3D Mobility Learning and Regression of Articulated, Tracked Robotic Vehicles by Physics-based Optimization

Papadakis, Panagiotis; Pirri, Fiora

3D Mobility Learning and Regression of Articulated, Tracked Robotic Vehicles by Physics-based Optimization

Files

147-156.pdf (2.42 MB)

Date

2012

Authors

Papadakis, Panagiotis
Pirri, Fiora

Publisher

The Eurographics Association

Abstract

Motion planning for robots operating on 3D rough terrain requires the synergy of various robotic capabilities, from sensing and perception to simulation, planning and prediction. In this paper, we focus on the higher level of this pipeline where by means of physics-based simulation and geometric processing we extract the information that is semantically required for an articulated, tracked robot to optimally traverse 3D terrain. We propose a model that quantifies 3D traversability by accounting for intrinsic robot characteristics and articulating capabilities together with terrain characteristics. By building upon a set of generic cost criteria for a given robot state and 3D terrain patch, we augment the traversability cost estimation by: (i) unifying pose stabilization with traversability cost estimation, (ii) introducing new parameters into the problem that have been previously overlooked and (iii) adapting geometric computations to account for the complete 3D robot body and terrain surface. We apply the proposed model on a state-of-the-art Search and Rescue robot by performing a plurality of tests under varying conditions and demonstrate its efficiency and applicability in real-time.

        @inproceedings{:10.2312/PE/vriphys/vriphys12/147-156
,
booktitle = {Workshop on Virtual Reality Interaction and Physical Simulation
},
editor = {Jan Bender and Arjan Kuijper and Dieter W. Fellner and Eric Guerin
},
title = {{3D Mobility Learning and Regression of Articulated, Tracked Robotic Vehicles by Physics-based Optimization
}},
author = {Papadakis, Panagiotis and 
Pirri, Fiora
},
year = {2012
},
publisher = {The Eurographics Association
},
ISBN = {978-3-905673-96-8
},
DOI = {/10.2312/PE/vriphys/vriphys12/147-156
}
}