Visual Prediction of Rover Slip: Learning Algorithms and Field Experiments

Perception of the surrounding environment is an essential tool for intelligent navigation in any autonomous vehicle. In the context of Mars exploration, there is a strong motivation to enhance the perception of the rovers beyond geometry-based obstacle avoidance, so as to be able to predict potential interactions with the terrain. In this thesis we propose to remotely predict the amount of slip, which reflects the mobility of the vehicle on future terrain. The method is based on learning from experience and uses visual information from stereo imagery as input. We test the algorithm on several robot platforms and in different terrains. We also demonstrate its usefulness in an integrated system, onboard a Mars prototype rover in the JPL Mars Yard. Another desirable capability for an autonomous robot is to be able to learn about its interactions with the environment in a fully automatic fashion. We propose an algorithm which uses the robot's sensors as supervision for vision-based learning of different terrain types. This algorithm can work with noisy and ambiguous signals provided from onboard sensors. To be able to cope with rich, high-dimensional visual representations we propose a novel, nonlinear dimensionality reduction technique which exploits automatic supervision. The method is the first to consider supervised nonlinear dimensionality reduction in a probabilistic framework using supervision which can be noisy or ambiguous. Finally, we consider the problem of learning to recognize different terrains, which addresses the time constraints of an onboard autonomous system. We propose a method which automatically learns a variable-length feature representation depending on the complexity of the classification task. The proposed approach achieves a good trade-off between decrease in computational time and recognition performance.</p

Adaptive Localization and Mapping for Planetary Rovers

Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach

Mars Sedimentology and Stratigraphy

This conference seeks to stimulate the exchange of ideas among the community of scientists with common interests in sedimentary processes and the stratigraphic record of sedimentary rocks on Mars. Topical sessions will include weathering processes, provenance, and diagenesis of sediments; transport and depositional processes (fluvial, eolian, lacustrine, evaporitic, volcaniclastic, and impact), both past and present; characterization and origin of vast exposures of layered bedrock; controls on stratigraphic stacking patterns and stratal geometry; and the evolution of sedimentary basins, including patterns of deformation.sponsors, California Institute of Technology ... [and others]conveners, John Grotzinger, David Beaty ; scientific organizing committee, Gilles Dromart ... [and others].PARTIAL CONTENTS: Sediment Prediction Through Basin Analysis: An Example from Acidalia Planitia--Geologic Analysis of Various Hydrated Formations Exposed on the Plateaus Surrounding Valles Marineris, Mars--Fluvial Sediment Accomodation and Mesoscale Architecture, Some Neglected Perspectives--Wind-eroded Floor Deposits in Noachian Degraded Craters on Mars--Geobiology and Sedimentology of the Hypersaline Great Salt Lake, Northern Utah, USA: Analogues for Assessing Watery Environments on Mars

Mars delivery service - development of the electro-mechanical systems of the Sample Fetch Rover for the Mars Sample Return Campaign

This thesis describes the development of the Sample Fetch Rover (SFR), studied for Mars Sample Return (MSR), an international campaign carried out in cooperation between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The focus of this document is the design of the electro-mechanical systems of the rover. After placing this work into the general context of robotic planetary exploration and summarising the state of the art for what concerns Mars rovers, the architecture of the Mars Sample Return Campaign is presented. A complete overview of the current SFR architecture is provided, touching upon all the main subsystems of the spacecraft. For each area, it is discussed what are the design drivers, the chosen solutions and whether they use heritage technology (in particular from the ExoMars Rover) or new developments. This research focuses on two topics of particular interest, due to their relevance for the mission and the novelty of their design: locomotion and sample acquisition, which are discussed in depth. The early SFR locomotion concepts are summarised, covering the initial trade-offs and discarded designs for higher traverse performance. Once a consolidated architecture was reached, the locomotion subsystem was developed further, defining the details of the suspension, actuators, deployment mechanisms and wheels. This technology is presented here in detail, including some key analysis and test results that support the design and demonstrate how it responds to the mission requirements. Another major electro-mechanical system developed as part of this work is the one dedicated to sample tube acquisition. The concept of operations of this machinery was defined to be robust against the unknown conditions that characterise the mission. The design process led to a highly automated robotic system which is described here in its main components: vision system, robotic arm and tube storage

Effects of Turning Radius on Skid-Steered Wheeled Robot Power Consumption on Loose Soil

This research highlights the need for a new power model for skid-steered wheeled robots driving on loose soil and lays the groundwork to develop such a model. State-of-the-art power modeling assumes hard ground; under typical assumptions this predicts constant power consumption over a range of small turning radii where the inner wheels are rotating backwards. However, experimental results performed both in the field and in a controlled laboratory sandbox show that, on sand, power is not in fact constant with respect to turning radius. Power peaks by 20% in a newly identified range of turns where the inner wheels rotate backwards but are being dragged forward. This range of turning radii spans from half the rover width to R', the radius at which the inner wheel is not commanded to turn. Data shows higher motor torque and wheel sinkage in this range. To progress toward predicting the required power for a skid-steered wheeled robot to maneuver on loose soil, a preliminary version of a two-dimensional slip-sinkage model is proposed, along with a model of the force required to bulldoze the pile of sand that accumulates next to the wheels as it they are skidding. However, this is shown to be a less important factor contributing to the increased power in small-radius turns than the added inner wheel torque induced by dragging these wheels through the piles of sand they excavate by counter-rotation (in the identified range of turns). Finally, since a direct application of a power model is to design energy-efficient paths, time dependency of power consumption is also examined. Experiments show reduced rover angular velocity in sand around turning radii where the inner wheels are not rotated and this leads to the introduction to a new parameter to consider in path planning: angular slip

Modeling of Wheel-Soil Interaction for Small Ground Vehicles Operating on Granular Soil.

Unmanned ground vehicles continue to increase in importance for many industries, from planetary exploration to military defense. These vehicles require significantly fewer resources compared to manned vehicles while reducing risks to human life. Terramechanics can aid in the design and operation of small vehicles to help ensure they do not become immobilized due to limited traction or energy depletion. In this dissertation methods to improve terramechanics modeling for vehicle design and control of small unmanned ground vehicles (SUGVs) on granular soil are studied. Various techniques are developed to improve the computational speed and modeling capability for two terramechanics methods. In addition, a new terramechanics method is developed that incorporates both computational efficiency and modeling capability. First, two techniques for improving the computation performance of the semi-empirical Bekker terramechanics method are developed. The first technique stores Bekker calculations offline in lookup tables. The second technique approximates the stress distributions along the wheel-soil interface. These techniques drastically improve computation speed but do not address its empirical nature or assumption of steady-state operation. Next, the discrete element method (DEM) is modified and tuned to match soil test data, evaluated against the Bekker method, and used to determine the influence of rough terrain on SUGV performance. A velocity-dependent rolling resistance term is developed that reduced DEM simulation error for soil tests. DEM simulation shows that surface roughness can potentially have a significant impact on SUGV performance. DEM has many advantages compared to the Bekker method, including better locomotion prediction, however large computation costs limit its applicability for design and control. Finally, a surrogate DEM model (S-DEM) is developed to maintain the simulation accuracy and capabilities of DEM with reduced computation costs. This marks one of the first surrogate models developed for DEM, and the first known model developed for terramechanics. S-DEM stores wheel-soil interaction forces and soil velocities extracted from DEM simulations. S-DEM reproduces drawbar pull and driving torque for wheel locomotion on flat and rough terrain, though wheel sinkage error can be significant. Computational costs are reduced by three orders of magnitude, bringing the benefits of DEM modeling to vehicle design and control.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108811/1/wsmithw_1.pd

The physics of wind-blown sand and dust

The transport of sand and dust by wind is a potent erosional force, creates sand dunes and ripples, and loads the atmosphere with suspended dust aerosols. This article presents an extensive review of the physics of wind-blown sand and dust on Earth and Mars. Specifically, we review the physics of aeolian saltation, the formation and development of sand dunes and ripples, the physics of dust aerosol emission, the weather phenomena that trigger dust storms, and the lifting of dust by dust devils and other small-scale vortices. We also discuss the physics of wind-blown sand and dune formation on Venus and Titan.Comment: 72 journal pagers, 49 figure

First Landing Site/Exploration Zone Workshop for Human Mission to the Surface of Mars : October 27–30, 2015, Houston, Texas

The purpose of this workshop is to identify and discuss candidate locations where humans could land, live, and work on the martian surface.Organizer, Lunar and Planetary Institute, Universities Space Research Association, National Aeronautics and Space Administration ; Co-Chairs, Human Landing Sites Study Steering Committee, Benjamin Bussey, National Aeronautics and Space Administration, Richard Davis, National Aeronautics and Space Administratio