SBIR-STTR Award

Energid Technologies proposes a game-theory inspired simulation tool for testing and validating robotic lunar and planetary missions. It applies Monte Carlo simulation within a multi-optimization environment tailored to the needs of NASA. Stochastic optimization is combined with randomized simulation to maximize multiple statistical measures of performance and calculate the parameters giving the extreme scenarios. The tool works with continuous parameters, such as mass and terrain properties, and with discrete parameters, such as lighting selection, gearing selection, and navigation parameters. It includes accurate modeling of sensors and terrain interaction using calculations performed on Graphical Processing Units (GPUs). The technique proposed is computationally expensive, but highly parallelizable, and the approach includes a design for distribution of computational burden over multiple computers, GPUs, clusters, and cloud configurations. The proposed combination of fast algorithms and game-theory-inspired statistical optimization will provide a powerful tool for NASA's use in planning missions.

Energid Technologies' innovation is an easy-to-use suite of simulation tools for formulating, testing, and implementing robotic missions for lunar and planetary environments. It will aid multiple phases of mission planning, from hardware development to deployment by providing a way to simulate and evaluate maneuvers under complex and uncertain conditions. It is designed to be used to seek out conditions that maximize the probability of either system failure or success. The multioptimization tool draws on concepts from game theory to find difficult corner cases by optimizing over multiple goals. The framework allows for profoundly unknown variables (those without known probability distributions) to be considered within an application, by determining values of these variables for which failure or success is likely to occur. A static design analysis tool draws on CAD model analysis and lifecycle probability distributions to determine system failure points. A terrain analysis tool builds on the stochastic optimization framework to provide detailed user feedback on available maneuvers. Each of these tools is dependent on an accurate simulation of the underlying system and sensor behavior. Therefore, in addition to the effort to develop the tools themselves, a portion of the innovation is the design and implementation of terramechanics, illumination, and advanced mechanism simulations for lunar and planetary environments.