混沌边缘下机器人集群的极简探索策略
Minimalist exploration strategies for robot swarms at the edge of chaos
AI总结：
本文研究资源受限机器人集群的探索问题，提出用随机布尔网络生成混沌或混沌边缘动力学作为 Kilobot 的极简控制器，并通过进化机器人学优化探索行为，显著优于 Lévy 调制的相关随机游走。
摘要：
有效的探索能力是机器人集群的基础，尤其是当使用小型、廉价的机器人（例如微型或纳米机器人）时。如果机器人在传感器和计算方面受到过多限制而无法实现最先进的解决方案，随机游走往往成为唯一可行的选择。然而，确定最佳的随机游走参数化方式可能并不简单。此外，当无法进行精确校准时，机器人在运动能力方面的个体差异——这是一种非常常见的情况——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一群模拟 Kilobot 执行的简单探索任务中的有效性。首先，我们展示了如何将随机布尔网络用作 Kilobot 的控制器，与 Lévy 调制的相关随机游走的最佳参数化相比，实现了显著的性能提升。其次，我们证明了混沌动力学有利于最大化探索有效性。最后，我们展示了如何通过进化机器人学方法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。
英文摘要：
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 Lévy-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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
