Paper Info
Title
Achieving bipedal locomotion on rough terrain through human-inspired control
Abstract
This paper presents a method for achieving robotic walking on rough terrain through Human-Inspired Control. This control methodology uses human data to achieve human like walking in robots by considering outputs that appear to be indicative of walking, and using nonlinear control methods to track a set of functions called Canonical Walking Functions (CWF). While this method has proven successful on a specific well-defined terrain, rough terrain walking is achieved by dynamically changing the CWF that the robot outputs should track at every step. To make the computation more tractable Extended Canonical Walking Functions (ECWF) are used to generate these desired functions instead of CWF. The state of the robot, after every non-stance foot strike, is actively sensed and a new CWF is constructed to ensure Hybrid Zero Dynamics is respected for the next step. Finally, the technique developed is implemented on different terrains in simulation. The same technique is adopted experimentally on the bipedal robot AMBER and tested on sinusoidal terrain. Experimental results show how the walking gait morphs based upon the terrain, thereby justifying the theory applied.
Year
DOI
Venue
2012
10.1109/SSRR.2012.6523897
Safety, Security, and Rescue Robotics
Keywords
Field
DocType
gait analysis,legged locomotion,nonlinear control systems,path planning,robot dynamics,ECWF,bipedal locomotion,bipedal robot AMBER,control methodology,extended canonical walking functions,human data,human like walking,human-inspired control,hybrid zero dynamics,nonlinear control methods,nonstance foot strike,robot outputs,robotic walking,rough terrain walking,sinusoidal terrain,walking gait morphs,Bipedal robotic walking,human-inspired control,rough terrain navigation
Bipedalism,Motion planning,Computer vision,Gait,Computer science,Nonlinear control,Simulation,Terrain,Gait analysis,Artificial intelligence,Robot,Computation
Conference
ISBN
Citations 
PageRank 
978-1-4799-0164-7
7
0.56
References 
Authors
4
2
Name
Order
Citations
PageRank
Shishir Kolathaya1677.40
Aaron D. Ames21202136.68