Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the stored digital elevation information to allow multiresolution access. We give results of simulations designed to compare the performance of our approach with two other approaches for multiresolution access, namely: 1) linear interpolation on Delaunay triangles of the sampled terrain data points and 2) terrain learning using support vector machines (SVMs). The results show that to achieve the same mean square error during access, the memory needed in our approach is significantly lower. Additionally, the offline training time for the ELM network is much less than that for the SVM.

Our goal is to decompose an arbitrary pathwise continuous rectilinear polygon with holes into a fixed number of nonoverlapping pathwise continuous rectilinear open subsets. This goal involves two additional hard constraints – a. The area of each subset must equal an a priori value. b. The union of the closure of the subsets must be equal to the polygon itself. A soft constraint is that any edge that defines the closure of any subset measures ω units or more. We give a decomposition algorithm that satisfies these constraints. Our goal is motivated by a problem involving parallel utilization of multiple unmanned reconnaissance aerial vehicles (URAVs) with heterogeneous endurance characteristics and equipped with similar sensors with heterogeneous capabilities to conduct a photographic scan of regions with urban-like structures. We discuss our motivation in greater details.

An unmanned reconnaissance aerial vehicle mounted sensor of footprint with a small area /spl omega//sub s//sup 2/ is used to cover critical airbase structures for damage assessment. The region of coverage interest is modeled with a closed union of a minimal set of interior disjoint rectangles of width /spl omega//sub i/ = /spl omega//sub s/. We wish to find a minimum-length flight path for complete coverage for the nonholonomic vehicle. We prove that our minimization problem is a traveling salesman problem (TSP) that is symmetric, non-Euclidean and satisfies the triangular inequality. We compare our modeling primitive with two simple primitives commonly used for representing coverage regions. An ant colony optimization heuristic for solving the TSP is presented.