Adaptive chicken swarm optimization algorithm for identifying structural parameters of 6-DOF mechanical arm

Question

1. How do structural parameter errors impact measurement results?

2. What are the commonly used modeling methods for a 6-DOF mechanical arm?

3. How to identify structural parameters?

4. What is the role of each chicken in CSO?

Accepted Answer

Structural parameter errors in the mechanical arm can lead to observable errors in measurement results due to their nonlinear impact. Tiny errors in certain structural parameters may significantly affect the accuracy of measurements. Therefore, it is crucial to periodically identify and monitor the structural parameters of the mechanical arm to ensure reliable and precise measurements. Various researchers have proposed different methods to identify these parameters, but most rely on precise components, which can be costly and sensitive to environmental factors. A novel approach combines single-point conical hole repeatability experiments with intelligent optimization algorithms to identify structural parameters effectively.

Accepted Answer

The commonly used modeling methods for a 6-DOF mechanical arm are the DH modeling method and the MDH modeling method. The DH modeling method is widely used due to its clear physical meaning and simple mathematical structure. However, it has limitations when dealing with links with parallel or nearly parallel relationships, leading to singularities in the mechanical arm. To overcome this, the MDH modeling method is employed. The MDH model establishes a coordinate system based on the MDH rule, using translation and rotation transformations to describe the relative pose relationship between links. Additionally, the probe offset is considered as the fifth set of structural parameters in the MDH model, ensuring accurate representation of the mechanical arm's kinematics.

Accepted Answer

To identify structural parameters, a single-point conical hole repeatability experiment is proposed. This experiment involves acquiring N sets of coordinate values and corresponding joint angles, resulting in 3N equations derived from equation (4). However, these equations are implicit and challenging to solve. The experiment uses the average of N sets of probe end's coordinates ( , , ) as a replacement for the actual coordinate, denoted as 1Nk mean iNk mean iNk mean ixxN yyN zzN. The objective function (9) is established using the mean and standard deviation of kE to indicate the single-point repeatability error of the mechanical arm. The closer the structural parameter X is to the actual structural parameter, the smaller the objective function value. The goal is to find *X or approach the solution of *X using intelligent optimization algorithms like the Adaptive Chicken Swarm Optimization Algorithm.

Accepted Answer

In CSO, each chicken has a specific role based on its fitness value. The rooster has the strongest adaptability and a leading advantage, while the hens have the largest number and mainly learn from the rooster. The chicks have the weakest adaptability and follow their mother, conducting local searches around her. The motion equations for each chicken type are also provided, with the rooster's equation involving its position, fitness value, and random selection. The hen's equation includes random selection from the entire population and the rooster's position. The chick's equation involves its mother's position and a following coefficient parameter, G, which determines the speed of population evolution.

Accepted Answer

The improved rooster's motion equation optimizes in unknown environments by learning from both itself and the optimal individual ( ) and X best j t. It introduces inertia weights 1 w and 2 w into the modified equation. Initially, the optimal individual is essential for the immature rooster, allowing rapid fitness improvement and movement towards the optimal solution. As the algorithm progresses, roosters gradually approach ( ) and X best j t. To prevent premature convergence, the self-information of each rooster is utilized by continuously increasing 1 w and gradually decreasing 2 w. This wider area optimization helps the rooster lead its subgroups to globally optimize in unknown environments.

Accepted Answer

The mCSO algorithm consists of seven steps. Step 1 involves initializing parameters and chickens' positions. Step 2 calculates the fitness of each chicken and records the optimal fitness value and its corresponding individual. Step 3 checks if a condition is met, and if so, sorts fitness values and divides them into subgroups. Step 4 updates the rooster's position based on equation (18). Step 5 updates the hen's position using equations (13), (19), and (20). Step 6 updates the chick's position using equation (21). Step 7 determines if the maximum iteration count has been reached, and if not, the algorithm proceeds to Step 2. This iterative process continues until the maximum iteration count is reached or the optimal solution is found.

Accepted Answer

CSO and mCSO use joint angle data to identify structural parameters of the mechanical arm. They have 100 chickens each, with a 2:6:2 ratio of roosters, hens, and chicks. The algorithm iterates up to 5000 times and considers boundary values for structural parameters. After optimization, the structural parameters are transmitted to the controller. Single-point conical hole repeatability experiments are conducted before and after identification to compare the results. The single-point repeatability error decreases significantly after mCSO identification, as shown in Fig 6 and Tables 5-8.

Accepted Answer

CSO has the advantage of dynamically updating subgroups and evolving multiple subgroups. However, it suffers from low convergence accuracy when dealing with complicated problems. This limitation led to the development of mCSO as a response to CSO's deficiencies. mCSO aims to improve the accuracy and efficiency of solving complex problems, particularly in the context of identifying structural parameters of mechanical arms. By combining mCSO with the single-point conical hole repeatability experiment, a new method is proposed to enhance the identification process. This approach offers a more practical and effective solution for determining the structural parameters of mechanical arms, reducing single-point repeatability errors and providing convenience in industrial settings.

Adaptive chicken swarm optimization algorithm for identifying structural parameters of 6-DOF mechanical arm

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Most frequently asked questions

1. How do structural parameter errors impact measurement results?

2. What are the commonly used modeling methods for a 6-DOF mechanical arm?

3. How to identify structural parameters?

4. What is the role of each chicken in CSO?

5. How does the improved rooster's motion equation optimize in unknown environments?

6. What are the steps involved in mCSO algorithm?

7. How do CSO and mCSO identify structural parameters?

8. What are the defects of CSO in solving complex problems?

References

Lecture Notes in Artificial Intelligence

Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems

Kinematic parameter estimation technique for calibration and repeatability improvement of articulated arm coordinate measuring machines

A self-centering active probing technique for kinematic parameter identification and verification of articulated arm coordinate measuring machines

Comparison of accuracy of virtual articulated arm coordinate measuring machine based on different metrological models

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