Aiming at the localization and composition of mobile robots in unknown environments, this paper briefly summarizes the connotation of SLAM, analyzes the principle of extended Kalman filter theory, which is an algorithm process of gradually approaching the real state of the system through recursive estimation of the state of nonlinear random dynamic system, and finally verifies the relevant theories and algorithms through two-dimensional EKF vSLAM simulation experiments. The experimental results show that the EKF-vSLAM algorithm can effectively improve the accuracy of robot positioning and landmark positioning in two-dimensional environments, reduce the uncertainty of mobile robot positioning and composition in unknown environments, and to some extent improve the autonomy of mobile robots.
Positioning of mobile robots is the most basic link of robot navigation, and also one of the key technologies for robots to achieve various complex tasks. This paper mainly studies the basic Monte Carlo positioning algorithm and adaptive Monte Carlo positioning algorithm, and analyzes the convergence process of positioning algorithm through simulation experiments. It is proved that the adaptive Monte Carlo localization algorithm has the ability to find the real pose of the robot in the map.
Biological sequence alignment is one of the most basic research topics in bioinformatics. The double sequence alignment algorithm based on dynamic programming mainly uses iterative algorithm and vacancy penalty rules to compare gene sequences one by one, calculate their similarity scores, and finally obtain the best alignment between sequences through backtracking analysis. This paper studies the analysis and implementation of the global double sequence alignment optimization algorithm based on dynamic programming.
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