Robots Solving the Urgent Problems by Themselves: A Review
Dongdong Guo, Li Hua Fu, Lingling Wang · 2019
Recently, more and more robots act as a substitute for humans in many tasks, such as exploring the universe and the deep-sea. In these situations, solving the urgent problems, such as failures and damages of robots, by on-site maintenance is unpractical. Therefore, it is necessary for robots to know how to deal with the urgent problems by themselves. At present, there are lots of extensive and in-deep researches on hexapod robots especially in the adaption methods of leg injury, which have reference meaning to other robots. In order to control the remaining legs of a hexapod robot with leg failure or injury, fault tolerant and artificial intelligence (AI) represented by reinforcement learning and intelligent trial-and-error algorithm were implemented. This paper analyzed the advantages and disadvantages of the two approaches. The results show that the combination of fault-tolerant methods and AI methods can make robots solve their urgent problems better.