## 3 Multi-Objective Optimization of System Construction
### A Multi-Objective Optimization Method for System Construction Based on a Genetic Algorithm
#### 3.2.2 Multi-Objective Optimization of Mobile Robot System Construction  
Most of the existing mobile robot system construction relies on human experience, and make sufficient allowance in the accuracy, cost and efficiency of each component. The constructed scheme is not the best. On this basis, the current paper proposes a multi-objective optimization method for mobile robot system construction based on NSGA-II, which is mainly composed of a genetic algorithm population by analyzing the influence rules among the precision, cost and time of the components of omnidirectional mobile platform, manipulator, milling end-effector, vision measurement system and flexible position changing system, combined with the actual system construction data. It can be understood that there are five types of components, and each type of component has multiple specific components according to the relationship between precision, cost and time. One specific component is selected from the five types of components to put in the system, so that the system has the smallest accumulation of errors, the lowest total cost and the shortest time spent on all the installation surface milling. The multi-objective optimization flow chart of the mobile robot system construction based on NSGA-II is shown in Figure 5.  
Figure 4: NSGA-II algorithm flow chart. The concrete steps of constructing the multi-objective optimization method for the mobile robot system based on NSGA-II are as follows:  
1. Measured data acquisition: the positioning accuracy of the omnidirectional mobile platform is related to the measurement method, and the positioning accuracy of laser navigation is generally 3 mm. Based on two-dimensional code visual measurement positioning, positioning accuracy is generally 1 mm. The navigation and positioning accuracy of a high precision spatial locator (iGPS) is generally 0.5 mm. Composite measurements can ensure higher positioning accuracy, but the cost is also higher. At the same time, the higher the precision, the longer the time to adjust and control. There are many ways to improve the positioning accuracy of the manipulator, such as correcting D-H parameters to improve the error compensation. The grating two-stage closed-loop is used to improve the joint angle accuracy and then the robot positioning accuracy. The higher the accuracy, the higher the cost, and the smaller the working space. The accuracy of the vision measurement system, milling end-effector and flexible position changing system are also positively correlated with the cost. 2. Initialize component set: According to step (1), components are classified and selected according to accuracy, cost and time, and multiple groups of actual data constitute the initial population of the genetic algorithm. 3. Coding and generating population: the number of system components is represented by \(m\), and the number of specific components in the component is represented by \(n\). Rows and columns of the matrix are used for coding, that is, columns represent \(m\) components and rows represent \(n\) specific components. Then the coding of the constructed system can be obtained and corresponds to the matrix of precision, cost and time. 4. Fitness calculation: there are three goals. The first goal is the sum of the error accumulation of each component in the milling process, the second goal is the sum of the  
Figure 5: Construction of the multi-objective optimization process for the mobile robot system based on NSGA-II. cost of each component, and the third goal is the sum of the working time of each component. Its fitness function is shown in Equation (1). 5. Update population and non-inferior solution set: For the populations processed by the above steps, the non-inferior solution set constructed by the optimal system can be updated through the NSGA-II genetic algorithm operations such as fast non-dominant sorting, crowding calculation, selection, crossover and mutation, and the updated new population can be used for the next generation calculation. 6.