  The vast majority of existing Distributed Computing literature about mobile
robotic swarms considers computability issues: characterizing the set of system
hypotheses that enables problem solvability. By contrast, the focus of this
work is to investigate complexity issues: obtaining quantitative results about
a given problem that admits solutions. Our quantitative measurements rely on a
newly developed simulation framework to benchmark pen and paper designs. First,
we consider the maximum traveled distance when gathering robots at a given
location, not known beforehand (both in the two robots and in the n robots
settings) in the classical OBLOT model, for the FSYNC, SSYNC, and ASYNC
schedulers. This particular metric appears relevant as it correlates closely to
what would be real world fuel consumption. Then, we introduce the possibility
of errors in the vision of robots, and assess the behavior of known rendezvous
(aka two robots gathering) and leader election protocols when sensors are
unreliable. We also introduce two new algorithms, one for fuel efficient
convergence, and one for leader election, that operate reliably despite
unreliable sensors.
