MARS

Conceptual Design Phase Trade Model for the Mars Sample Return mission

David Miller, Julien-Alexandre Lamamy

Executive Summary

The Mars Surface Exploration (MSE) tool is used to analyze the design alternative between MAV-rovers (rovers that carry a Mars Ascent Vehicle) and fetch-rovers (rovers that bring back collected samples to the lander and MAV) in the context of the Mars Sample Return mission. The study shows that with a pin-point landing capability the MAV-rover strategy should be selected. The distinction between the two strategies is less as the landing error increases.

Motivation

From a surface operations point of view, the Mars Sample Return (MSR) mission involves two functionalities: sample collection, followed by sample launch to Mars’ orbit. It is assumed that the Mars Sample Return (MSR) mission consists of three elements: a lander, a Mars Ascent Vehicle (MAV), and a rover. The MAV is the element that provides the sample launch functionality. The rover provides enhanced sample collection functionality thanks to its mobility. Two mission design options are then possible. The first option is to couple the mobility and launch capabilities by having the sample collection rover carry the MAV. This option is called MAV-rover option. The second option is to have the sample collection rover fetch samples and bring them back to the MAV which remains with the lander. This option is named fetch-rover option. On the one hand, fetch-rovers are less massive and less complex than MAV-rovers. On the other hand, MAV-rovers traverse less terrain to collect samples, and consequently have a higher probability of mission success. Using MSE’s analytical capabilities, the purpose of this study is to support the decision making process to the selection of the best strategy.

Mars Surface Exploration Tool

The Mars Surface Exploration (MSE) tool is a systems engineering tool for rover design that was originally developed during MIT’s Space Systems Class of 2003. It fully designs a rover and assesses its performance based on user inputs, such as mission lifetime, wheel size, power system type, and level of autonomy. For the purpose of this study, the tool has been upgraded with a enhanced user interface. It is now possible for the users to select directly the sites they wish to investigate on map of Mars (Figure 1 ). Based on the site distribution, MSE calculates the optimal landing targets and exploration paths for the fetch- and MAV-rover strategies (Figure 2 ).

In addition to the science site selection, the users input information about landing uncertainty. The landing uncertainty is assumed to be one-dimensional, and is referred to as landing segment rather than landing ellipse. The landing segment is in fact the landing ellipse approximated to its major axis. The information required from the user is the direction of the landing approach (angle of the landing segment with the Equator), and the range uncertainty (length of the segment). This information is represented in Figure 2 by a black segment centered on the landing targets of the fetch- and MAV-rovers. 

Design trade analyses

The surface exploration is characterized by the mean distance between lander and sites, and mean distance between sites in the case of fetch- and MAV- strategies, respectively. This mean distance changes according to where the lander actually lands within the landing segment.

The rovers are designed assuming that they land on the landing target (center of the landing segment), for which the mean distance to sites is minimum. For this particular case, Figure 3 shows the trade space of fetch- and MAV-rovers with expected number of samples collected (return) on the y-axis and the total mass of the rover and MAV (surrogate to cost)  on the x-axis. The two optimization objectives are to minimize the mass and maximize the number of samples

collected. Given these objectives, the optimal designs are those that lay on the upper right of Figure 3 . Notice that only two fetch-rover designs are optimal, while six of the MAV-rover designs are optimal. In this situation, the MAV-rover strategy is the best option since it offers more science return, and a wider array of optimal designs to choose from.

The conclusion reached in the above paragraph is based on the assumption that the rover lands on target (best case scenario). The worst case scenario is that it actually lands at one extremity of the landing segment. The mean distance to the science sites is in that case the longest, and the probability of successfully collecting samples at every site is the lowest (the rover has a higher chance to fail along the way). Figure 4 is similar to Figure 3 but with added uncertainty bars related to landing. A first observation is that the science return of MAV-rover designs is more sensitive to landing uncertainty than that of fetch-rovers. Second, varying the uncertainty changes the ranking of the designs. Some MAV-designs that perform better than fetch-rovers in the nominal case show a lower science return in the worst case scenario. The distinction between the two strategies in this situation is less obvious than it is in the nominal case. Further work on the impact of landing uncertainty is currently being conducted.