Learning integrated perception-based speed control

Advances in the area of autonomous mobile robotics have allowed robots to explore vast and often unknown terrains. This paper presents a particular form of autonomy that allows a robot to autonomously control its speed, based on perception, while traveling on unknown terrain. The robot is equipped with an onboard camera and a 3-axis accelerometer. The method begins by classifying a query image of the terrain immediately before the robot. Classification is based on the Gabor wavelet features. In learning the speed, a genetic algorithm is used to map the Gabor texture features to approximate speed that minimizes changes in accelerations along the three axes from their nominal values. Learning is performed continuously. Experiments are done in real time.