## 7 Conclusions  
This paper presents a novel dsRNA splicing evolutionary algorithm and a novel smooth, fixed-time and high-precision obstacle-avoidance trajectory planning method for a dual-arm free-floating space robot, and the proposed novel evolutionary algorithm achieved 7.56-30.40% performance improvement over benchmark methods. According to the idea of pose feedback trajectory planning based on the low-input fixed-time stability system, the planned joint angular velocity trajectory can make the pose errors of manipulator and base converge to nearly zero within fixed-time \(T_{c}\). The novel evolutionary algorithm can imitate dsRNA virus gene splicing evolution to optimize the parameter of the fixed-time obstacle-avoidance trajectory planning algorithm to make the planned joint angular velocity trajectory both smoother and more accurate than the existing methods. Compared with other evolutionary algorithms, this study mainly realizes the optimization of a semi-global search strategy described by Remark 3, which is more likely to find a solution that is close to the global optimal than the optimal strategy searching directly in the full domain. In addition, the essence of the proposed algorithm to achieve error-free trajectory planning is the addition of fast pose feedback strategy. Compared with the existing optimal trajectory planning strategy without feedback, this method can eliminate the error condition, thus essentially reducing the amount of calculation and improving the accuracy. In the future, we will apply the dsRNA splicing evolutionary algorithm-based trajectory planning strategy to plant robotics, and the encoding method of this novel evolutionary algorithm will be improved so that it can be used for a biological information detection, which is valuable.  
Conceptualization, J.Y. and W.Y.; methodology, J.Y. and W.Y.; software, J.Y., W.Y. and Q.L.; validation, J.Y., W.Y., Q.L., Y.L. and Y.Z.; investigation, J.Y. and W.Y.; resources, Y.Z.; data curation, J.Y., W.Y. and Q.L.; writing--original draft preparation, J.Y. and W.Y.; writing--review and editing, J.Y., W.Y., Q.L., Y.L. and Y.Z.; visualization, J.Y. and W.Y.; supervision, J.Y., W.Y. and Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China: Interaction of RNA aptamer \(3 wj-4\times\) bro as a gene marker with target gene expression(Grant No. 32171457); Major Special Project of the Science and Technology on Intelligent Manufacturing and Robot (Grant No. 2019 ZDZX0019).  
No. 2019 ZDZX0019  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method a** & **Method b** & **Method c** \\ \hline \(\theta_{1}^{1}\) (\({}^{\circ}\)) & 72.60 & 52.08 & \(-11.88\) \\ \(\theta_{2}^{1}\) (\({}^{\circ}\)) & 24.29 & 24.29 & 24.31 \\ \(\theta_{3}^{1}\) (\({}^{\circ}\)) & \(-29.32\) & \(-29.32\) & \(-29.35\) \\ \(\theta_{4}^{1}\) (\({}^{\circ}\)) & \(-36.22\) & \(-31.32\) & 1.49 \\ \(\theta_{5}^{1}\) (\({}^{\circ}\)) & \(-24.57\) & \(-13.87\) & 7.35 \\ \(\theta_{6}^{1}\) (\({}^{\circ}\)) & 48.39 & 40.46 & \(-1.06\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The maximum joint angular velocity components of Arm-1 in three comparing methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method a** & **Method b** & **Method c** \\ \hline \(\theta_{2}^{1}\) (\({}^{\circ}\)) & 49.12 & 39.42 & \(-6.042\) \\ \(\theta_{2}^{2}\) (\({}^{\circ}\)) & \(-32.39\) & \(-32.45\) & \(-32.45\) \\ \(\theta_{3}^{2}\) (\({}^{\circ}\)) & 37.97 & 30.64 & \(-30.08\) \\ \(\theta_{4}^{2}\) (\({}^{\circ}\)) & 114.25 & 161.15 & 30.8 \\ \(\theta_{5}^{2}\) (\({}^{\circ}\)) & \(-113.45\) & \(-99.35\) & 70.82 \\ \(\theta_{6}^{2}\) (\({}^{\circ}\)) & 161.34 & 231.11 & \(-41.28\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The maximum joint angular velocity components of Arm-2 in three comparing methods.