We consider the problem of learned navigation of a circular robot R , of radius 6 (B 0), through a terrain whose model is not a priori known. W consider two-dimensional finite-sized terrains populated by an unknown (but, finite) number of simple polygonal obstacles. The number and locations of the vertices of each obstacle are unknown to R ; R is equipped with a sensor system that detects all vertices and edges that are visible from its present location. In this context, we deal with to problems: the visit problem and the terrain model acquisition problem. In the visit problem, the robot is required to visit a sequence of destination points, and in the terrain model acquisition problem, the robot is required to acquire the complete model of the terrain. We present an algorithmic framework for solving these two problems based on a retraction of the free space onto the Voronoi diagram of the terrain. We then present algorithms to solve the visit problem and the terrain model acquisition problem.
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In a number of distributed computing applications, messages must be transmitted on demand between processes running at different locations on the Internet. The end-to-end delays experienced by the messages have a significant "random" component due to the complicated nature of network traffic. We propose a method based on delay-regression estimation to achieve low end-to-end delays for message transmissions in distributed computing applications. Two-paths are realized between various communicating processes in a transparent manner. Our scheme is implemented over the Internet by a network of NetLets, which communicate with one another to maintain an accurate "state" of delay-regressions in the network. NetLets handle all network traffic between the processes and also perform routing at a certain level depending on the underlying network. We present experimental results to illustrate that NetLets provide a viable and practical means for achieving low end-to-end delays for distributed computing applications over the Internet.
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