## 4 Results and Discussion
### Evaluation of Self-Learning Mudskipper-Inspired Crawling Method (SLMIC)
#### 4.2.1 Simulation Results of SLMIC Vis-a-Vis Other Methods  
The numerical simulation was conducted separately for each case in the experiments. As shown in Figure 19, the results of four different damage scenarios are as follows:  
1. case A: The results show that all methods used in the simulation allowed the robot to be able to move again. With one leg lost, it was easy for the robot to travel with three functional legs. However, not all methods provided the acceptable results. EA helps the robot move the shortest distance compared with other methods, as shown in Figure 19b. EAD helps the robot move longer than EA but it lost out to the MUD and SLMIC methods. By employing the specific actions of mudskippers, both MUD and SLMIC help the robot travel longer distances. However, SLMIC provided the best result in this test because it helped the robot learn to move forward faster.
2. case B: The robot programmed using EA traveled faster than the robot programmed using EAD, as shown in Figure 19c. However, EAD provided the better result in terms of direction. It seemed that, with EAD, the robot optimized multiple objectives, namely, distance traveled and direction of travel. As a result, the robot assigned more importance to direction in optimization, which reduced the distance traveled. MUD and SLMIC provided decent results in terms of distance traveled and direction of travel. Once again, SLMIC provided the best performance.
3. case C: Similar to the two cases in the experiments, with MUD and SLMIC, the robot covered longer distances. However, SLMIC performed better in terms of direction of travel. Opposite to case B, EAD could deal with only the distance traveled. At this time, EAD attempted to optimize the distance traveled by the robot, but it failed to optimize the direction of travel, and thus the robot failed to move straight ahead. With EA, the robot could not perform well because the two diagonal legs affected its balance. The robot flipped over during the recovery process which limited its ability to move. As a result, the robot programmed using EA could travel properly, as shown in Figure 19d.
4. case D: This experiment was the most challenging because of the limited number of functional legs and actuators, as shown in the results in Figure 19e. Given the extremities, the robot programmed using SLMIC could learn to recovery itself with SLMIC and provided the best results in terms of direction and distance. MUD with its specific control method was the second best performer in this experiment. EAD exhibited the worst performance owning to the same reason as in case B, and EA achieved a fair level of performance.  
From the results, it can be concluded that the proposed method (SLMIC) can provide the best results compared to the other methods in terms of direction of travel and the distance traveled. However, a certain learning time is required to achieve the goal. The performance of MUD was inferior to that of SLMIC, but, in most cases, it performed satisfactorily. However, the crucial aspect of MUD is that it employs specific mudskipper-inspired actions with no time required for evolutionary process or learning. The results indicate that it would be difficult to guarantee satisfactory performance in terms of direction and distance traveled with both EA and EAD because differences in robot model can cause task failures, and the methods can get stuck in local minima when performing multi-objectives optimization, which can lead to failure.