TY - CHAP
T1 - Evolutionary aerial robotics
T2 - The human way of learning
AU - Santoso, Fendy
AU - Garratt, Matthew A.
AU - Anavatti, Sreenatha G.
AU - Wang, Jiefei
N1 - Publisher Copyright:
© 2021 Elsevier Inc. All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Robotic aircraft are often required to operate in harsh environments (e.g., underground mining, cluttered environments, and battlefields). In this chapter, we discuss an adaptive (evolving) fuzzy system that has the ability to learn and to configure itself based on the human way of learning, which is also somewhat akin to the principles of natural evolution. We will be looking at the capability of an evolving Takagi-Sugeno (ETS) fuzzy algorithm to learn-from-scratch in order to adapt the challenging dynamics of autonomous systems in real-time. The ETS system can also work in unknown environments, where there is no expert knowledge. While we focus on the implementation of the ETS system to identify the behavior of a fast-dynamical system as in the case of the low altitude hovering of our Tarot hexacopter drone by performing an online ETS-based data driven modelling (online system identification) technique, we also conduct a preliminary study to highlight the efficacy of the ETS autopilot under computer simulations.
AB - Robotic aircraft are often required to operate in harsh environments (e.g., underground mining, cluttered environments, and battlefields). In this chapter, we discuss an adaptive (evolving) fuzzy system that has the ability to learn and to configure itself based on the human way of learning, which is also somewhat akin to the principles of natural evolution. We will be looking at the capability of an evolving Takagi-Sugeno (ETS) fuzzy algorithm to learn-from-scratch in order to adapt the challenging dynamics of autonomous systems in real-time. The ETS system can also work in unknown environments, where there is no expert knowledge. While we focus on the implementation of the ETS system to identify the behavior of a fast-dynamical system as in the case of the low altitude hovering of our Tarot hexacopter drone by performing an online ETS-based data driven modelling (online system identification) technique, we also conduct a preliminary study to highlight the efficacy of the ETS autopilot under computer simulations.
KW - Aerial robotics
KW - Evolutionary TS fuzzy systems
KW - Learning-from-scratch
KW - Online system identification
UR - http://www.scopus.com/inward/record.url?scp=85114659924&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114659924&partnerID=8YFLogxK
U2 - 10.1016/B978-0-12-820276-0.00008-X
DO - 10.1016/B978-0-12-820276-0.00008-X
M3 - Chapter (peer-reviewed)
AN - SCOPUS:85114659924
SN - 9780128202760
T3 - Advances in Nonlinear Dynamics and Chaos
SP - 1
EP - 23
BT - Unmanned aerial systems
A2 - Koubaa, Anis
A2 - Taher Azar, Ahmad
PB - Academic Press
CY - London
ER -