OK, so I am reflecting my age with the song lyric that I chose for the title. For my first science blog post I have selected a story about genetic algorithms, which have been used by a research team at Cornell University’s Creative Machines Lab to create virtual robots. These computer-animated robots demonstrate various solutions to the problem of robotic walking. I chose this story in part because I am interested in genetic algorithms and their potential for finding unexpected solutions to engineering problems. But mostly I enjoyed the amusing video the researchers produced showing the robots. (More on the video in a moment.)

Genetic algorithms are biomimicry of a sort, at a very fundamental level. They use DNA, genetics, and evolution as the inspiration for a means to create computer code that models complicated engineering problems and arrives at  interesting solutions.

The diversity and complexity of solutions created by genetic algorithms–in a relatively small number of generations–is suggestive of the possibility that life on earth might have evolved by similar processes. It is hardly proof of evolution, though. Research in other fields provides much stronger evidence supporting the theory of evolution. Genetic algorithms are of interest primarily for their application to solving engineering problems.

In this case, the problem is that of making robots walk. Anyone who has followed the robotic rovers that NASA has developed, for exploring places like Mars, knows that robotic walking is complicated. Just getting them to move can be hard enough. Going over or around obstacles without flipping over or getting stuck is even harder.

The starting point for a genetic algorithm is a collection of entities, in this case robots, created with random configurations of components. Four types of components were used in these robots: hard tissue representing bone, soft tissue, and two types of  “muscle” tissues that either expand first and then contract, or contract and then expand. Each of these components is represented by cubic voxels that are arranged in various configurations in relation to each other.

Once the first generation is created, all of the robots are tested to identify which ones walk the best. The most important test of fitness is the distance a robot can travel, but penalties are also built in. A robot can be penalized for being made up of large numbers of voxels, representing an inefficient weight that requires more energy to carry. It can also be penalized for having too many muscle voxels, which consume higher amounts of energy. A penalty is also assessed for the number of voxels surrounding other voxels, because a solid mass with a lot of interconnected voxels has a lower surface area than a set with few adjoining connections. Such a configuration can reduce the effectiveness of cooling and cause them to overheat in a warm environment.

It is at this point when the genetic modeling comes into play. The highest-scoring robots are allowed to reproduce, but reproduction is not just creating copies. In a sort of numerical mating ritual, the most successful robots are paired off. Each has its genetic coding split at a random location and combined with the complementary string of the other. Multiple offspring are created in this way, each with a different random mash-up of the parents’ genetic codes. The idea is that some of the offspring will inherit the code segments that made their parents good walkers. Better still, some might inherit the code segments from both parents that made them successful. Typical genetic algorithms also have a built-in probability of occasionally introducing new random components, intended to be analogous to mutation.

The new generation is then put through the same set of tests.

The result, spanning 1000 generations, is portrayed in their video, http://www.youtube.com/watch?v=z9ptOeByLA4&feature=youtu.be.

Of course, these are computer animations, not real robots. In particular, they are built from unspecified “muscle” tissues that expand and contract by 20% cyclically. As far as I know, this sort of material does not exist, and the obstacles to creating such materials are among the factors that prevent engineers from building robots as agile and efficient as living creatures. (That’s just the sort of observation that is sure to bring about comments telling me how wrong I am.)

An intriguing aspect of genetic algorithms is that the best coding from one generation is passed on to some members of succeeding generations, without any need to examine the code along the way in order to identify what is good, or what characteristics of the code make it good. The only thing that matters is that the code produces effective walking. It is that approach that leads to unusual and counterintuitive solutions.

One aim for the Cornell researchers was to experiment with using several different types of tissues in each robot, with varying amounts of stiffness and built-in muscle-like activity. Genetic algorithms were first applied to robotic movement almost 20 years ago by Karl Sims, using rigid objects connected by hinges with varying degrees of freedom. Examples can be seen here: http://www.youtube.com/watch?v=bBt0imn77Zg. The unique approach in the Cornell team’s research was to obtain the flexibility from soft and “muscle” tissues instead of hinged connections.

Another interesting aspect of the Cornell research was that they challenged human engineers to come up with robotic designs using the same components. The engineers were unable to achieve results that were as successful as those reached by the genetic algorithms.

The paper, to be published in Proceedings of the Genetic and Evolutionary Computation Conference, can be downloaded here: http://jeffclune.com/publications/2013_Softbots_GECCO.pdf

When plastic products don’t measure up, the cause can be a challenge to identify. Even if the chemical composition and processing parameters haven’t changed, internal structure differences in the materials can cause surface irregularities, leaks, dimensional changes, or distortions with no apparent cause.

In such cases, atomic force microscopy (AFM) might help identify a solution. AFM is a relatively new technology that uses a tiny nanometer-scale bar positioned near the surface of the material. As the beam is moved over the surface of the specimen, individual atoms exert force on it and cause it to bend.  By recording the deformation of the beam, the molecular structure of the material can be mapped out and flaws can be detected.

I met with Mike Mallamaci, co-owner of PolyInsight, today. His Akron-based company is one of only a few independent analytical laboratories to use atomic force microscopy to examine the internal structures of polymeric materials. The lab is equipped to prepare specimens so that both surfaces and cross-sections of plastics can be inspected.

Yesterday’s meeting of the Northeast Ohio Software Association (NEOSA) included a great tour of NASA Glenn Research Center (GRC). We saw three facilities, none of which I had toured before. First stop was the Propulsion Systems Laboratory (PSL), which is used to test jet engines. Glenn Research Center has long provided services to jet engine manufacturers in developing and testing engines for both commercial and military planes. The PSL includes two test chambers that can simulate flight at altitude conditions–low pressure and low temperature–while collecting data from inside and outside the engine. It is not an easy task simulating altitudes up to 90000 ft., mach 4 speed, and -90 degrees F inlet conditions, with a running jet engine in the chamber. One upcoming program will  examine engine icing, which is believed to have been responsible for more than 250 engine flameouts. It is believed that ice forms on aircraft parts and enters the engine, where it melts in the compressor. The liquid water extinguishes the flames in the burner, causing the engine to shut down.

The second stop on the tour was the Electric Propulsion Laboratory. Electric propulsion, or ionic propulsion, is used mainly on satellites and deep space probes. Instead of a chemical rocket propellant, ionic propulsion systems use ionized atoms–typically xenon atoms–as propellants. The ions are accelerated through an electric field and then escape from the engine. The high rate of acceleration results in strong thrust without using a lot of propellant, which allows lighter weight and/or longer missions than can be achieved with chemical fuels. The engines only work in the vacuum of space, though, so once again a large facility was needed to pump air out of the test chamber and simulate high altitudes. A cryo pump is used in order to simulate the nearly total vacuum of space. Most of the air is removed using mechanical pumps, but the remaining few particles are removed by chilling them to almost absolute zero. At such low temperatures the air atoms have almost no energy and simply collect on the liquid-helium-chilled cryo pump surface.

The final stop on the tour was the Research Analysis Center, with its 3D and virtual reality computer facilities. These systems are used by engineers to get an immersive experience visualizing images from space, flow simulations, engineering structures, and anything else where a three-dimensional display is useful, including finding new ways of presenting data. NASA engineers have worked with medical professionals also, who are excited about the possibilities for immersive visualizing of MRIs and other medical images. The lab is also a popular spot among children on school tours. Our guide admitted, though, that with falling prices for games and other entertainment, it was getting harder and harder to show the children things they had not already seen.

A reception followed the tour with delicious refreshments and a chance to meet astronaut Michael Foreman, who has flown on two Space Shuttle missions and is now chief of external programs for GRC.