The Experts below are selected from a list of 8361 Experts worldwide ranked by ideXlab platform
Karl D. Iagnemma - One of the best experts on this subject based on the ideXlab platform.
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statistical mobility prediction for Planetary Surface exploration rovers in uncertain terrain
International Conference on Robotics and Automation, 2010Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Planetary Surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of terrain physical parameters, however in practical scenarios knowledge of terrain parameters contains significant uncertainty. In this paper, a statistical method for mobility prediction that incorporates terrain uncertainty is presented. The proposed method consists of two techniques: a wheeled vehicle model for calculating vehicle dynamic motion and wheel-terrain interaction forces, and a stochastic response Surface method (SRSM) for modeling of uncertainty. The proposed method generates a predicted motion path of the rover with confidence ellipses indicating the probable rover position due to uncertainty in terrain physical parameters. Rover orientations and wheel slippage are also predicted. The computational efficiency of SRSM as compared to conventional Monte Carlo methods is shown via numerical simulations. Experimental results of rover travel over sloped terrain in two different uncertain terrains are presented that confirms the utility of the proposed mobility prediction method.
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predictable mobility a statistical approach for Planetary Surface exploration rovers in uncertain terrain
IEEE Robotics & Automation Magazine, 2009Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Predictable Mobility : A Statistical Approach for Planetary Surface Exploration Rovers in Uncertain Terrain
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statistical approach to mobility prediction for Planetary Surface exploration rovers in uncertain terrain
International Conference on Robotics and Automation, 2009Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Planetary Surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of te ...
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Reconfigurability in Planetary Surface vehicles: Modelling approaches and case study
JBIS - Journal of the British Interplanetary Society, 2006Co-Authors: Afreen Siddiqi, Olivier Ladislas De Weck, Karl D. IagnemmaAbstract:Reconfigurability is being recognized as increasingly important for space systems for reasons of efficiency, extensibility and mission robustness. Planetary Surface Vehicles (PSVs), that may be used in future manned exploration missions, can especially benefit from reconfigurability. Two frameworks for studying reconfigurable systems are proposed that allow for analyzing systems that undergo changes over time. The first framework, using Non-Homogeneous Markov Models (NHMM), allows for identifying useful configurations or states. The second framework, based on control theory, enables issues such as reconfiguration time, and dynamics of the reconfiguration process to be assessed. The application of these models is shown for a PS V in a future human exploration mission that can reconfigure to respond to changing terrain conditions. The results for the specific case considered show that reconfigurability improves the performance of the vehicle over the course of a simulated sortie by approximately 30%.
Genya Ishigami - One of the best experts on this subject based on the ideXlab platform.
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statistical mobility prediction for Planetary Surface exploration rovers in uncertain terrain
International Conference on Robotics and Automation, 2010Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Planetary Surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of terrain physical parameters, however in practical scenarios knowledge of terrain parameters contains significant uncertainty. In this paper, a statistical method for mobility prediction that incorporates terrain uncertainty is presented. The proposed method consists of two techniques: a wheeled vehicle model for calculating vehicle dynamic motion and wheel-terrain interaction forces, and a stochastic response Surface method (SRSM) for modeling of uncertainty. The proposed method generates a predicted motion path of the rover with confidence ellipses indicating the probable rover position due to uncertainty in terrain physical parameters. Rover orientations and wheel slippage are also predicted. The computational efficiency of SRSM as compared to conventional Monte Carlo methods is shown via numerical simulations. Experimental results of rover travel over sloped terrain in two different uncertain terrains are presented that confirms the utility of the proposed mobility prediction method.
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predictable mobility a statistical approach for Planetary Surface exploration rovers in uncertain terrain
IEEE Robotics & Automation Magazine, 2009Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Predictable Mobility : A Statistical Approach for Planetary Surface Exploration Rovers in Uncertain Terrain
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statistical approach to mobility prediction for Planetary Surface exploration rovers in uncertain terrain
International Conference on Robotics and Automation, 2009Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Planetary Surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of te ...
Gaurav Kewlani - One of the best experts on this subject based on the ideXlab platform.
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statistical mobility prediction for Planetary Surface exploration rovers in uncertain terrain
International Conference on Robotics and Automation, 2010Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Planetary Surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of terrain physical parameters, however in practical scenarios knowledge of terrain parameters contains significant uncertainty. In this paper, a statistical method for mobility prediction that incorporates terrain uncertainty is presented. The proposed method consists of two techniques: a wheeled vehicle model for calculating vehicle dynamic motion and wheel-terrain interaction forces, and a stochastic response Surface method (SRSM) for modeling of uncertainty. The proposed method generates a predicted motion path of the rover with confidence ellipses indicating the probable rover position due to uncertainty in terrain physical parameters. Rover orientations and wheel slippage are also predicted. The computational efficiency of SRSM as compared to conventional Monte Carlo methods is shown via numerical simulations. Experimental results of rover travel over sloped terrain in two different uncertain terrains are presented that confirms the utility of the proposed mobility prediction method.
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predictable mobility a statistical approach for Planetary Surface exploration rovers in uncertain terrain
IEEE Robotics & Automation Magazine, 2009Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Predictable Mobility : A Statistical Approach for Planetary Surface Exploration Rovers in Uncertain Terrain
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statistical approach to mobility prediction for Planetary Surface exploration rovers in uncertain terrain
International Conference on Robotics and Automation, 2009Co-Authors: Genya Ishigami, Gaurav Kewlani, Karl D. IagnemmaAbstract:Planetary Surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of te ...
Gregory Michael - One of the best experts on this subject based on the ideXlab platform.
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Planetary Surface dating from crater size frequency distribution measurements poisson timing analysis
Icarus, 2016Co-Authors: Gregory Michael, T Kneissl, Adrian NeesemannAbstract:The predictions of crater chronology models have customarily been evaluated by dividing a crater population into discrete diameter intervals, plotting the crater density for each, and finding a best-fit model isochron, with the uncertainty in the procedure being assessed using 1/√n estimates, where n is the number of craters in an interval. This approach yields an approximate evaluation of the model predictions. The approximation is good until n becomes small, hence the often-posed question: what is the minimum number of craters for an adequate prediction? This work introduces an approach for exact evaluation of a crater chronology model using Poisson statistics and Bayesian inference, expressing the result as a likelihood function with an intrinsic uncertainty. We demonstrate that even in the case of no craters at all, a meaningful likelihood function can be obtained. Thus there is no required minimum count: there is only varying uncertainty, which can be well described. We recommend that the Poisson timing analysis should be preferred over binning/best-fit approaches. Additionally, we introduce a new notation to make it consistently clear that crater chronology model calibration errors are inseparable from stated crater model ages and their associated statistical errors.
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Planetary Surface dating from crater size frequency distribution measurements spatial randomness and clustering
Icarus, 2012Co-Authors: Gregory Michael, T Kneissl, Thomas Platz, N SchmedemannAbstract:We describe a quantitative procedure to measure the degree of clustering in an observed crater population relative to a series of randomly distributed populations. We split the population by according to crater size to be able to identify clustering at different scales, and find that a clustering analysis based on the mean 2nd-closest neighbour distance measure more closely corresponds to visual interpretations of the spatial configuration than the mean closest neighbour distance. Standard deviation of adjacent area is found in certain cases to be an even more sensitive measure. The technique is demonstrated for two sites on Mars. We were able to make use of a case where the spatial distribution was ‘less clustered than random’ to reveal the transition between a crater population superimposed on a lava flow and that belonging to an underlying unit. In general, this type of analysis may give a better insight into the post-formation modification of studied units, enabling a more precise classification of which sizes of craters derive from the original accumulation population and which from areas reSurfaced by later modification events, consequentially improving the accuracy of dating resurfacing events. Clustering analysis is thus an additional tool for understanding the structure of a crater population and the effects causing an observed population to differ from an ideal one.
T Kneissl - One of the best experts on this subject based on the ideXlab platform.
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Planetary Surface dating from crater size frequency distribution measurements poisson timing analysis
Icarus, 2016Co-Authors: Gregory Michael, T Kneissl, Adrian NeesemannAbstract:The predictions of crater chronology models have customarily been evaluated by dividing a crater population into discrete diameter intervals, plotting the crater density for each, and finding a best-fit model isochron, with the uncertainty in the procedure being assessed using 1/√n estimates, where n is the number of craters in an interval. This approach yields an approximate evaluation of the model predictions. The approximation is good until n becomes small, hence the often-posed question: what is the minimum number of craters for an adequate prediction? This work introduces an approach for exact evaluation of a crater chronology model using Poisson statistics and Bayesian inference, expressing the result as a likelihood function with an intrinsic uncertainty. We demonstrate that even in the case of no craters at all, a meaningful likelihood function can be obtained. Thus there is no required minimum count: there is only varying uncertainty, which can be well described. We recommend that the Poisson timing analysis should be preferred over binning/best-fit approaches. Additionally, we introduce a new notation to make it consistently clear that crater chronology model calibration errors are inseparable from stated crater model ages and their associated statistical errors.
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Planetary Surface dating from crater size frequency distribution measurements spatial randomness and clustering
Icarus, 2012Co-Authors: Gregory Michael, T Kneissl, Thomas Platz, N SchmedemannAbstract:We describe a quantitative procedure to measure the degree of clustering in an observed crater population relative to a series of randomly distributed populations. We split the population by according to crater size to be able to identify clustering at different scales, and find that a clustering analysis based on the mean 2nd-closest neighbour distance measure more closely corresponds to visual interpretations of the spatial configuration than the mean closest neighbour distance. Standard deviation of adjacent area is found in certain cases to be an even more sensitive measure. The technique is demonstrated for two sites on Mars. We were able to make use of a case where the spatial distribution was ‘less clustered than random’ to reveal the transition between a crater population superimposed on a lava flow and that belonging to an underlying unit. In general, this type of analysis may give a better insight into the post-formation modification of studied units, enabling a more precise classification of which sizes of craters derive from the original accumulation population and which from areas reSurfaced by later modification events, consequentially improving the accuracy of dating resurfacing events. Clustering analysis is thus an additional tool for understanding the structure of a crater population and the effects causing an observed population to differ from an ideal one.
