  Two the most common tasks for autonomous mobile robots is to explore the
environment and locate a target. %In the last case, the objective is either to
find a target in the shortest time possible or, alternatively, to find %as many
targets as possible for a given amount of time. Targets could range from
sources of chemical contamination to people needing assistance in a disaster
area. From the very beginning, the quest for most efficient search algorithms
was strongly influenced by behavioral science and ecology, where researchers
try to figure out the strategies used by leaving beings, from bacteria to
mammals. Since then, bio-inspired random search algorithms remain one the most
important directions in autonomous robotics. Recently a new wave arrived
bringing a specific type of random walks as a universal search strategy
exploited by immune cells, insects, mussels, albatrosses, sharks, deers, and a
dozen of other animals including humans. These \textit{L\'{e}vy} walks combine
two key features, the ability of walkers to spread anomalously fast while
moving with a finite velocity. The latter is especially valuable in the context
of robotics because it respects the reality autonomous robots live in. There is
already an impressive body of publications on L\'{e}vy robotics; yet research
in this field is unfolding further, constantly bringing new results, ideas,
hypothesis and speculations. In this mini-review we survey the current state of
the field, list latest advances, discuss the prevailing trends, and outline
further perspectives.
