Human Competitive Text File (1) The complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result Evolutionary Computation Technologies for the Automatic Design of Space Systems (2) The name, physical mailing address, e-mail address, and phone number of EACH author of EACH paper Richard J. Terrile, rich.terrile@jpl.nasa.gov 818 354-6158 Hrand Aghazarian Hrand.Aghazarian@jpl.nasa.gov 818 354-5121 Michael I. Ferguson Michael.I.Ferguson@jpl.nasa.gov 818 393-6967 Wolfgang Fink Wolfgang.Fink@jpl.nasa.gov 818 393-2695 Terry Huntsberger Terrance.L.Huntsberger@jpl.nasa.gov 818 354-5794 Didier Keymeulen Didier.Keymeulen@jpl.nasa.gov 818 354-4280 Gerhard Klimeck gekco@jpl.nasa.gov 818 354-2182 Mark Kordon Mark.A.Kordon@jpl.nasa.gov 818 393-0476 Seungwon Lee Seungwon.Lee@jpl.nasa.gov 818 393-7720 Boris Oks Boris.Oks@jpl.nasa.gov 818 393-7551 Chris Peay Chris.S.Peay@jpl.nasa.gov 818 354-3782 Anastassios Petropoulos anastassios.e.petropoulos@jpl.nasa.gov 818 354-1509 Paul von Allmen pva@jpl.nasa.gov 818 393-7520 Karl Yee Karl.Y.Yee@jpl.nasa.gov 818 393-3523 Physical Mailing address for all: Jet Propulsion Lab 4800 Oak Grove Drive Pasadena , CA 91109 (3) The name of the corresponding author (to whom notices will be sent concerning the competition) Richard J. Terrile (4) The abstract of the paper(s) Paper 1 The Evolvable Computation Group, at NASA's Jet Propulsion Laboratory (JPL), is tasked with demonstrating the utility of computational engineering and computer optimized design for complex space systems. The group is comprised of researchers over a broad range of disciplines including biology, genetics, robotics, physics, computer science and system design, and employs biologically inspired evolutionary computational techniques to design and optimize complex systems. Over the past two years we have developed tools using genetic algorithms, simulated annealing and other optimizers to improve on human design of space systems. We have further demonstrated that the same tools used for computer-aided design and design evaluation can be used for automated innovation and design, and be applied to hardware in the loop such as robotic arms and MEMS micro-gyroscopes. These powerful techniques also serve to reduce redesign costs and schedules. Paper 2 We address the problem of optimizing a spacecraft trajectory by using three different multi-objective evolutionary algorithms: i) Non-dominated sorting genetic algorithm, ii) Pareto-based ranking genetic algorithm, and iii) Strength Pareto genetic algorithm. The trajectory of interest is an orbit transfer around a central body when the spacecraft uses a low-thrust propulsion system. We use a Lyapunov feedback control law called the Q-law to create an eligible trajectory, while the Q-law control parameters are selected with the multi-objective algorithms. The optimization goal is to minimize flight time and consumed propellant mass simultaneously. The Pareto fronts (trade-off surface between flight time and propellant mass) produced by these algorithms are evaluated by means of two quantitative metrics: 1) size of the dominated space and 2) coverage of two Pareto fronts. With the two metrics, a hierarchy of algorithms emerged. The non-dominated sorting genetic algorithm and the strength Pareto genetic algorithm are equally effective, and they outperform the Pareto-based ranking genetic algorithm. Paper 3 We propose a tuning method for MEMS gyroscopes based on evolutionary computation to efficiently increase the sensitivity of MEMS gyroscopes through tuning and, furthermore, to find the optimally tuned configuration for this state of sensitivity. The tuning method tested for the second generation JPL/Boeing Post-resonator response of the MEMS device in open-loop operation. (5) A list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies E, F and G (6) A statement stating why the result satisfies that criteria (use the examples below as a guide as to possible forms of this "statement") We claim that we have demonstrated human competitive performance in four separate implementations and they cover criteria E, F and G. The areas are 1) automated design of spacecraft power sub-systems, 2) optimization of low-thrust trajectories, 3) path planning for the deployment of robotic arms and 4) tuning of MEMS Micro-gyroscopes. The common thread through these implementations are that they are part of a program to demonstrate that conventional computer aided design software models can be incorporated into an evolutionary framework and made to operate in the inverse sense to automatically innovate and optimize designs. E - The result is equal to or better than the most recent human-created solution to a long- standing problem for which there has been a succession of increasingly better human- created solutions. 1) A design tool call MMPAT (Multi-Mission Power Analysis Tool) was integrated into an evolvable framework and ran a population of designs using the mission profile for the NASA/JPL Deep Impact mission. The evolved power system designs showed better performance (power margins) and smaller use of resources (smaller battery and solar panel size) than the Deep Impact mission design that was flown by NASA/JPL. 2) We created a design tool that optimizes the flight time and fuel consumption for ion-drive, low-thrust trajectories. This tool optimizes a control law and delivers 10% increased payload and up to 50% decrease in flight times over conventional implementations of the control law design tools. When compared to exhaustive computational methods our tool matches the optimum performance at a fraction of the computation time. 3) We have demonstrated a path planning tool in our genetic framework that generates the optimum arm path given a terrain map. This has been tested on the 5 DOF (degrees of freedom) rover arm ground software and hardware and has been approved for testing on the Opportunity Mars Exploration Rover (MER) on the surface of Mars. We expect the Mars surface test to be performed during the current extended mission. The algorithm solves a difficult problem in less than a second that is now done in several hours by ground technicians. 4) A hands-off tuning platform was developed to tune MEMS Micro-gyroscopes so that the X and Y axis have equal performance. This is a required step in order to allow the gyros to work with high sensitivity. Our platform delivers half the frequency difference (compared to conventional tuning) between X and Y axis in less than one hour. Conventional tuning by humans requires from 4 to 8 hours. F - The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. 1) MMPAT is used on all JPL spacecraft mission designs, but never in an optimized sense that explores vast volumes of design space. The Deep Impact human optimized design was flown. 2) Q-law control laws were consider a breakthrough at the time of their invention, but were difficult to optimize. Generally, they were used by weighting all of the orbit parameters equally. Our genetic algorithm-based Q-law trajectory tools find the optimum weights and are faster and better at optimizing orbit transfers than the previously implemented design tools and are now in use for trajectory design. 4) MEMS-Micro gyros can only be used as sensitive devices if they are individually tuned because of fabrication variations. A tuned micro gyroscope is a large achievement in size reduction if they can be efficiently tuned and stay in that state. Hands-off auto tuning enables that achievement. G - The result solves a problem of indisputable difficulty in its field. 1) The large number of variables, the shortness of design and redesign schedules and the difficultly of the optimization requires and experienced design engineer to iterate around a point design derived from experience. For space craft power systems, there has never been a tool available that can automatically provide the optimum solution for a give set of requirements, 2) Low-thrust propulsion systems are relatively new and offer a new capability for interplanetary spacecraft. However, the difficulty in designing efficient trajectories has limited their usefulness in enabling future implementations. Our new tools offer a fast method for calculating optimum trajectories and trading off flight time for payload. 3) Instrument placement on a rough surface based stereo imaging data from a 5 degrees of freedom robotic arm is extremely challenging. Currently, technicians can only find a safe path in several hours without regard to the shortest or safest (greatest clearance from collision) paths. Our algorithms allow for providing a metric basis for path planning (fitness). 4) Tuning of micro-gyros is a non-linear setting of voltages and capacitances that have non-intuitive effects on the resonant gyro frequencies. It is a tedious and extremely difficult task that benefits greatly from evolutionary automation. (7) A full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable). Terrile, R. J., Aghazarian, H., Ferguson, M. I., Fink, W., Huntsberger, D. Keymeulen, T. L., Klimeck, G., Kordon, M. A., Lee, S. and von Allmen, P. A. (2005) "Evolutionary Computation Technologies for the Automatic Design of Space Systems" NASA/DOD Evolvable Hardware Conference Proceedings, Washington, DC, June 2005. Lee, S., von Allmen, P., Fink, W., Petropoulos, A. E. and Terrile, R. J. (2005) "Comparison of Multi-Objective Genetic Algorithms in Optimizing Q-law Low- Thrust Orbit Transfers." GECCO Conference Proceedings, Washington, DC, June 2005. Keymeulen, D., Fink, W., Ferguson, M. I., Peay, C., Oks, B., Terrile, R. and Yee, K. (2005) "Evolutionary Computation applied to the Tuning of MEMS gyroscopes." GECCO Conference Proceedings, Washington, DC, June 2005