Projected Improvements in Human and Robot Performance

Since collaborative human-robot missions (large telescope assembly, planetary exploration, etc.) will likely occur no sooner than two decades from now, it is important to project the improvements that are likely to develop during that interval in the performance of both humans and robots before attempting to determine the best ways for humans to use robots in space.

Revolutionary technologies can both magnify and reduce the scale and resolution of human sensory, motive and cognitive skills, allowing humans to perceive, move and operate, and perhaps even think at multiple scales beyond their natural physiological limits, without increasing stress. Examples are space suits that convert in-situ resources to allow humans to move, work, communicate and breathe better than with current technologies -- enabling them to jump or fly over large obstacles, carry heavy loads and apply large forces, sense farther and more sharply, control machines by means of their brain-waves, etc.

Similarly, advances in robotics will allow deployment of machines that are much more capable than current robots in the performance of many important functions.

We computed the integrated net performance (performance minus resource demands) of humans, robots, and human-robot teams over future decades, as applied to a possible future mission to assemble a large space telescope known as "FAIR-DART" (Filled Aperture Infrared-Dual Anamorphic Reflector Telescope). While the specific results presented here apply to telescope assembly, our approach to projecting performance improvements is common to all applications.

The initial Condition at 0 Years does not reflect current differences in Human vs Robot Technologies; an estimate of Human EVA of ~20 bits has been obtained from prior studies. The plot for Human technology would have to be displaced upward by this amount in order to reflect such an estimate. Reference: Rodriguez, et al, Human-Robot Performance Analysis Methods, JPL Report, Aug. 6, 2002.

The greatest net performance improvements are expected to come from human-robot teams.