177 resultados para Significance driven computation
Resumo:
An important concept proposed in the early stage of robot path planning field is the shrinking of the robot to a point and meanwhile expanding of the obstacles in the workspace as a set of new obstacles. The resulting grown obstacles are called the Configuration Space (Cspace) obstacles. The find-path problem is then transformed into that of finding a collision free path for a point robot among the Cspace obstacles. However, the research experiences obtained so far have shown that the calculation of the Cspace obstacles is very hard work when the following situations occur: 1. both the robot and obstacles are not polygons and 2. the robot is allowed to rotate. This situation is even worse when the robot and obstacles are three dimensional (3D) objects with various shapes. Obviously a direct path planning approach without the calculation of the Cspace obstacles is strongly needed. This paper presents such a new real-time robot path planning approach which, to the best of our knowledge, is the first one in the robotic community. The fundamental ideas are the utilization of inequality and optimization technique. Simulation results have been presented to show its merits.
a constraint-driven human resource scheduling method in software development and maintenance process
Resumo:
The nonlinear optical absorption in a three-subband step asymmetric semiconductor quantum well driven by a strong terahertz (THz) field is investigated theoretically by employing the intersubband semiconductor-Bloch equations. We show that the optical absorption spectrum strongly depends on the intensity, frequency, and phase of the pump THz wave. The strong THz field induces THz sidebands and Autler-Townes splitting in the probe absorption spectrum. Varying the pump frequency can bring not only the new absorption peaks but also the changing of the energy separation of the two higher-energy levels. The dependence of the absorption spectrum on the phase of the pump THz wave is also very remarkable.