Highlights Terrain parameter estimation using neural networks is proposed. Preliminary results from micro-rover instrumented mobility system tests. On-line terrain parameter estimation to avoid immobilization.
Abstract This paper presents a method for extracting data on regolith online with a planetary exploration micro-rover. The method uses a trained neural network to map engineering data from an instrumented chassis to estimates of regolith parameters. The target application for this method is a low-cost micro-rover scout on Mars that will autonomously traverse the surface and detect changes in the regolith cohesion and shearing resistance without the need for dedicated visual sinkage estimation on each wheel. This method has been applied to Kapvik, a low-cost 30kg micro-rover analogue designed and built for the Canadian Space Agency. Data was collected using a motor controller interface designed for Kapvik using off-the-shelf components. The neural network was trained from parameters derived by classical terramechanics theory using Matlab’s Neural Network Toolbox. The results demonstrate a proof of concept that neural networks can estimate the terrain parameters which may have applications for automated online traction control.
Estimating terrain parameters for a rigid wheeled rover using neural networks
Journal of Terramechanics ; 50 , 3 ; 165-174
2013-04-06
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Estimating terrain parameters for a rigid wheeled rover using neural networks
Online Contents | 2013
|Design of a wheeled articulating land rover
NTRS | 1994
|Terrain reconstruction from rover images
AIAA | 1993
|Terrain Reconstruction from Rover Images
British Library Conference Proceedings | 1993
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