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.

翻译：我们寻求模型、控制和分析机器人团队执行环境监测任务的方法。在环境监测过程中,目标是让机器人团队在固定区域收集各种数据,时间较长。标准自下而上的任务分配方法不会随着机器人和任务地点数量的增加而扩大规模,而需要计算昂贵的重新规划。或者,自上而下的方法被用于消除计算复杂性,但大多数方法仅限于分析侧重于任务之间过渡时间的方法。在这项工作中,我们研究了一类非线性宏观模型,我们使用这些模型来控制在整个环境中执行不同任务的机器人的时间变化分布。我们提议的共性模型和控制通过利用执行任务的机器人之间自然发生的相互作用,保持了理想的机器人时间变化人口。我们验证了我们多轨性的方法,包括实验结果,表明我们进行环境监测的方法的有效性。