  Effective exploration abilities are fundamental for robot swarms, especially
when small, inexpensive robots are employed (e.g., micro- or nano-robots).
Random walks are often the only viable choice if robots are too constrained
regarding sensors and computation to implement state-of-the-art solutions.
However, identifying the best random walk parameterisation may not be trivial.
Additionally, variability among robots in terms of motion abilities-a very
common condition when precise calibration is not possible-introduces the need
for flexible solutions. This study explores how random walks that present
chaotic or edge-of-chaos dynamics can be generated. We also evaluate their
effectiveness for a simple exploration task performed by a swarm of simulated
Kilobots. First, we show how Random Boolean Networks can be used as controllers
for the Kilobots, achieving a significant performance improvement compared to
the best parameterisation of a L\'evy-modulated Correlated Random Walk. Second,
we demonstrate how chaotic dynamics are beneficial to maximise exploration
effectiveness. Finally, we demonstrate how the exploration behavior produced by
Boolean Networks can be optimized through an Evolutionary Robotics approach
while maintaining the chaotic dynamics of the networks.
