# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 5 Experimental Results
### Comparison Experiment of Optimal-Path Model Optimization
#### 5.2.1 Experimental Comparison

Table 3: Comparison of task-planning paths for a space-robot cluster with a total energy of 5000. As can be seen from Table 3, the energy consumption of each space robot are basically close to its maximum of available energy, it has made full use of energy, but under the model of optimal path space-robot clustering task earnings significantly higher than that of the shortest-path model, which is about 1.38 times of the shortest-path model, the benefits of maintenance were obvious ascension; it can also be seen that the number of maintenance nodes in the optimal-path model is much higher than that in the shortest-path model, which is about 1.36 times that of the shortest-path model, by comparing the Income rate, it can be seen that the task return rate of the optimal path is also much higher than the shortest-path model. In conclusion, under the same energy constraints, the planning effect of the optimal-path model is better than that of the shortest-path model. Similarly, Tables 4 and 5 show the optimization results of the optimal path and the shortest path under the other two energies. By comparing Tables 3-5, the planning effect of the optimal-path model is better than that of the shortest-path model at different energy levels, which shows the superiority of the optimal-path model proposed in this paper. In space, resource constraint is one of the main factors that affect the space robot, and they must maximize efficiency with limited resources. It can be clearly seen from Tables 3-5 that under certain resource conditions, the number of tasks completed by the optimal path, the total revenue and the task completion rate are obviously better than the shortest path. The total revenue of the optimal path is 42.33% more than the total revenue of the shortest path, and the number of tasks completed by the optimal path is 40.67% more than the average number of tasks completed by the shortest path. Therefore, the optimal-path model has obvious advantages in dealing with the task allocation problem of space-robot clusters with limited resources. The specific maintenance routes of the space-robot cluster with energies of 5000, 6000 and 7000 are, respectively shown in Figures 3-5, the starting point of the optimized path is the location of each space charging pile. Figures 3-5 correspond to the paths in Tables 3-5, wherein star points represent the starting positions of the space robot, solid line loop in the optimal-path model represents the maintenance path of the space robot, and the path consumption of each robot corresponds to the corresponding table. The solid line and stippling line in the shortest path represent the maintenance path of the space robot, while the dotted line represents the position in the shortest path that the space robot cannot complete under the condition of limited energy. The stippling line is the route that the space robot can return to the charging pile position within a safe energy range. However, in some cases, because the points in the cluster are too scattered, the different results of the cluster will have a certain impact on the number of completed tasks. This experiment proves the feasibility of the optimal path.