This Ph.D. thesis presents the design and implementation of a biologically inspired
four-phase walking strategy using behaviours for a four legged walking robot. In
particular, the walking strategy addresses the balance issue, including both static and
dynamic balance that were triggered non-deterministically based on the robotÂ’s realtime interaction with the environment. Four parallel Subsumption Architectures
(SA) and a simple Central Pattern Producer (CPP) are employed in the physical
implementation of the walking strategy. An implementation framework for such a
parallel Subsumption Architecture is also proposed to facilitate the reusability of the
system. A Reinforcement Learning (RL) method was integrated into the CPP to
allow the robot to learn the optimal walking cycle interval (OWCI), appropriate for
the robot walking on various terrain conditions. Experimental results demonstrate
that the robot employs the proposed walking strategy and can successfully carry out
its walking behaviours under various experimental terrain conditions, such as flat
ground, incline, decline and uneven ground. Interactions of all the behaviours of the
robot enable it to exhibit a combination of both preset and emergent walking
behaviours.
| Identifer | oai:union.ndltd.org:ADTP/221804 |
| Date | January 2006 |
| Creators | shiqi.peng@woodside.com.au, Shiqi Peng |
| Publisher | Murdoch University |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.murdoch.edu.au/goto/CopyrightNotice, Copyright Shiqi Peng |
Page generated in 0.0017 seconds