## 6 Conclusions  
To solve the problem of autonomous decision making and trajectory planning for launch vehicles under power failures, this paper investigates the orbit-injection strategy based on the target degradation rule and proposes an online trajectory-planning method based on the combination of the ATU-IGM and the RBFNN. The TDOS performs gradient degradation of the target mission from orbital energy and payload mass by analyzing mission attributes, failure modes, and multi-payload composition. To meet the requirement of flight timeliness, this paper proposes an online trajectory-planning method based on the offline decision-making learning model and online parameter-mapping method. For the offline decision-making learning model, RBFNN is used to train the optimal decision-making samples under power failures generated by the combination of the TDOS and the ATU-IGM; for the online parameter-mapping method, the main orbital elements are generated from the trained decision-making model according to the mission requirements and the failure states. Then, the remaining orbital elements are generated based on the TDOS, and the ATU-IGM is used for parameter adjustment and online trajectory planning.  
To test the feasibility of the proposed method, simulation analysis was conducted under four sets of typical missions with failure conditions. The results show that the proposed method can generate effective mission-planning results and achieve high trajectory-planning accuracy for different mission types and fault states. To further test the effectiveness of the mission planning, the particle swarm optimization method was used to perform the optimization comparison simulation under power failure. The results show  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Perigee** & **Apogee** & **Payload** & **Semimajor** & **Eccentricity** & **Mission-** & **Mission-** \\  & **Altitude/km** & **Altitude/km** & **Mass/kg** & **Axis Error/m** & **Error** & **Time/s** & **Detaining** \\ \hline I & 410 & 500 & 9.00 & 85.76 & \(4.85\times 10^{-5}\) & 0.01 & \\ I + PSO & 414 & 500 & 9.00 & \(-11.89\) & \(1.01\times 10^{-5}\) & 434.60 & \\ IV & 370 & 400 & 5.37 & 26.60 & \(-6.73\times 10^{-5}\) & 0.01 & \\ IV + PSO & 370 & 400 & 5.65 & \(-205.41\) & \(4.39\times 10^{-5}\) & 552.78 & 4.96 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effectiveness results of mission decision making and trajectory planning.  
that the mission-planning results of the proposed method are conservative compared with the optimization results. The maximum mission-planning deviation is 4.96%, determined by the optimal failure mission samples' height and payload mass degradation gradient. In addition, the deviation of the decision-making model can be reduced by reducing the gradient values. In summary, the proposed method can adapt to various mission situations and has obvious computational efficiency advantages, providing theoretical support for autonomous decision making and planning of space launch missions.  
Conceptualization, Y.D. and J.P.; methodology, Y.D.; software, H.X.; investigation, R.M.; writing--original draft preparation, Y.D.; writing--review and editing, Y.D.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Laboratory Opening Funding of Technology of Micro-Spacecraft (Grant No. HIT.KLOF.MST.2018028).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.