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 an online coverage of the environment without the need for the map. 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. The deterministic nature of chaotic path planners allows the designer to control the system and achieve a high scanning coverage while dodging obstacles and intruders. This study aims to establish scalable and adaptable approaches that enable a robot team to efficiently and cooperatively explore any uncertain environment using dynamical system-based motion planning.  Realizing fully autonomous and fast coverage of unknown areas requires addressing complexities arising from the chaotic nonlinear nature of dynamical systems and the associated learning, task assignment, and decision making strategies. If successful, it will significantly impact the way autonomous swarms of robots are employed in exploration applications.