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Acquisition of earthworm-like movement patterns of many-segmented peristaltic crawling robots
| Content Provider | SAGE Publishing |
|---|---|
| Author | Saga, Norihiko Tesen, Satoshi Sato, Toshiyuki Nagase, Jun-Ya |
| Copyright Year | 2016 |
| Abstract | In recent years, attention has been increasingly devoted to the development of rescue robots that can protect humans from the inherent risks of rescue work. Particularly, anticipated is the development of a robot that can move deeply through small spaces. We have devoted our attention to peristalsis, the movement mechanism used by earthworms. A reinforcement learning technique used for the derivation of the robot movement pattern, Q-learning, was used to develop a three-segmented peristaltic crawling robot with a motor drive. Characteristically, peristalsis can provide movement capability if at least three segments work, even if a segmented part does not function. Therefore, we had intended to derive the movement pattern of many-segmented peristaltic crawling robots using Q-learning. However, because of the necessary increase in calculations, in the case of many segments, Q-learning cannot be used because of insufficient memory. Therefore, we devoted our attention to a learning method called Actor–Critic, which can be implemented with low memory. Because Actor-Critic methods are TD methods that have a separate memory structure to explicitly represent the policy independent of the value function. Using it, we examined the movement patterns of six-segmented peristaltic crawling robots. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.1177/1729881416657740?download=true |
| ISSN | 17298806 |
| Issue Number | 5 |
| Volume Number | 13 |
| Journal | International Journal of Advanced Robotic Systems (ARX) |
| e-ISSN | 17298814 |
| DOI | 10.1177/1729881416657740 |
| Language | English |
| Publisher | Sage Publications UK |
| Publisher Date | 2016-10-16 |
| Publisher Place | London |
| Access Restriction | Open |
| Rights Holder | © The Author(s) 2016 |
| Subject Keyword | reinforcement learning Peristaltic crawling robot biomimetic Actor–Critic |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Science Applications Software |
