This is called service robotic, and it is supposed that the robot acts total or partially with autonomy. Nowadays, the artificial intelligent field has developed service task in robotic systems with the aim of providing help or comfort to humans. ![]() The resulted occupancy grid is analysed and discussed at the end of this document. Experiments on detecting, mapping and avoiding obstacles are performed using the mobile robotic platform DaNI 2.0 and the VxWorks system. A Gaussian function is used for modelling the ultrasonic sensor with the aim of reaching higher precision of the distance measured for each obstacle in the scene. As soon as the robot starts its trajectory in real indoor environments with obstacles, the sensor continuously detects and updates the occupancy map by the logsig strategy. A first path for robot navigation based on the initial local map is estimated using the potential field strategy. The encoders allow computing the robot location in the initial local map. ![]() This map provides the position of the nearest obstacles in the scene useful for achieving the reactive navigation. A local map is initially constructed using the ultrasonic sensor mounted in the front of the robot. This chapter presents a strategy for constructing an occupancy map by proposing a probabilistic model of an ultrasonic sensor, during robot indoor navigation. Robot mobile navigation is a hard task that requires, essentially, avoiding static and dynamic objects.
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