Abstract
The kinematics of human walking are largely driven by passive dynamics, but adaptation to varying terrain conditions and responses to perturbations require some form of active control. The basis for this control is often thought to take the form of entrainment between a neural oscillator (i.e., a central pattern generator and/or distributed counterparts) and the mechanical system. Here we use techniques in evolutionary robotics to explore the potential of a purely reactive, linear controller to control bipedal locomotion over rough terrain. In these simulation studies, joint torques are computed as weighted linear sums of sensor states, and the weights are optimized using an evolutionary algorithm. We show that linear reactive control can enable a seven-link 2D biped and a nine-link 3D biped to walk over rough terrain (steps of ~5% leg length or more in the 2D case). In other words, the simulated walker gradually learns the appropriate weights to achieve stable locomotion. The results indicate that oscillatory neural structures are not necessarily a requirement for robust bipedal walking. The study of purely reactive control through linear feedback may help to reveal some basic control principles of stable walking.
Original language | English (US) |
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Pages (from-to) | 29-46 |
Number of pages | 18 |
Journal | Adaptive Behavior |
Volume | 21 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2013 |
Funding
We would like to express our special thanks to Dr. Shoushi, Mr. Zadeh Dabbagh and Mr. Yavari for helping with this research.
Keywords
- Bipedal walking
- biped
- dynamic walking
- evolutionary robotics
- reactive control
- rough terrain
ASJC Scopus subject areas
- Experimental and Cognitive Psychology
- Behavioral Neuroscience