论文标题
机器人团队环境监控的随机非线性合奏建模和控制
Stochastic Nonlinear Ensemble Modeling and Control for Robot Team Environmental Monitoring
论文作者
论文摘要
我们寻求建模，控制和分析机器人团队执行环境监视任务的方法。在环境监测期间，目标是让机器人团队在固定区域长时间收集各种数据。标准的自下而上任务分配方法不会随着机器人的数量和任务位置的增加而扩展，并且需要计算昂贵的重新启动。或者，自上而下的方法已用于打击计算复杂性，但大多数方法仅限于分析侧重于任务之间的过渡时间的方法。在这项工作中，我们研究了一类非线性宏观模型，用于控制在整个环境中执行不同任务的机器人的时变分布。我们提出的整体模型和控制通过利用执行任务的机器人之间的自然相互作用来维持所需的时间变化机器人。我们在多个保真度水平上验证方法，包括实验结果，这表明我们进行环境监测的方法有效。
We seek methods to model, control, and analyze robot teams performing environmental monitoring tasks. During environmental monitoring, the goal is to have teams of robots collect various data throughout a fixed region for extended periods of time. Standard bottom-up task assignment methods do not scale as the number of robots and task locations increases and require computationally expensive replanning. Alternatively, top-down methods have been used to combat computational complexity, but most have been limited to the analysis of methods which focus on transition times between tasks. In this work, we study a class of nonlinear macroscopic models which we use to control a time-varying distribution of robots performing different tasks throughout an environment. Our proposed ensemble model and control maintains desired time-varying populations of robots by leveraging naturally occurring interactions between robots performing tasks. We validate our approach at multiple fidelity levels including experimental results, suggesting the effectiveness of our approach to perform environmental monitoring.
