HoverBot: a manufacturable swarm robot that has multi-functional sensing capabilities and uses collisions for two-dimensional mapping
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Date
29/11/2018Author
Nemitz, Markus P.
Metadata
Abstract
Swarm robotics is the study of developing and controlling large
groups of robots. Collectives of robots possess advantages over single robots
such as being robust to mission failures due to single-robot errors. Experimental
research in swarm robotics is currently limited by swarm robotic technology.
Current swarm robotic systems are either small groups of sophisticated
robots or large groups of simple robots due to manufacturing overhead,
functionality-cost dependencies, and their need to avoid collisions, amongst
others. It is therefore useful to develop a swarm robotic system that is easy to
manufacture, that utilises its sensors beyond standard usage, and that allows
for physical interactions. In this work, I introduce a new type of low-friction
locomotion and show its first implementation in the HoverBot system. The
HoverBot system consists of an air-levitation and magnet table, and a HoverBot agent. HoverBots are levitating circuit boards which are equipped with
an array of planar coils and a Hall-effect sensor. HoverBot uses its coils to
pull itself towards magnetic anchors that are embedded into a levitation table.
These robots consist of a Printed Circuit Board (PCB), surface mount components,
and a battery. HoverBots are easily manufacturable, robots can be
ordered populated; the assembly consists of plugging in a battery to a robot. I
demonstrate how HoverBot’s low-cost hardware can be used beyond its standard
functionality. HoverBot’s magnetic field readouts from its Hall-effect sensor
can be associated with successful movement, robot rotation and collision
measurands. I build a time series classifier based on these magnetic field readouts,
I modify and apply signal processing techniques to enable the online
classification of the time-variant magnetic field measurements on HoverBot’s
low-cost microcontroller. This method allows HoverBot to detect rotations,
successful movements, and collisions by utilising readouts from its single Hall-effect
sensor. I discuss how this classification method could be applied to other
sensors and demonstrate how HoverBots can utilise their classifier to create an
occupancy grid map. HoverBots use their multi-functional sensing capabilities
to determine whether they moved successfully or collided with a static object
to map their environment. HoverBots execute an "explore-and-return-to-nest"
strategy to deal with their sensor and locomotion noise. Each robot is assigned
to a nest (landmark); robots leave their nests, move n steps, return and share
their observations. Over time, a group of four HoverBots collectively builds a
probabilistic belief over its environment.
In summary, I build manufacturable swarm robots that detect collisions
through a time series classifier and map their environment by colliding with
their surroundings. My work on swarm robotic technology pushes swarm
robotics research towards studies on collision-dependent behaviours, a research
niche that has been barely studied. Collision events occur more often in
dense areas and/or large groups, circumstances that swarm robots experience.
Large groups of robots with collision-dependent behaviours could become a
research tool to help invent and test novel distributed algorithms, to understand
the dependencies between local to global (emergent) behaviours and
more generally the science of complex systems. Such studies could become
tremendously useful for the execution of large-scale swarm applications such
as the search and rescue of survivors after a natural disaster.
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