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This will allow the development of optimal navigation strategies.
Participants are required to search, identify, compare information, communicate results and solve problems, defining in advanced the optimal navigation path and the correct final information.
As opposed to exhaustively scanning the parameter space, the system uses reinforcement learning to discover optimal navigation paths guiding the search to the optimal location.
The purpose of this paper is to deal with the optimal navigation co-scheduling (NCS) for the two dams of the Three Gorges Project in China, i.e., the Three Gorges Dam and the Gezhouba Dam.
However, the problem of achieving the optimal navigation sharing by designing the proper network topology is still unsolved, especially in the case that some members of the cluster have absolute navigation devices, some just have relative navigation devices and some only have communication devices.
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Thirdly, for the consideration of fuel consumption, the optimal biased proportional navigation guidance (OBPNG) law is derived by solving the Schwartz inequality.
Under this architecture, three core services, namely the real-time assembly operating guidance service (OGS), collaborative production service (CPS) among assembly stations and real-time queuing service (RQS) of the jobs at each station, are designed to provide optimal and dynamical navigation for assembly activities for each station.
By using a cooperative evolutionary algorithm for configuration parameter optimization, some optimal constellations are finally obtained for the simplified navigation architecture.
In this paper, computer vision-based navigation and optimal fuzzy control strategies for the robotic airship are proposed.
The button to press at each Decision Point was based on the optimal answer in the navigation version of that route.
Harmonic functions provide optimal potential maps for robot navigation in a previously explored static environment.
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