Name
Abstract | ||
|---|---|---|
Walking animals have shown energy efficiency and versatile locomotion ability when adapting to their environments. Different methods, including machine learning, model control and biologically inspired control, have been applied in artificial legged locomotion systems to imitate these natural characteristics. In this paper, a virtual motoneuron activation module (VMAM) is built to convert goal-directed behavioral instructions into nerve signal strength, which leads to animal-like signal processing mechanisms and behavioral responses. The system is composed of a reticulospinal neuron module, a central pattern generator (CPG) and a virtual motoneuron network (VMN). The VMN receives the rhythm signals from the CPG and generates different motions that are controlled by global and local factors of muscle activation and goal-directed points; then, through an inverse kinematics module, the signals for driving the joint motors of the robot are obtained. Finally, four groups of experiments with different global and local factors of muscle activation are tested on a bionic hexapod robot to validate the effectiveness. The results prove that the method can generate active adaption and emergent behavior according to signal feedback and improve the locomotion agility, coordination, and diversity of the robot. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICARM49381.2020.9195387 | 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM) |
Keywords | DocType | ISBN |
muscle activation,inverse kinematics module,bionic hexapod robot,signal feedback,goal-directed locomotion,versatile locomotion ability,machine learning,biologically inspired control,artificial legged locomotion systems,virtual motoneuron activation module,goal-directed behavioral instructions,nerve signal strength,signal processing mechanisms,behavioral responses,reticulospinal neuron module,central pattern generator,CPG,virtual motoneuron network,VMN,rhythm signals | Conference | 978-1-7281-6480-9 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaguang Zhu | 1 | 0 | 0.34 |
| Liang Zhang | 2 | 12 | 2.59 |
| Poramate Manoonpong | 3 | 226 | 31.35 |