Human and robotic testing of a Space Suit Simulator (S3) was performed by Professor Newman and her team and MIT for NASA colleagues (Dr. Schaffner of Wyle Laboratories and Dr. Gernhardt of NASA Johnson Space Center). The S3 program involved testing a right knee joint brace, or joint suit simulator, on the Robotic Space Suit Tester (RSST) at MIT in the Man-Vehicle Laboratory. Testing requirements achieved included: characterization of the sensitivity of the joint torque versus angle relationship on joint displacement speed; hysteresis behavior of the joint; quantification of the adjustability of joint range of motion; and the adjustability of joint friction and stiffness. All knee results show hysteresis of the joint, which is a characteristic of both the robot and actual space suit joint torques. Adjusting trajectory speed has little effect on maximum joint torques. For range of motion, three configurations of the knee joint were tested: one without any limitation (0º - 130º), and two smaller ranges (20º - 110º and 40º - 90º). Torques were higher only for the most highly constrained case. As expected, joint torque increases with friction, but we also found that the range of motion decreases with increasing friction. Preliminary testing of aerogel swatches for EVA thermal protection was also performed. The swatches were sewn into arm sections and tested over the RSST elbow. These garments were considerably stronger than expected, and allowed for 130 degrees of movement without failing.

Another research deliverable under this project included developing a 3D EVA mission planning tool. Applied to future planetary manned missions, an EVA mission planner will assist astronauts plan and navigate their traverses, and will also inform the crewmember of planetary terrain mapping (digital elevation maps, editing, waypoint automation and path planning, 3D viewing and zooming), sun illumination levels, life support status, metabolic costs, scientific timelines and emergency walkback calculations. This research effort investigated the appropriate balance between humans and automation for geospatial path problem solving within the high-risk domain of human planetary surface exploration, where decisions are time critical and humans must adapt to uncertainty.

As a human-machine interface, the planner should function efficiently and intuitively with the astronaut. Cognitive decision making tools were studied, which showed that the path planning process should include 3 steps: collecting external information, integrating the information together with reference to costs and goals, and generating the path iteratively using a sensitivity analysis. The EVA mission planner allows for the collection of all pertinent information, such as physiological factors, navigating waypoints, obstacle settings/angles etc, and then uses a geographical information system for terrain data. An optimization algorithm then determines the most efficient path to achieve the desired waypoints using an EVA metabolic rate model. The best path is shown on a map in 3D for clear guidance to the astronaut. Early trials of the planner have proven to be a success. Navigating system will be highly valuable to astronauts during EVA on planetary surfaces.

The final component of this research effort was to review and quantify space suit biomechanics and energetics for planetary locomotion EVAs. We developed a framework for comparison of energetics data across and between all published lunar EVA studies published in the literature. Major factors affecting the energetic cost of movement while wearing a space suit include: suit pressurization, gravity, velocity, surface slope, and space suit configuration. Apollo lunar surface EVA on-foot locomotion metabolic rates, while unexpectedly low, were higher than other activity categories. In a follow on publication, we hypothesized that space suit legs act as springs during running, thereby lowering cost of transport relative to space-suited walking. We transformed data from suited and unsuited energetics studies into a common format and developed a regression equation for specific resistance, S, (energy per weight per distance) based on the Froude number (a non-dimensional parameter associated with gait transitions), surface slope, gravity, and space suit pressure. A regression on S achieved an adjusted R2 of 0.83; all factors were significant (P<0.0005). No additional evaluated factors met the acceptance criteria. The categorical regression, but not the hypothesis test, suggested that the running group had reduced efficiency per unit time; both tests suggested that the running group had increased efficiency per unit distance. Findings suggest that gas-pressure space suit legs function as springs during running, including the finding of higher efficiency per unit distance during running, despite the presumed increased work against space suit joint torques at higher velocities.

The EVA mission planner will aid in the safe and efficient planning of any earth traverse, particularly where the subject/group needs to manage navigation, physiologic- and mission-specific information, all in time-finite situations. Planning traversals for complex scientific or information-rich exploration can be dangerous and inefficient. Further, managing significant information becomes increasingly difficult when the task changes to real-time re-planning of a traversal, i.e., new discoveries or discrepancies between planned and observed conditions demanding immediate selection of a new path while not violating mission constraints. Applications for this mission planner includes basic hiking and exploring, various scientific missions - such as geologic surveys in desert conditions - and especially expeditions to any extreme environments, such polar expeditions and deep-sea diving.

A planetary EVA mission planner was also developed and tested as part of the funded project. This study focused on improving planetary EVAs through human and automation collaboration by investigating the real-time task of traversal planning and re-planning, and creating a tool for such EVA mission planning. Cognitive decision making tools were studied, which showed that the path planning process should include 3 steps: collecting external information, integrating the information together with reference to costs and goals, and generating the path iteratively using a sensitivity analysis. The EVA mission planner allows for the collection of all pertinent information, such as physiological factors, navigating waypoints, obstacle settings/angles etc, and then uses a geographical information system for terrain data. An optimization algorithm then determines the most efficient and safe path to traverse the desired waypoints using an EVA metabolic rate model. The best path is shown on a map in 3D for clear guidance to the astronaut. Early trials of the planner around the MIT campus have proven to be a success.

As a further component to the research program, we quantified space suit biomechanics and energetics using a partial gravity simulator. Metabolic costs limit the duration and intensity of extravehicular activity (EVA), an essential component of future human missions to the Moon and Mars. We developed a framework for comparison of energetics data across and between published partial gravity EVA studies. We found that gas-pressure space suit legs function as springs during running, including the finding of higher efficiency per unit distance during running, despite the presumed increased work against space suit joint torques at higher velocities.

To support current advanced EVA biomedical and metabolic research and to provide modeling, data analysis and robotic spacesuit testing for partial-gravity exploration in support of human spaceflight, we will research in three related areas of our expertise to enhance our collective understanding of astronaut locomotion, human and robotic tools, and rigorous data analysis and modeling for exploration EVA.

Specific Aims

1. Identify biomechanics and energetics knowledge gaps for EVA, provide data analysis support for ongoing NASA EVA experiments (underwater, pogo and parabolic flight), and perform enhanced MIT exoskeleton (EVA simulator) locomotion experiments.

2. Produce EVA dynamic analysis and modeling for partial gravity and simulated environments in collaboration with NASA Johnson Space Center (JSC).

3. Conduct human and robotic testing for advanced EVA motions utilizing the robotic spacesuit tester currently on loan to MIT from NASA, develop task-specific parametric analyses, and further EVA path planning capabilities by incorporating metabolic algorithms from JSC and enhancing the current MIT capability by incorporating numerous representative EVA tasks.