最小机器人群对未知运动目标的有保证封装
Guaranteed Encapsulation of Targets with Unknown Motion by a Minimalist Robotic Swarm
摘要：
我们提出了一个分散控制算法的机器人群体给定的任务封装静态和移动目标在一个有界的未知环境。我们认为最低限度的机器人没有记忆，明确的沟通，或本地化信息。国家的最先进的方法通常假设在群中的机器人能够检测到相邻的机器人和目标的相对位置，以提供收敛保证。在这项工作中，我们提出了一种新的控制律，保证封装的静态和移动目标，同时避免所有的碰撞，当机器人不知道任何机器人或目标在环境中的确切相对位置。我们利用李雅普诺夫稳定性理论证明我们的控制算法的收敛性，并提供目标和机器人的速度之间的比率的界限。此外，我们提出的方法是能够提供随机保证的范围内，我们确定的任务参数的情况下，目标移动速度比机器人。最后，我们提出了一个分析的紧急行为的变化与不同的参数的任务和嘈杂的传感器读数。
英文摘要：
We present a decentralized control algorithm for a robotic swarm given the task of encapsulating static and moving targets in a bounded unknown environment. We consider minimalist robots without memory, explicit communication, or localization information. The state-of-the-art approaches generally assume that the robots in the swarm are able to detect the relative position of neighboring robots and targets in order to provide convergence guarantees. In this work, we propose a novel control law for the guaranteed encapsulation of static and moving targets while avoiding all collisions, when the robots do not know the exact relative location of any robot or target in the environment. We make use of the Lyapunov stability theory to prove the convergence of our control algorithm and provide bounds on the ratio between the target and robot speeds. Furthermore, our proposed approach is able to provide stochastic guarantees under the bounds that we determine on task parameters for scenarios where a target moves faster than a robot. Finally, we present an analysis of how the emergent behavior changes with different parameters of the task and noisy sensor readings.
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