A trajectory learner for sonar based LEGO NXT differential drive robot

This paper presents a trajectory learning algorithm for sonar based LEGO Mindstorm NXT differential drive robot. The trajectory learning technique utilizes vehicle odometry and sensor scanning data. The aim of this technique is to find the obstacle free path for mobile robots. Consequently, a free configuration space from the higher dimension sensor data is extracted by employing highest eigen-vectors technique [2] in discrete time scans during robotic manipulation. Integration of these eigenvectors in discrete time results in a new trajectory. This new trajectory is free from dynamic and static obstacles. The trajectory generation algorithm was tested on a nonholonomic LEGO NXT differential drive robot equipped with sonar and position sensors. The trajectory learning proposed method has been tested in different scenarios which resulted into promising preliminary results and are shown in this paper.