Mars Exploration Rover Mobility Assembly Design, Test and Performance

Lindemann, R.; Voorhees, Christopher

doi:10.1109/icsmc.2005.1571187

Cited by 137 publications

(99 citation statements)

References 5 publications

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“…the slip in the forward motion direction as dependent on the longitudinal slope, similar to slip measurements done for the Mars Exploration Rover [18].…”

Section: Tha34supporting

confidence: 56%

“…This experiment is partially controlled, to be able to report classification error, comparing to human labeled terrains, and to measure goodness-of-fit to the mechanical behavior models. The models used are generated to simulate actual 'slip vs. slope' behavior, as measured and reported in [18] for MER. The image patches are collected from three actual terrains, while driving on sand, soil and gravel, but the visual representation used is also very simple: it is composed of the average normalized Red and Green of the terrain patch.…”

Section: Experimental Evaluation Of the Frameworkmentioning

confidence: 99%

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Learning slip behavior using automatic mechanical supervision

Angelova

Matthies

Helmick

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract-We address the problem of learning terrain traversability properties from visual input, using automatic mechanical supervision collected from sensors onboard an autonomous vehicle. We present a novel probabilistic framework in which the visual information and the mechanical supervision interact to learn particular terrain types and their properties.The proposed method is applied to learning of rover slippage from visual information in a completely automatic fashion. Our experiments show that using mechanical measurements as automatic supervision significantly improves the visual-based classification alone and approaches the results of learning with manual supervision. This work will enable the rover to drive safely on slopes, learning autonomously about different terrains and their slip characteristics.

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“…the slip in the forward motion direction as dependent on the longitudinal slope, similar to slip measurements done for the Mars Exploration Rover [18].…”

Section: Tha34supporting

confidence: 56%

Section: Experimental Evaluation Of the Frameworkmentioning

confidence: 99%

Learning slip behavior using automatic mechanical supervision

Angelova

Matthies

Helmick

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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show abstract

“…The slopes can be easily estimated by the robot remotely using range data from stereo, ladar, etc., and a tilt sensor on the robot, which is readily available from the IMU, for example. We consider only the slip in the forward motion direction as dependent on the longitudinal slope, similar to slip measurements done for the Mars Exploration Rover [14], which is a simpler and more straightforward representation of slip than in [2]. This representation is also more convenient for using the slip measurements as supervision during learning.…”

Section: Problem Formulationmentioning

confidence: 99%

Dimensionality Reduction Using Automatic Supervision for Vision-Based Terrain Learning

Angelova¹,

Matthies²,

Helmick³

et al. 2007

Robotics: Science and Systems III

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Abstract-This paper considers the problem of learning to recognize different terrains from color imagery in a fully automatic fashion, using the robot's mechanical sensors as supervision. We present a probabilistic framework in which the visual information and the mechanical supervision interact to learn the available terrain types. Within this framework, a novel supervised dimensionality reduction method is proposed, in which the automatic supervision provided by the robot helps select better lower dimensional representations, more suitable for the discrimination task at hand. Incorporating supervision into the dimensionality reduction process is important, as some terrains might be visually similar but induce very different robot mobility. Therefore, choosing a lower dimensional visual representation adequately is expected to improve the vision-based terrain learning and the final classification performance. This is the first work that proposes automatically supervised dimensionality reduction in a probabilistic framework using the supervision coming from the robot's sensors. The proposed method stands in between methods for reasoning under uncertainty using probabilistic models and methods for learning the underlying structure of the data.The proposed approach has been tested on field test data collected by an autonomous robot while driving on soil, gravel and asphalt. Although the supervision might be ambiguous or noisy, our experiments show that it helps build a more appropriate lower dimensional visual representation and achieves improved terrain recognition performance compared to unsupervised learning methods.

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“…The locomotion systems used in this work are the CRAB of the Autonomous Systems Lab (ASL) and the Concept E (RCL-E), which was proposed to the European Space Agency (ESA) by RCL and VNIITRANSMASH (Kucherenko, Bogatchev & Van Winnendael, 2004). Since both concepts are related to the rocker-bogie-suspension type rovers by NASA, their performance was also compared in simulation to a scaled version of the Mars Exploration Rover (MER) (Lindemann & Voorhees, 2005). This paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%