Name
Title | ||
|---|---|---|
CNNs based Foothold Selection for Energy-Efficient Quadruped Locomotion over Rough Terrains. |
Abstract | ||
|---|---|---|
When deployed in practical scenario, the legged robot has higher terrain passing ability but is suffering from lower locomotion efficiency than the wheeled robot. In this paper, we present a strategy that can improve the locomotion efficiency for a quadrupedal robot. First, an optimized energy-efficient nominal stance is generated. Second, a Convolutional Neural Networks (CNNs) based and self-supervised foothold classifier is implemented which will guide the robot to form the supporting legs in energy-efficient nominal stance during locomotion. The effectiveness of the present approach is validated on our quadrupedal robot Pegasus in stairs climbing experiment. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ROBIO49542.2019.8961842 | ROBIO |
Field | DocType | Citations |
Convolutional neural network,Efficient energy use,Terrain,Legged robot,Control engineering,Artificial intelligence,Engineering,Robot,Classifier (linguistics),Climbing,Stairs | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu Chen | 1 | 0 | 1.35 |
| Shusheng Ye | 2 | 0 | 1.35 |
| Caiming Sun | 3 | 0 | 4.06 |
| Aidong Zhang | 4 | 2970 | 405.63 |
| Ganyu Deng | 5 | 0 | 2.03 |
| Tianjiao Liao | 6 | 0 | 0.68 |
| Junwen Sun | 7 | 0 | 0.34 |