机器人群体的弹性源追踪
Resilient source seeking with robot swarms
AI总结：
本研究针对未知标量场的源定位问题，提出一种基于机器人群体密度分析的弹性源追踪方法，通过群体形状优化保证上升方向与梯度近似平行，可应对个体损失与噪声干扰，经数百机器人数值验证有效。
摘要：
我们提出了一种利用移动机器人群体定位未知标量场的源（即最大值点）的解决方案。与依赖传统梯度信息的方法不同，该群体可确定一个上升方向，以任意精度逼近源点。该上升方向是根据机器人所在位置的场强测量值以及它们相对于质心的相对位置计算得出的。我们并未聚焦于单个机器人，而是将分析重点放在单位面积的机器人密度上，以确保群体具有更强的弹性——即便在任务过程中有个体丢失或位置错位，群体功能仍可保持。我们通过给出群体形状的充分条件来增强算法的鲁棒性，使得上升方向与梯度几乎平行。该群体能够通过改变自身形状并利用多个上升方向的存在，应对意外环境。最后，我们用数百个机器人以数值方式验证了我们的方法。大量机器人始终能计算出上升方向，这一特性弥补了个体损失的影响，并缓解了由执行器和传感器噪声带来的问题。
英文摘要：
We present a solution for locating the source, or maximum, of an unknown scalar field using a swarm of mobile robots. Unlike relying on the traditional gradient information, the swarm determines an ascending direction to approach the source with arbitrary precision. The ascending direction is calculated from measurements of the field strength at the robot locations and their relative positions concerning the centroid. Rather than focusing on individual robots, we focus the analysis on the density of robots per unit area to guarantee a more resilient swarm, i.e., the functionality remains even if individuals go missing or are misplaced during the mission. We reinforce the robustness of the algorithm by providing sufficient conditions for the swarm shape so that the ascending direction is almost parallel to the gradient. The swarm can respond to an unexpected environment by morphing its shape and exploiting the existence of multiple ascending directions. Finally, we validate our approach numerically with hundreds of robots. The fact that a large number of robots always calculate an ascending direction compensates for the loss of individuals and mitigates issues arising from the actuator and sensor noises.
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