# An Efficient Task Autonomous Planning Method for Small Satellites
## 4 The Experiment and Analysis
### Performance Analysis of the Method  
Experiment 1 and Experiment 2 are a group of comparative experiments. They compare the effects of different resource states on task scheduling under the same condition of low overlapping of time windows. We set 156 observation points evenly distributed on the nine dimensions of the Earth \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Semi-Major Axis** & **Eccentricity** & **Orbit Inclination Angle** &
\begin{tabular}{c} **Right Ascension of** \\ **Asceding Node** \\ \end{tabular} & **Argument of Perigee** & **True Anomaly** \\ \hline
7878 km & 0 & 60\({}^{\circ}\) & 330\({}^{\circ}\) & 0\({}^{\circ}\) & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Satellite orbit parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ground Station** & **Longitude** & **Latitude** & **Minimum Angle of Elevation** \\ \hline Beijing & 116\({}^{\circ}\) & 40\({}^{\circ}\) & 20\({}^{\circ}\) \\ Kiamusze & 130.32\({}^{\circ}\) & 46.8\({}^{\circ}\) & 20\({}^{\circ}\) \\ Kashgar & 75.93\({}^{\circ}\) & 39.51\({}^{\circ}\) & 20\({}^{\circ}\) \\ Nanning & 108.37\({}^{\circ}\) & 22.82\({}^{\circ}\) & 20\({}^{\circ}\) \\ Xiamen & 118.09\({}^{\circ}\) & 22.48\({}^{\circ}\) & 20\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ground Station Parameters. to simulate Experiment 1 and Experiment 2, as shown in Figure 9. The minimum elevation angle of the targets are random numbers between [50; 60]. In Experiment 1, resources are relatively abundant, and the specific parameters are set as follows. \(M\), the maximum storage capacity, equals 16 GB; the maximum electricity, \(E\), equals 5000 J; and the charge rate of satellite C equals 10 W. While in Experiment 2, \(E\) equals 20,000 J, and C equals 5 W.  
The parameters of the task in Experiment 1 and Experiment 2 are set as follows: the length of the execution time of the task obeys normal distribution, with the expectation of 120 s and the standard deviation of 20, the crossover probability is 0.3, the mutation probability is 0.5, the proportion of random individuals introduced into the new parent population is 0.15, the maximum number of iterations is 10, and the maximum population capacity is 30. Experiment 3 and Experiment 4 are a group of comparative experiments too. They compare the effects of different resource states on task scheduling under the same condition of a high overlapping of time windows. The specific parameters of Experiment 3 are the same as those of Experiment 1, and the parameters of Experiment 4 are the same as those of Experiment 2. The experimental arrangements can be summarized as follows. For the above four groups of experiments, the running time and the task revenue of the planning algorithm are shown in Figures 10-13 respectively, after 20 times running. Figure 10: The result of Experiment 1. (**a**) The running time of Experiment 1 and (**b**) the task revenue of Experiment 3. Figure 9: The task scenery. In Experiment 1, both the time constraints and resource constraints are relatively loose. From Figure 10a,b, we can see that the maximum task revenue of GA and IGA are almost equal, but the running time of the IGA is only 50% that of the GA. The time constraint in Experiment 2 is the same as that in Experiment 1, but its resource constraints are more stringent than Experiment 1. From Figure 11a,b, we can see that the income of the IGA is similar to that of GA. In terms of running time, IGA is superior in all running times, and its running time is about 25% that of GA. In Experiment 3, the degree of the overlapping of time windows was high, while the resource constraints were relatively loose. It can be seen from Figure 12a,b that the average income of IGA is slightly higher than that of GA. In terms of the running time, the average running time of IGA is about 75% of the average running time of GA. The time constraint in Experiment 4 is the same as that in Experiment 3, but its resource constraints are more stringent than Experiment 3.