| Autor: |
Paolo Arena, Luca Patanè, Salvatore Taffara |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Robotics, Vol 12, Iss 3, p 67 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2218-6581 |
| DOI: |
10.3390/robotics12030067 |
| Popis: |
In this paper, the locomotion and steering control of a simulated Mini Cheetah quadruped robot was investigated in the presence of terrain characterised by low friction. Low-level locomotion and steering control were implemented via a central pattern generator approach, whereas high-level steering control manoeuvres were implemented by comparing a neural network and a linear model predictive controller in a dynamic simulation environment. A data-driven approach was adopted to identify the robot model using both a linear transfer function and a shallow artificial neural network. The results demonstrate that, whereas the linear approach showed good performance in high-friction terrain, in the presence of slippery conditions, the application of a neural network predictive controller improved trajectory accuracy and preserved robot safety with different steering manoeuvres. A comparative analysis was carried out using several performance indices. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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