The paper introduces the method for indoor localization of a mobile robot based on infrared beacons positioned stationary in the environment. The beacons provide bearing only information, fused together with either motion controllers commands or robot odometry via Extended Kalman filter to estimate the location of the robot. Simulation results include comparison with UKF and PF based localization and comparison with distance, distance/bearing beacons measurement. The verification on the test real robot with differential chassis and commercial robot with Ackerman chassis is provided. The main advantage of the method is the low cost of whole localization system. Ill. 8, bibl. 17, tabl. 2 (in English; abstracts in English and Lithuanian).
Inverse problems deal with determining the causes on the basis of knowing their effects. The object of the inverse parameter estimation problem is to fix the thermal material parameters (the cause) on the strength of a given observation of the temperature history at one or more interior points (the effect). This paper demonstrates two novel approaches to the inverse problems. These approaches use two artificial intelligence mechanisms: neural network and genetic algorithm. Examples shown in this paper give a comparison of results obtained by both of these methods. The numerical technique of neural networks evolved from the effort to model the function of the human brain and the genetic algorithms model the evolutional process of nature. Both of the presented approaches can lead to a solution without having problems with the stability of the inverse task. Both methods are suitable for parallel processing and are advantageous for a multiprocessor computer architecture.
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