  This work presents a low-cost robot, controlled by a Raspberry Pi, whose
navigation system is based on vision. The strategy used consisted of
identifying obstacles via optical flow pattern recognition. Its estimation was
done using the Lucas-Kanade algorithm, which can be executed by the Raspberry
Pi without harming its performance. Finally, an SVM-based classifier was used
to identify patterns of this signal associated with obstacles movement. The
developed system was evaluated considering its execution over an optical flow
pattern dataset extracted from a real navigation environment. In the end, it
was verified that the acquisition cost of the system was inferior to that
presented by most of the cited works, while its performance was similar to
theirs.
