Over the last decade the world has seen numerous autonomous vehicle programs. Wheels and track designs are the basis for many of these vehicles. This is primarily due to four main reasons: a vast preexisting knowledge base for these designs, energy efficiency of power sources, scalability of actuators, and the lack of control systems technologies for handling alternate highly complex distributed systems. Though large efforts seek to improve the mobility of these vehicles, many limitations still exist for these systems within unstructured environments, e.g. limited mobility within industrial and nuclear accident sites where existing plant configurations have been extensively changed. These unstructured operational environments include missions for exploration, reconnaissance, and emergency recovery of objects within reconfigured or collapsed structures, e.g. bombed buildings. More importantly, these environments present a clear and present danger for direct human interactions during the initial phases of recovery operations. Clearly, the current classes of autonomous vehicles are incapable of performing in these environments. Thus the next generation of designs must include highly reconfigurable and flexible autonomous robotic platforms. This new breed of autonomous vehicles will be both highly flexible and environmentally adaptable. Presented in this paper is one of the most successful designs from nature, the snake-eel-worm (SEW). This design implements shape memory alloy (SMA) actuators which allow for scaling of the robotic SEW designs from sub-micron scale to heavy industrial implementations without major conceptual redesigns as required in traditional hydraulic, pneumatic, or motor driven systems. Autonomous vehicles based on the SEW design posses the ability to easily move between air based environments and fluid based environments with limited or no reconfiguration. Under a SEW designed vehicle, one not only achieves vastly improved maneuverability within a highly unstructured environment, but also gains robotic manipulation abilities, normally relegated as secondary add-ons within existing vehicles, all within one small condensed package. The prototype design presented includes a Beowulf style computing system for advanced guidance calculations and visualization computations. All of the design and implementation pertaining to the SEW robot discussed in this paper is the product of a student team under the summer fellowship program at the DOEs INEEL.
A large number of terrain images were taken at Aberdeen Proving Grounds, some containing ground vehicles. Is it possible to screen the images for possible targets in a short amount of time using the fractal dimension to detect texture variations. The fractal dimension is determined using the wavelet transform for these visual images. The vehicles are positioned within the grass and in different locations. Since it has been established that natural terrain exhibits a statistical l/f self-similarity property and the psychophysical perception of roughness can be quantified by the same self-similarity, fractal dimensions estimates should vary only at texture boundaries and breaks in the tree and grass patterns. Breaks in the patterns are found using contour plots of the dimension estimates and are considered as perceptual texture variations. Variation in the dimension estimate is considered more important than the accuracy of the actual dimensions number. Accurate variation estimates are found even with low resolution images.