Learning action models to support efficient navigation planning for unmanned ground vehicles

Natasha Balac; Daniel M. Gaines; Siripun Thongchai; Doug H. Fisher; Surachai Suksakulchai

doi:10.1117/12.439980

20 September 2001 Learning action models to support efficient navigation planning for unmanned ground vehicles

Natasha Balac, Daniel M. Gaines, Siripun Thongchai, Doug H. Fisher, Surachai Suksakulchai

Proceedings Volume 4364, Unmanned Ground Vehicle Technology III; (2001) https://doi.org/10.1117/12.439980
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

An effective navigation planner must have knowledge not only of the effects its actions will have, but also the effect that the environment will have on its actions (e.g. the UGV may travel more slowly over rough terrain). This is needed because the shortest path to the goal is not always the most efficient when you consider the rate of travel over the terrain. To address this issue, we have developed an approach called ERA which uses regression tree induction to learn action models that predict the effect terrain conditions will have on a UGV's navigation actions. The action models support a high level mission planner that finds efficient navigation plans consisting of way-points through which the UGV should travel. We will present results from our experiments in a simulated environment and on an RWI ATRV-Jr robot. The studies evaluate the performance of ERA in different mission scenarios with different amounts of sensor and actuator noise. Advantages of our approach include the ability to automatically learn action models, generate efficient high level navigation plans taking into account terrain conditions and transfer learned knowledge to other missions.

Citation Download Citation

Natasha Balac, Daniel M. Gaines, Siripun Thongchai, Doug H. Fisher, and Surachai Suksakulchai "Learning action models to support efficient navigation planning for unmanned ground vehicles", Proc. SPIE 4364, Unmanned Ground Vehicle Technology III, (20 September 2001); https://doi.org/10.1117/12.439980

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available