Credit: Facultad de Informática de la Universidad Politécnica de Madrid.
the European Centre for Soft Computing and the UPM’s Facultad de Informática
have developed an antonym-based technique for building maps for mobile robots.
This technique can be applied to improve current robot navigation systems.
Another advantage of the technique is that the low-cost ultrasonic sensors that
it uses are built into almost all robotic platforms and produce a smaller
volume of data for processing.
mobile robot is a robot that is able to navigate its environment without
colliding or getting lost. Unmanned robots are also able to recover from
spatial disorientation. Conducted by Sergio Guadarrama, researcher of the
European Centre for Soft Computing, and Antonio Ruiz, assistant professor at
the Universidad Politécnica de Madrid’s Facultad de Informática, published in Information Sciences the research
focuses on map building. Map building is one of the skills related to
autonomous navigation, where a robot is required to explore an unknown
environment (enclosure, plant, buildings, etc.) and draw up a map of the
environment. Before it can do this, the robot has to use its sensors to
The main sensor
types used for autonomous navigation are vision and range sensors. Although
vision sensors can capture much more information from the environment, this
research used range, specifically ultrasonic, sensors, which are less accurate,
to demonstrate that the model builds accurate maps from few and imprecise input
Once it has
captured the ranges, the robot has to map these distances to obstacles on the
map. Point clouds are used to draw the map, as the imprecision of the range
data rules out the use of straight lines or even isolated points. Even so, the
resulting map is by no means an architectural blueprint of the site, because
not even the robot’s location is precisely known, and there is no guarantee
that each point cloud is correctly positioned. In actual fact, one and the same
obstacle can be viewed properly from one robot position, but not from another.
This can produce contradictory information -obstacle and no obstacle- about the
same area of the map under construction. Which of the two interpretations is
The solution is
based on linguistic descriptions of the antonyms “vacant” and
“occupied” and inspired by computing with words and the computational
theory of perceptions, two theories proposed by L.A. Zadeh of the Univ. of California
Whereas other published research views obstacles and empty spaces as
complementary concepts, this research assumes that, rather than being
complements, obstacles, and vacant spaces are a pair of opposites.
For example, we
can infer that an occupied space is not vacant, but we cannot infer that an
unoccupied space is empty. This space could be unknown or ambiguous, because
the robot has limited information about its environment. Also the
contradictions between “vacant” and “occupied” are also
This way, the
robot is able to make a distinction between two types of unknown spaces: spaces
that are unknown because information is contradictory and spaces that are
unknown because they are unexplored. This would lead the robot to navigate with
caution through the contradictory spaces and explore the unexplored spaces. The
map is constructed using linguistic rules, such as “If the measured
distance is short, then assign a high confidence level to the measurement”
or “If an obstacle has been seen several times, then increase the
confidence in its presence”, where “short”, “high”,
and “several” are fuzzy sets, subject to fuzzy sets theory.
Contradictions are resolved by a greater reliance on shorter ranges and
combining multiple measures.
Compared with the
results of other methods, the outcomes show that the maps built using this
technique better capture the shape of walls and open spaces, and contain fewer
errors from incorrect sensor data. This opens opportunities for improving the
current autonomous navigation systems for robots.