  Surveillance and exploration of large environments is a tedious task. In
spaces with limited environmental cues, random-like search is an effective
approach as it allows the robot to perform online coverage of environments
using simple algorithm designs. One way to generate random-like scanning search
is to use nonlinear dynamical systems to impart chaos into the searching
robot's controller. This will result in the generation of unpredictable yet
deterministic trajectories, allowing designers to control the system and
achieve a high scanning coverage of an area. However, the unpredictability
comes at the cost of increased coverage time and a lack of scalability, both of
which have been ignored by the state-of-the-art chaotic path planners. This
work introduces a new, scalable technique that helps a robot to steer away from
the obstacles and cover the entire search space in a short period of time. The
technique involves coupling and manipulating two chaotic systems to reduce the
coverage time and enable scanning of unknown environments with different online
properties. Using this new technique resulted in an average 49% boost in the
robot's performance compared to the state-of-the-art planners. the overall
search performance of the chaotic planner remained comparable to optimal
systems while still ensuring unpredictable paths.
