This doctoral thesis extends analytical terramechanic modelling for small lightweight mobile robots operating on sandy soil. Previous terramechanic models were designed to capture and predict the mean values of the forces and sinkage that a wheel may experience. However, these models do not capture the fluctuations in the forces and sinkage that were observed in experimental data. The model developed through the course of this research enhances existing terramechanic models by proposing and validating a new pressure-sinkage relationship. The resulting two-dimensional model was validated with a unique high fidelity single-wheel testbed (SWTB) which was installed on a Blohm Planomat 408 computer-numerically controlled creepfeed grinding machine. The new SWTB translates the terrain in the horizontal direction while the drivetrain and wheel support systems are constrained in the horizontal direction but allowed to freely move in the vertical direction. The design of the SWTB allowed for a counterbalance to be installed and, as a result, low normal loads could be examined. The design also took advantage of the grinding machine's high load capacity and precise velocity control. Experiments were carried out with the new SWTB and predictable repeating ridges were found in the track of a smooth rigid wheel operating in sandy soil. To ensure that these ridges were not an artifact of the new SWTB a mobile robot was used to validate the SWTB findings, which it did. The new SWTB is a viable method for investigating fundamental terramechanic issues. A series of experiments at different slip ratios and normal loads were carried out on the SWTB to validate the new pressure-sinkage relationship which explicitly captures and predicts the oscillations about the mean values for the forces and sinkage values for both a smooth wheel and a wheel with grousers. The new pressure-sinkage relationship adds two new dimensionless empirical factors to the well known pressure-sinkage relationship for a rigid wheel. The first new factor accounts for changes in the local density of the terrain around the wheel and the second factor accounts for the effects grousers have on the forces and sinkage.

The desire to send humans to Mars will require a change in the way that extravehicular activity (EVA) is performed; in-space crews (including those within a vehicle or habitat monitoring others conducting EVA) will need to be more autonomous and that will require them to monitor large amounts of information in order to ensure crew safety and mission success. The amount of information to perceive and process will overwhelm unassisted intra-vehicular (IV) crewmembers, meaning that automation will need to be developed to support these crews on Mars while EVA is performed (Mishkin, Lee, Korth, & LeBlanc, 2007). This dissertation seeks to identify the information requirements for the performance of scientific EVA and determine which information streams will need to be allocated to in-space crew and which are the most effective streams to automate. The first study uses Mars rover operations as a homology—as defined by von Bertalanffy (1968)—to human scientific exploration. Mars rover operations personnel were interviewed using a novel method to identify the information requirements to perform successful science on Mars, how that information is used, and the timescales on which those information streams operate. The identified information streams were then related to potential information streams relevant to human exploration in order to identify potential function allocation or automated system development areas. The second study focused on one identified mission-critical information stream for human space exploration: monitoring astronaut status physiologically. Heart rate, respiration rate, and heart rate variability measurements were recorded from participants as they performed field science tasks (potentially tasks that are similar to those that will be performed by astronauts on Mars). A statistical method was developed to analyze this data in order to determine whether or not physiological responses to different tasks were statistically different, and whether any of those differences followed consistent patterns. A potential method to automate the monitoring of physiological data was also described. The results of this work provide a more detailed outline of the information requirements for EVA on Mars and can be used as a starting point for others in the exploration community to further develop automation or function allocation to support astronauts as they explore Mars.