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.

翻译：有效的探索能力对于机器人群体至关重要，尤其是在使用小型廉价机器人（如微型或纳米机器人）时。如果机器人在传感器和计算方面过于受限而无法实施最先进的解决方案，随机游走通常是唯一可行的选择。然而，确定最佳的随机游走参数化可能并非易事。此外，机器人之间运动能力的差异性——在无法进行精确校准时非常常见——引入了对灵活解决方案的需求。本研究探讨了如何生成呈现混沌或混沌边缘动力学的随机游走。我们还评估了它们在模拟Kilobot群体执行简单探索任务中的有效性。首先，我们展示了如何利用随机布尔网络作为Kilobot的控制器，相比Lévy调制的相关随机游走的最佳参数化，实现了显著的性能提升。其次，我们论证了混沌动力学如何有利于最大化探索效率。最后，我们证明了通过进化机器人学方法可以优化布尔网络产生的探索行为，同时保持网络的混沌动力学特性。