In the mobile robotic systems, a precise estimate of the robot pose with the intention of the optimization in the path planning is essential for the correct performance, on the part of the robots, for tasks that are destined to it. This paper describes the use of RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bidimensional indoor environment, where GPS system is out of range. This methodology takes advantage of high-performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass, and the result of triangulation Cartesian estimative, are fused for better position estimative. It uses a mathematical and computational tool for nonlinear systems with time-discrete sampling for pose estimative calculation of mobile robots, with the utilization of extended Kalman filter (EKF). A mobile robot platform with differential drive and nonholonomic constraints is used as a base for state space, plants and measurements models that are used in the simulations and validation of the experiments. 1. Introduction The planning of trajectory for the mobile robots, and consequently its better estimative of positioning, is the reason of intense scientific inquiry. A good path planning of trajectory is fundamental for optimization of the interrelation between the environment and the mobile robot. A great diversity of techniques based on different physical principles exists and different algorithms for the localization and the planning of the best possible trajectory. The localization in structuralized environment is helped, in general, by external elements that are called of markers. It is possible to use natural markers that already existing in the environment for the localization. Another possibility is to add intensionally to the environment artificial markers to guide the localization of the robot. This work uses an important mathematical and computational tool for the calculation of the data fusing collected by the sensors and the disturbances caused for the errors, with the purpose of estimate the mobile robot pose, that is the Extended Kalman filter (EKF). A mobile robot platform with differential traction and nonholonomics restrictions is used for experiments validation. In the direct kinematics the system of mobile robot positioning is presented. A model for state space, plants and measurements are presented, that are needed for the development of the necessary attributes to the positioning estimates made with the Extended Kalman filter. Finally, we
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