  This paper presents a literature survey and a comparative study of Bug
Algorithms, with the goal of investigating their potential for robotic
navigation. At first sight, these methods seem to provide an efficient
navigation paradigm, ideal for implementations on tiny robots with limited
resources. Closer inspection, however, shows that many of these Bug Algorithms
assume perfect global position estimate of the robot which in GPS-denied
environments implies considerable expenses of computation and memory -- relying
on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry
(VO) methods. We compare a selection of Bug Algorithms in a simulated robot and
environment where they endure different types noise and failure-cases of their
on-board sensors. From the simulation results, we conclude that the implemented
Bug Algorithms' performances are sensitive to many types of sensor-noise, which
was most noticeable for odometry-drift. This raises the question if Bug
Algorithms are suitable for real-world, on-board, robotic navigation as is.
Variations that use multiple sensors to keep track of their progress towards
the goal, were more adept in completing their task in the presence of
sensor-failures. This shows that Bug Algorithms must spread their risk, by
relying on the readings of multiple sensors, to be suitable for real-world
deployment.
