A stereovision system for a planetary rover: calibration, correlation, registration, and fusion

1997 ◽  
Vol 10 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Zhengyou Zhang
Keyword(s):  
Planetary Rover

Inflatable tires for planetary rover vehicle

2000 ◽  
Author(s):  
Frank Kustas ◽  
Suraj Rawal ◽  
T. Charles ◽  
W. Chun
Keyword(s):  
Planetary Rover

Analyzing the effectiveness of rescheduling and Flexible Execution methods to address uncertainty in execution duration for a planetary rover

2021 ◽  
pp. 103758
Author(s):  
Jagriti Agrawal ◽  
Wayne Chi ◽  
Steve Chien ◽  
Gregg Rabideau ◽  
Daniel Gaines ◽  
...  
Keyword(s):  
Planetary Rover

Experimental Validation of a Volumetric Planetary Rover Wheel/Soil Interaction Model

2013 ◽  
Author(s):  
Willem Petersen ◽  
John McPhee
Keyword(s):  
Contact Force ◽  
Interaction Model ◽  
Contact Model ◽  
Soil Parameters ◽  
Model Parameters ◽  
Force Model ◽  
Normal Contact ◽  
Planetary Rover ◽  
Normal Contact Force ◽  
Contact Force Model

For the multibody simulation of planetary rover operations, a wheel-soil contact model is necessary to represent the forces and moments between the tire and the soft soil. A novel nonlinear contact modelling approach based on the properties of the hypervolume of interpenetration is validated in this paper. This normal contact force model is based on the Winkler foundation model with nonlinear spring properties. To fully define the proposed normal contact force model for this application, seven parameters are required. Besides the geometry parameters that can be easily measured, three soil parameters representing the hyperelastic and plastic properties of the soil have to be identified. Since it is very difficult to directly measure the latter set of soil parameters, they are identified by comparing computer simulations with experimental results of drawbar pull tests performed under different slip conditions on the Juno rover of the Canadian Space Agency (CSA). A multibody dynamics model of the Juno rover including the new wheel/soil interaction model was developed and simulated in MapleSim. To identify the wheel/soil contact model parameters, the cost function of the model residuals of the kinematic data is minimized. The volumetric contact model is then tested by using the identified contact model parameters in a forward dynamics simulation of the rover on an irregular 3-dimensional terrain and compared against experiments.

scholarly journals Training a terrain traversability classifier for a planetary rover through simulation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773540 ◽  
Author(s):  
Robert A Hewitt ◽  
Alex Ellery ◽  
Anton de Ruiter
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Planetary Rover ◽  
Training Methodology ◽  
Sensor Model ◽  
Random Fashion ◽  
Manual Classification

A classifier training methodology is presented for Kapvik, a micro-rover prototype. A simulated light detection and ranging scan is divided into a grid, with each cell having a variety of characteristics (such as number of points, point variance and mean height) which act as inputs to classification algorithms. The training step avoids the need for time-consuming and error-prone manual classification through the use of a simulation that provides training inputs and target outputs. This simulation generates various terrains that could be encountered by a planetary rover, including untraversable ones, in a random fashion. A sensor model for a three-dimensional light detection and ranging is used with ray tracing to generate realistic noisy three-dimensional point clouds where all points that belong to untraversable terrain are labelled explicitly. A neural network classifier and its training algorithm are presented, and the results of its output as well as other popular classifiers show high accuracy on test data sets after training. The network is then tested on outdoor data to confirm it can accurately classify real-world light detection and ranging data. The results show the network is able to identify terrain correctly, falsely classifying just 4.74% of untraversable terrain.

Automatic Rocks Segmentation Based on Deep Learning for Planetary Rover Images

2021 ◽  
pp. 1-6
Author(s):  
Haichao Li ◽  
Linwei Qiu ◽  
Zhi Li ◽  
Bo Meng ◽  
Jianbin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Planetary Rover
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