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Additional info for Autotestcon 2001: IEEE Systems Readiness Technology Conference: Revolutionary Ideas in Test
The positions of robot starting and target points were generated randomly from 0 to 1 on the normalized x-y plane. In the test phase, using obtained training data, the relation between current goal positions and desired robot movement is learned. All the process related to experiments is computer simulation programed by the MATLAB. In the following sections, RNN simulation results and some analysis are presented. 1 The Recurrent Neural Network The RNN is an artiﬁcial intelligence computational model.
The A-step fails to ﬁne suitable personal meaning (as agent A1 has no destroying action). Thus communication action will be selected. So A1 sends message to the agent A2 with coordinates of obs1 and description of the action (“destroy 1”), needed to be performed in order for the agent A1 to achieve the goal (“agent 1”,“place X1 ”). I. Panov and K. Yakovlev “empty”. As A2 has personal meaning “I destroy 1” A-step of the PMA-procedure for A2 will be accomplished with success and the obstacle will be destroyed.
Localization approaches using the prior map data [9–12] have also received much attention in recent years. For this approach, the map data that is made in advance, for example Google maps, 3D point cloud maps, and image maps, are required in order to determine the robot pose. Matching between the prior map data and current sensor information enables the robot to estimate the ﬁnal pose. Although this approach relatively guarantees accurate robot pose estimation than other robot approaches, this approach should have the prior map data and may result in the localization failure due to the inaccurate map information.
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