## 5 Optimal Collision-Avoidance Trajectory Planning Based on dsRNA Splicing Evolutionary Algorithm
### Trajectory Planning Simulation for a Space Robot  
Since the particularity of the space robot, both simulation and ground experiment are the important research methods. The proposed trajectory planning strategy of a dual-arm free-floating space robot under microgravity uses a common simulation platform based on MATLAB2016b to carry out numerical simulation tests, because it is difficult for the underwater equipment to simulate the perturbation of gravity parameters. It is worth noting that, by referring to the aviation development history of various countries and actual space experiment experiences, the simulation results will be closer to the actual experiment and more able to guide the actual experiment if the actual space environment is simulated more perfect. Hence, gravity gradient perturbation will be considered in this simulation, and it will be preliminarily simulated in the form of parametric perturbation (\(1\pm 20\%sin(t)\)). Two current methods and an unoptimized method in (14) are selected as comparison to illustrate the advantages of the proposed trajectory planning method for the dual-arm free-floating space robot, and the constant parameters are all set the same. Line a is the proposed method, line b is the predefined-time trajectory planning method  
Figure 3: The system state and input curve of (2). Figure 2: The fitness curve of dsRNA splicing evolutionary algorithm. for space robot [22], line c is the IGA-based trajectory planning method for space robot without pose-feedback [19], line d is the method given by Section 4 and method d appears as a foil method. The initial parameter of trajectory planning for a space robot is given by Table 2. The parameters of the proposed dsRNA splicing evolutionary algorithm are shown in Table 3. The structure parameters of the space robot are given by the previous study [16]. The fitness curve of the proposed trajectory planning method based on dsRNA splicing evolutionary algorithm is given in Figure 4, the optimal parameters \(m=0.1361\) and \(\Delta=17.1750\). As shown in Figure 5, the space robot system successfully completed the obstacle avoidance task in 16 s. The danger distance is set at 0.15 (m) and the collision distance is 0.1 (m). According to the first curves figure of Figure 6, it is clear that the methods a, b can make the position errors of end-effector converge to nearly zero within bounded time, but the method c cannot eliminate the pose errors. Meanwhile, as shown in the second curves figure of Figure 6, the methods a, b can also make the attitude errors of end-effector converge to nearly zero within bounded time, but the method c can also not. Furthermore, According to Figure 7, the methods a, b can even make the pose error caused by dynamic coupling converge to nearly zero within fixed-time, but method c can also not. The essential reason for the large error of method c is that the optimal strategy can only find out the better parameter with the smaller error and smoother motion trajectory. On the other hand, the motion error cannot be eliminated by the planning algorithm without pose feedback. The related convergence performance data of system is recorded in Tables 4 and 5, which refers to the subfigures (a) and (b) in Figures 6 and 7. Compared with method b and