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