Aaron William Johnson

An Integrated Traverse Planner and Analysis Tool for Planetary Exploration

Future planetary explorations will require surface traverses of unprecedented frequency, length, and duration. As a result, there is need for exploration support tools to maximize productivity, scientific return, and safety. The Massachusetts Institute of Technology is currently developing such a system, called the Surface Exploration Traverse Analysis and Navigation Tool (SEXTANT). The goal of this system is twofold: to allow for realistic simulations of traverses in order to assist with hardware design, and to give astronauts an aid that will allow for more autonomy in traverse planning and re-planning. SEXTANT is a MATLAB-based tool that incorporates a lunar elevation model created from data from the Lunar Orbiter Laser Altimeter instrument aboard the Lunar Reconnaissance Orbiter spacecraft. To assist in traverse planning, SEXTANT determines the most efficient path across a planetary surface for astronauts or transportation rovers between user-specified Activity Points. The path efficiency is derived from any number of metrics: the traverse distance, traverse time, or the explorer’s energy consumption. The generated path, display of traverse obstacles, and selection of Activity Points are visualized in a 3D mapping interface. After a traverse has been planned, SEXTANT is capable of computing the most efficient path back home, or “walkback”, from any point along the traverse – an important capability for emergency operations. SEXTANT also has the ability to determine shadowed and sunlit areas along a lunar traverse. This data is used to compute the thermal load on suited astronauts and the solar power generation capacity of rovers over the entire traverse. These both relate directly to the explorer’s consumables, which place strict constraints on the traverse. This paper concludes by presenting three example traverses, detailing how SEXTANT can be used to plan and modify paths for both explorer types.

Decision-Making and Risk-Taking Behavior in Lunar Landing

Model-based simulation and human subject experiments can be used to develop quantitative methods for analyzing the human-automation task allocation of a system early in the design process. An integral part of the human-system model is a representation of human decision-making and risk-taking behavior. These behaviors were investigated in a lunar landing human subject experiment. Subjects were asked to select a landing aim point that was near both a point of interest and hazardous region. It was expected that the placement of the landing aim point would vary with the probability of manual versus automatic flight and whether estimated touchdown dispersions were remembered by the subjects from earlier in the experiment or presented graphically on scatter plots. The experiment found that subjects did systematically modify the placement of landing aim points. Further, presenting landing dispersions graphically allowed subjects to compensate for touchdown deviations in both risk-critical and non-risk critical directions.

A Closed-Loop Model of Operator Visual Attention, Situation Awareness, and Performance Across Automation Mode Transitions

Objective: This article describes a closed-loop, integrated human–vehicle model designed to help understand the underlying cognitive processes that influenced changes in subject visual attention, mental workload, and situation awareness across control mode transitions in a simulated human-in-the-loop lunar landing experiment. Background: Control mode transitions from autopilot to manual flight may cause total attentional demands to exceed operator capacity. Attentional resources must be reallocated and reprioritized, which can increase the average uncertainty in the operator’s estimates of low-priority system states. We define this increase in uncertainty as a reduction in situation awareness. Method: We present a model built upon the optimal control model for state estimation, the crossover model for manual control, and the SEEV (salience, effort, expectancy, value) model for visual attention. We modify the SEEV attention executive to direct visual attention based, in part, on the uncertainty in the operator’s estimates of system states. Results: The model was validated using the simulated lunar landing experimental data, demonstrating an average difference in the percentage of attention ≤3.6% for all simulator instruments. The model’s predictions of mental workload and situation awareness, measured by task performance and system state uncertainty, also mimicked the experimental data. Conclusion: Our model supports the hypothesis that visual attention is influenced by the uncertainty in system state estimates. Application: Conceptualizing situation awareness around the metric of system state uncertainty is a valuable way for system designers to understand and predict how reallocations in the operator’s visual attention during control mode transitions can produce reallocations in situation awareness of certain states.

Multi-Vehicle Lunar Operations Simulation Using SEXTANT

With increasingly higher resolution maps of planetary bodies becoming available, it now possible to predict operational properties of exploration missions using accurate vehicle path planning tools. This, in turn, could lead to a change in the way we view rover operational simulation during design. To this end, the Massachusetts Institute of Technology, along with Aurora Flight Sciences, has enhanced the Surface Exploration Traverse Analysis and Navigation Tool (SEXTANT). Previously developed as a tool to assist astronauts during extra-vehicular activities, SEXTANT now allows a designer to simultaneously plan the path of multiple rovers, each with different properties, within a single exploration mission. It is also paired with a rover modeling tool which estimates the mass of each of the rovers depending on the payload and operational requirements placed on them. It thus provides a feedback loop between operational conditions and system design. SEXTANT therefore has two major functionalities: (1) it allows for the realistic simulation of vehicle traverses to assist in hardware design and (2) it allows for pre-mission multi-vehicle path planning, which in turn allows the user to understand the operational properties of multi-asset lunar exploration. The paper will detail the capabilities of SEXTANT, including its ability to compute optimal paths, energy expenditure, illumination and communication visibility. It also includes a collision avoidance algorithm which enforces safe spacing between vehicles and an energy profile tool which ensures that solar powered rovers receive enough illumination to complete the traverse without running out of energy. The paper includes two cases studies. The first demonstrates how SEXTANT can be used to inform the design of the rover in the early mission concept phase and understand how the path affects the design. The second demonstrates that the collision avoidance algorithm can help plan the path of multiple rovers prior to a mission, thus reducing the software needs onboard the vehicles and allowing for routes to be pre-planned. This in turn increases the overall number of sites visited during the mission, and hence increases the mission’s science return.

Evaluation of a Surface Exploration Traverse Analysis and Navigation Tool

SEXTANT is an extravehicular activity (EVA) mission planner tool developed in MATLAB, which computes the most efficient path between waypoints across a planetary surface. The traverse efficiency can be optimized around path distance, time, or explorer energy consumption. The user can select waypoints and the time spent at each, and can visualize a 3D map of the optimal path. Once the optimal path is generated, the thermal load on suited astronauts or solar power generation of rovers is displayed, along with the total traverse time and distance traveled. A field study was conducted at the Mars Desert Research Station (MDRS) in Utah to see if there was a statistical difference between the SEXTANT-determined energy consumption, time, or distance of EVA traverses and the actual output values. Actual traverse time was significantly longer than SEXTANTpredicted EVA traverse time (n=6, p<0.01), traverse distance was not significantly different than SEXTANT-predicted distance, and explorer energy consumption was significantly greater than SEXTANT-predicted energy consumption (n=5, p<0.01). A second study was done to see if mission re-planning, or contingency planning, was faster and less work when using SEXTANT in the habitat or in the field using an iPad. Time and workload measurements were collected for each subject under both conditions. Contingency planning in the habitat was not significantly different than contingency planning in the field. There was no significant workload difference when contingency planning in either location, however there was a trend that suggested contingency planning was faster in the habitat (n=3, p=0.07). Every subject commented that it was a hassle to carry the mission planner in the field and it was difficult to see the screen in the sunlight. To determine if gloves were a factor in the difference between mission re-planning time, subjects were asked to plan a contingency indoors with and without gloves. Performance and workload were not significantly different when re-planning with and without the gloves. The SEXTANT mission planner will continue to be improved according to the results and the recommendations of subjects in this study.

Investigation Of The Relative Merits Between DAS And ORSAT For Small Satellite Reentry Analysis

The Glory program is a low-earth satellite that has both Earth and solar science objectives. Glory is required to meet NASA safety standard 1740.14, Guidelines and Assessment Procedures for Limiting Orbital Debris. The post-mission disposal method chosen for Glory is an uncontrolled atmospheric reentry option. Specifically, Guideline 7-1 requires the spacecraft to complete a Reentry Survivability Analysis (RSA). The guidelines intent is to limit the risk of human casualty by minimizing the surviving components of a spacecraft. Adjustments can be made to the allowable debris casualty area (DCA) depending on reentry year and inclination to keep the risk of human casualty constant (1 in 10,000). Recently Glory underwent a RSA, which was completed using two tools: the debris assessment software (DAS), version 1.5.3, and the object reentry survival analysis tool (ORSAT). The outcome of the RSA is very important to the mission. If the DCA is higher than the allowable, then the mission must pursue one of the possible resolutions that are discussed. If the ORSAT results are the end products that are assessed for policy compliance, then why use DAS at all? If done early DAS analysis can provide an indication of components that should be more carefully analyzed, which can indicate if steps should be taken in the design prior to a potential impact. DAS also helps prepare inputs for ORSAT, which has different input methods, but the information needed is much the same. This paper will strive to explore some of the pitfalls and over-conservatism that may cause difficulties when using the DAS tool and whether the effort is really a necessary task.