## 5 Conclusions  
In this paper, a novel structure model of the quadruped robot and self-recovery method were proposed. The CIQR was developed to imitate the crawling locomotion of the caterpillar for the case in which the robot has a small number of active legs. Prolegs similar to those on a caterpillar were added onto the robot limb to improve its ability to move after some parts of the robot were damaged. The proposed bio-inspired quadruped robot structure was optimized to ensure that it can operate in both normal and abnormal scenario, and PSO was used as the optimization method. Moreover, a new bio-inspired locomotion method based on the movement of mudskipper in nature, called SLMIC, was proposed in this paper. The reinforcement learning method, Q-learning, was integrated to improve locomotion adaptability. The specific actions inspired by mudskippers were set as an action for Q-learning processes. The orientation of the robot body was sent to the learning process as a state (current scenario of the robot). The positive and negative rewards were given to the robot when it performed the task. The robot received a positive reward when it moved forward in a straight direction. On the other hand, it received a negative reward when it moved backward.  
Figure 21: CIQR with two diagonal legs performing recovery action after learning with the SLMIC method.  
The results confirm that the proposed structure with prolegs provided better results compared to a normal structure when the robot legs were damaged, especially when the robot had only one leg. With one functional leg, the robot with the proposed structure could travel almost eight times longer than the robot without prolegs. According to the numerical simulation and experiments, the recovery methods are feasible for implementation in a damaged robot that has at least one leg. From the simulation, the robot programmed with SLMIC provided a straighter and longer path than the robot performed with other methods. During the experiment, it was found that the damaged robot controlled with SLMIC could travel in a straight path longer than the robot without recovery methods. The most interesting finding is that, with carefully designed actions, which are based on the simple behaviors of mudskipper, a robot can learn new habits to achieve the same goal as a healthy robot. In the future, it is planned to apply and to develop the proposed structure and self-recovery method with other legged robots to improve the performance.  
[PERSON] and Y.K. designed the study and conceived of the presented idea; [PERSON] carried out the simulation and experiments; [PERSON] took the lead in writing the manuscript. Y.K., T.E., and A.A.R. helped to supervise the study and reviewed the paper.  
This work is supported by MEXT (the Ministry of Education, Culture, Sports, Science and Technology), Japan.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
DOFs Degrees of Freedom GA Genetic Algorithm PSO Particle Swarm Optimization DH Denavit-Hartenberg CIQR Caterpillar-Inspired Quadruped Robot EA Evolutionary Algorithm EAD Evolutionary Algorithm MHD Mudskipper-Inspired Movement SLMIC Self-Learning Mudskipper-Inspired Crawling TGCT Trotting Gait Controller