups. In each fitness iteration, the entire population is recalculated.

[0162]

[0163] In the formula, For the positions of vultures other than the best and second-best vultures; , These are the optimal and second-optimal vulture positions, respectively. , These are the parameters to be measured, which are between 0 and 1, and their sum is 1. To determine the probability of selecting the best vulture; For the fitness of other vultures; use a roulette wheel to obtain the probability of selecting the best solution, and select each best solution for each group;

[0164]

[0165] Calculating the hunger rate of vultures; inspired by the rate at which vultures become full or hungry, mathematical modeling was used, which was also employed to transition from the exploration phase to the development phase; the satiety rate shows a decreasing trend, and the formula simulating this behavior is as follows:

[0166]

[0167] in, Indicates the vulture's hunger rate. Indicates the current iteration number. Indicates the maximum number of iterations. It is a random number between -1 and 1 that changes with each iteration. It is a random number between -2 and 2. It is a random number between 0 and 1. When When the value drops below 0, it indicates that the vulture is hungry. A value increasing to 0 indicates that the vultures have eaten their fill. The proportion of the total vulture population is decreasing, and the decrease is greater with each repetition. When When the value is greater than 1, the vulture searches for food in different areas, entering the exploration phase; if... When the value is less than 1, the vulture enters the development phase and searches for food near the optimal solution.

[0168] During the exploration phase, vultures can examine different random areas throughout the population, which can be based on two different strategies and using a method called... The parameter is used to select either strategy. This parameter must be assigned a value between 0 and 1 before the search operation to determine how to use either strategy. To select which strategy to use during the exploration phase, a random number between 0 and 1 needs to be generated. The process is as follows:

[0169]

[0170]

[0171] In the formula, vultures randomly search for food within a random distance of one of the two optimal groups, where... It is the vulture's position vector in the next iteration. It is the vulture's satiety rate obtained in the current iteration. These are areas where vultures randomly move to protect their food from other vultures. This is used as a coefficient vector to increase the random motion, which changes in each iteration, and is expressed using the formula... Obtain, among which It is a random number between 0 and 1; This is the vulture's current vector position:

[0172]

[0173] In the formula, and A random value between 0 and 1. and Indicate the upper and lower bounds of the variable; create a highly random coefficient at the search environment scale to increase diversity and search different search space regions, thereby simulating the movement of vultures;

[0174] After entering the development phase, when When the value is between 0.5 and 1, the vulture population enters the first stage of development. In this first stage, two different rotational flight and encirclement strategies are employed. This is used to determine the choice of each strategy, and the value should be between 0 and 1; at the beginning of this phase, the following is generated: It is a random number between 0 and 1; if the number is greater than or equal to the parameter If the random number is less than the parameter, the siege strategy will be implemented slowly; however, if the random number is less than the parameter... If so, a rotational flight strategy is executed; the process is shown in the following formula:

[0175]

[0176] when At this time, vultures are relatively energetic; when many vultures gather at a food source, they may cause serious conflicts over food acquisition; in such cases, the stronger vultures prefer not to share food with other vultures.

[0177] On the other hand, weaker vultures attempt to tire out healthier vultures by gathering around them and instigating minor conflicts in order to obtain food from the healthier vultures; this step is modeled as follows:

[0178]

[0179] In the formula, It is a random number between 0 and 1, used to increase the randomness coefficient. This is the current position vector of the vulture, through which the distance between the current vulture and one of the best vultures in the two groups can be obtained;

[0180] Vultures frequently engage in spiral flight to simulate helical motion; a spiral model has been used for mathematical modeling of spiral flight. In this method, a spiral equation is established between all vultures and one of the two best vultures; spiral flight is expressed as follows:

[0181]

[0182] In the formula, and It is a random number between 0 and 1; and The position of the vultures is updated through calculation;

[0183] if If this happens, then this phase of the algorithm is executed; at the beginning of this phase, the following steps are taken: This is a random number between 0 and 1; the vulture population launched an aggressive struggle involving sieges and competition for food, as shown in the following formula:

[0184]

[0185] All vulture movements toward food sources were examined; sometimes, vultures would go hungry, and there would be intense competition for food, potentially leading to the accumulation of several vulture species at a single food source; this movement of vultures is described by the following formula:

[0186]

[0187] Meanwhile, the lead vulture becomes hungry and weak, lacking the energy to fight the other vultures; on the other hand, the other vultures become aggressive in their search for food; moving in different directions toward the lead vulture, the following equation is used to model this movement:

[0188]

[0189] In the formula, The Levy flight mechanism is used to improve the effectiveness of the vulture population in the formula. Its step direction is completely random and isotropic, and the step size is a heavy-tailed distribution.

[0190] Step four, the improvements to the African vulture algorithm, specifically include:

[0191] First, a nonlinear adaptive incremental inertia weight factor is added during the location update process of the vulture population. The calculation formula is as follows:

[0192]

[0193] in, and The selection factors for the initial optimal and suboptimal vultures. for Random numbers;

[0194] Subsequently, a nonlinear adaptive incremental inertia weighting factor was introduced into the vulture position update formula during the exploration and development phases. The process is shown in the following formula:

[0195]

[0196]

[0197] The position of the vulture is optimized and updated using the above formula;

[0198] Meanwhile, a reverse learning competition strategy is added to the output position of the vulture in each iteration. The reverse learning solution is obtained by using the optimal output solution each time. The calculation formula is as follows:

[0199]

[0200] in, for Random numbers;

[0201] Through calculation and The population fitness of both species is used to optimize the optimal position of the vulture in this iteration.

[0202] Example 3

[0203] like Figure 8 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes: selecting commonly used benchmark functions. To verify the performance of the improved African vulture optimization algorithm, the theoretical optimal value of the function is 0, and the search region of the function is defined as follows. The performance of the improved African Vulture Optimization Algorithm (AVA) was tested by comparing it with the basic AVA, as well as classic algorithms such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (Grizzly Optimization), and Genetic Algorithm (GA). To ensure fairness, the population size for each algorithm was set to 30, and the maximum number of iterations was set to 300.

[0204] It can be seen that the improved African vulture optimization algorithm has a faster optimization speed compared to other algorithms.

[0205] Example 4

[0206] like Figure 9 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes: selecting commonly used benchmark functions. To verify the performance of the improved African vulture optimization algorithm, the theoretical optimal value of the function is 0, and the search region of the function is defined as follows. The comparison algorithm is the same as the previous embodiment, with the population size set to 30 for each algorithm and the maximum number of iterations set to 300.

[0207] It can be seen that the improved African vulture optimization algorithm has a faster optimization speed compared to other algorithms.

[0208] Example 5

[0209] like Figure 10 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes: selecting commonly used benchmark functions. To verify the performance of the improved African vulture optimization algorithm, the theoretical optimal value of the function is 0, and the search region of the function is defined as follows. The comparison algorithm is the same as the previous embodiment, with the population size set to 30 for each algorithm and the maximum number of iterations set to 300.

[0210] It can be seen that the improved African vulture optimization algorithm has a faster optimization speed compared to other algorithms.

[0211] Example 6

[0212] like Figure 11 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes: selecting commonly used benchmark functions. To verify the performance of the improved African vulture optimization algorithm, the theoretical optimal value of the function is 0, and the search region of the function is defined as follows. The comparison algorithm is the same as the previous embodiment, with the population size set to 30 for each algorithm and the maximum number of iterations set to 300.

[0213] It can be seen that the improved African vulture optimization algorithm has a faster optimization speed compared to other algorithms.

[0214] In summary, the above embodiments show that the improved African vulture optimization algorithm can find the theoretical optimal value of the test function, and the optimization speed is significantly improved compared with other algorithms. Moreover, the optimization ability satisfies multiple functions, and it has good stability, with obvious advantages.

[0215] Therefore, using the improved African vulture algorithm will have a very good effect on optimizing the selection of parameters for the control system of space mining robots.

[0216] Working principle: First, a kinematic model of the space mining robot is established based on its actual conditions. Considering the rugged and varied surface of the asteroid, a correction network is added to improve the overall performance of the space mining robot's control system during the establishment of the motor transfer function.

[0217] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0218] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A motion control method for a space mining robot based on an African vulture optimization algorithm, characterized in that: This includes a kinematic model of a space mining robot based on a six-wheeled space mining robot; The six-wheeled space mining robot includes a vehicle body (1), with main suspension (2) connected to both sides of the vehicle body (1), and a secondary suspension (3) connected to one end of the main suspension (2). Wheels (4) are rotatably connected to both the main suspension (2) and the secondary suspension (3). A control module (5) is installed on the vehicle body (1). The motion control method for the space mining robot includes the following steps: Step 1: Taking the six-wheeled space mining robot as the research object, establish the kinematic model of the space mining robot and calculate its position parameters; Step 2: Add a calibration network to improve the overall performance of the space mining robot control system; Step 3: Based on the African vulture algorithm, propose corresponding improvements to enhance its performance; Step 4: Adaptively optimize the control system of the space mining robot using an improved African vulture optimization algorithm; The improvements to the African vulture algorithm in step three specifically include: First, a nonlinear adaptive incremental inertia weight factor is added during the location update process of the vulture population. The calculation formula is as follows: ； in, and The selection factors for the initial optimal and suboptimal vultures. for Random numbers; Subsequently, a nonlinear adaptive incremental inertia weighting factor was introduced into the vulture position update formula during the exploration and development phases. The process is shown in the following formula: ； ； ； The position of the vulture is optimized and updated using the above formula; Meanwhile, a reverse learning competition strategy is added to the output position of the vulture in each iteration. The reverse learning solution is obtained by using the optimal output solution each time. The calculation formula is as follows: ； in, for Random numbers; Through calculation and The population fitness of both species is used to optimize the optimal position for the vulture in this iteration. Step 5: Using the above optimization methods, design an improved African vulture optimization algorithm to optimize the selection of control system parameters for the space mining robot.

2. The motion control method for a space mining robot according to claim 1, characterized in that: In step one, the parameters at each position are determined by the following formula: ； ； ； in, , , These represent the initial angle, x-coordinate, and y-coordinate of the space mining robot, respectively. This indicates the operating speed of the space mining robot; The space mining robot's travel time during its movement is Then, its motion distance deviation in the global coordinate system is described as follows: ； Angular deviation is described as follows: ； set up , ;when At that time, it can be obtained ； ； After performing the Laplace transform, we can obtain ； The relationship between the left and right wheel drive motors of the space mining robot and the linear velocity of the robot itself is as follows: ； ； in, This represents the transfer function of the drive motor of the space mining robot. and These represent the control voltages of the left and right motors of the space mining robot, respectively. The deviation in the movement distance of the space mining robot is expressed as: ； in, This is the Laplace transform of the control voltage difference between the left and right motors; Based on the motor system of the space mining robot, the following functional relationship can be obtained after Laplace transformation: ； in, This indicates the voltage used to control the motor. Indicates the resistance of the control circuit. Inductance of the control circuit For circuit current, This represents the back electromotive force caused by the angular velocity of the motor; Then by The generated motor output torque for: ； in, The electromagnetic time constant of the motor. This is the motor torque coefficient; back potential Caused by the rotation of the motor, it is represented as: ； in, This is the back EMF coefficient of the motor; Through the above derivation and analysis of the motor model, the motor's transfer function is obtained. for ； in, Represents the mechanical time constant of the motor. This indicates the wheel radius of the space mining robot.

3. The motion control method for a space mining robot according to claim 2, characterized in that: In step two, the transfer function of the lead compensation stage is as follows: ； in, The maximum leading phase factor, The period during which the maximum leading phase angle occurs; In summary, the corrected transfer function of the motor system is as follows: ； Therefore, the transfer function of the space mining robot control system is as follows: ； The transfer function of the space mining robot control system is established using the above formula, and a PID control system is built based on this.

4. The motion control method for a space mining robot according to claim 1, characterized in that: In step four: First, initialize the population, calculate the fitness of all solutions, select the best solution as the best vulture in the first group, select the second best solution as the best vulture in the second group, and move other solutions to the best solutions in the first and second groups. In each fitness iteration, the entire population will be recalculated. ； In the formula, For the positions of vultures other than the best and second-best vultures; , These are the optimal and second-optimal vulture positions, respectively. , These are the parameters to be measured, which are between 0 and 1, and their sum is 1. To determine the probability of selecting the best vulture; For the fitness of other vultures; use a roulette wheel to obtain the probability of selecting the best solution, and select each best solution for each group; ； Calculating the hunger rate of vultures; inspired by the rate at which vultures become full or hungry, mathematical modeling was used, which was also employed to transition from the exploration phase to the development phase; the satiety rate shows a decreasing trend, and the formula simulating this behavior is as follows: ； ； in, Indicates the vulture's hunger rate. Indicates the current iteration number. Indicates the maximum number of iterations. It is a random number between -1 and 1 that changes with each iteration. It is a random number between -2 and 2. It is a random number between 0 and 1. When the value drops below 0, it indicates that the vulture is hungry. When the value increases to 0, it indicates that the vultures are full, and the proportion of vultures in the total population is decreasing. Moreover, with each repetition, the decrease is more significant. When the value is greater than 1, the vulture searches for food in different areas, entering the exploration phase; if... When the value is less than 1, the vulture enters the development phase and searches for food near the optimal solution location. During the exploration phase, vultures throughout the population examine different random areas, based on two different strategies and using a method called... The parameter is used to select either strategy. This parameter must be assigned a value between 0 and 1 before the search operation to determine how to use either strategy. To select which strategy to use during the exploration phase, a random number between 0 and 1 needs to be generated. The process is as follows: ； In the formula, vultures randomly search for food within a random distance of one of the two optimal groups, where... It is the vulture's position vector in the next iteration. It is the vulture satiety rate obtained using the formula in the current iteration. These are areas where vultures randomly move to protect their food from other vultures. This is used as a coefficient vector to increase the random motion, which changes in each iteration, and is expressed using the formula... Obtain, among which It is a random number between 0 and 1; This is the vulture's current vector position: ； In the formula, and A random value between 0 and 1. and Indicate the upper and lower bounds of the variable; create a highly random coefficient at the search environment scale to increase diversity and search different search space regions, thereby simulating the movement of vultures; After entering the development phase, when When the value is between 0.5 and 1, the vulture population enters the first stage of development, in which two different rotational flight and encirclement strategies are implemented. This is used to determine the choice of each strategy, and the value should be between 0 and 1; at the beginning of this phase, the following is generated: It is a random number between 0 and 1; if the number is greater than or equal to the parameter If the random number is less than the parameter, the siege strategy will be implemented slowly; however, if the random number is less than the parameter... If so, a rotational flight strategy is executed; the process is shown in the following formula: ； when At this time, vultures have relatively abundant energy; when many vultures gather at one food source, they may cause serious conflicts over food acquisition; in such cases, the stronger vultures prefer not to share food with other vultures. On the other hand, weaker vultures attempt to tire out healthier vultures by gathering around them and instigating minor conflicts in order to obtain food from the healthier vultures; this step is modeled as follows: ； In the formula, These are random numbers between 0 and 1, used to increase the randomness factor. This is the current position vector of the vulture, through which the distance between the current vulture and one of the best vultures in the two groups can be obtained; Vultures frequently engage in spiral flight to simulate helical motion; a spiral model has been used for mathematical modeling of spiral flight, in which a spiral equation is established between all vultures and one of the two best vultures, and spiral flight is expressed as follows: ； ； ； In the formula, and It is a random number between 0 and 1; and The position of the vultures is updated through calculation; if If this happens, then this phase of the algorithm is executed; at the beginning of this phase, the following steps are taken: This is a random number between 0 and 1; the vulture population launched an aggressive struggle involving sieges and competition for food, as shown in the following formula: ； All vulture movements toward food sources were examined; sometimes, vultures would go hungry, and there would be intense competition for food, potentially leading to the accumulation of several vulture species at a single food source; this movement of vultures is described by the following formula: ； ； Meanwhile, the lead vulture becomes hungry and weak, lacking the energy to fight the other vultures; on the other hand, the other vultures become aggressive in their search for food; moving in different directions toward the lead vulture, the following equation is used to model this movement: ； In the formula, The Levi flight mechanism is used to improve the effectiveness of the vulture population in the formula. Its step direction is completely random and isotropic, and the step size is a heavy-tailed distribution.

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