Abstract
Autonomy is an important topic in modern robotics and it is attained by jointly applying motion planning and control algorithms. This paper presents a review of some well known motion planning techniques, which are: A-star A*, Probabilistic Roadmap and Genetic Algorithms, they are applied to a mobile robot operating into a given environment which contains random-shaped obstacles located arbitrarily. The results of any selected planning algorithm are used as setpoints for a feedback nonlinear controller that operates the motion of the robot in the environment. Simulation results show that probabilistic roadmap has the best performance in time computing, that it has path lengths close to A* algorithm results (optimal under given constraints), and also an effective behaviour on the proposed input-output feedback linearisation control methodology for path tracking.
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