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Learning the Goal Seeking Behaviour for Mobile Robots
Abstract
Machine Learning techniques have been widely used for the navigation of mobile robots moving towards a
region of interest while avoiding obstacles. Surprisingly, there is a sparingly little literature on the
use of machine learning techniques for navigating the robot towards a precisely defined goal configuration
amidst static and dynamic obstacles. The need to have the robot reach a precise configuration is needed for
applications like placing the robot for charging, robots carrying mobile manipulation, etc. This paper takes
the problem of planning motion of an autonomous robot to reach a specific goal configuration in presence of static
and dynamic obstacles as a machine learning problem; and compares two approaches, namely supervised learning and
reinforcement learning using neural networks. The comparisons are done on a simulation as well as on the physical
robot Amigobot operating at the robotics laboratory of the host institute. Experimental results show that reinforcement
learning is more suited to solve the problem as the technique does not require a human expert to generate data, which is
hence expensive.