Collaborative Research: Modeling, Analysis, and Control of the Spatio-temporal Dynamics of Swarm Robotic Systems

大量的低成本自主机器人群体或群体有可能共同执行非常大的域和时间尺度的任务，即使在存在故障、错误和干扰的情况下也能成功。由于计算、传感、驱动、电力、控制和3D打印技术的不断进步，在实践中创建机器人群变得可行。近年来，这些技术的小型化导致了许多用于群体应用的新型机器人平台，包括微型飞行器。然而，在全球信息和通信有限或不可靠的未知环境中，可靠地控制任意数量的此类资源受限的机器人仍然是一个挑战。该研究项目旨在通过开发在现实环境中可扩展控制机器人群的严格框架来克服这一挑战。该框架结合了流体动力学、信号重构、控制理论和优化等领域的技术。这项工作提供了一种理论上扎根的方法，用于自动对机器人群进行编程，以执行一系列广泛对社会有益的任务，包括环境监测和探索、灾难恢复、安全操作，甚至是纳米级的生物医学成像和靶向癌症治疗。这个项目开发了一种正式的方法来分析和控制将要部署在复杂未知环境中的机器人群体的时空动力学。所设计的机器人控制策略将随机相遇等随机行为与环境特征结合起来，在一定的置信度内产生目标集体行为。置信度估计是使用涡旋方法的一种新的应用来计算的，该方法最初是为流体动力学模型而得到的，最近被用来获得离散群模型的连续极限，该离散群模型包含了用于维持基团结构的成对相互作用规则。该控制方法使用新的压缩感知计算算法，从稀疏的机器人传感器数据重建标量环境场，并设计有效的机器人数据收集策略。该方法以一个为农田授粉的微型飞行器设计控制政策的案例研究为例。计算机模型和试验台现场实验都被用来验证理论预测对系统性能的置信度估计。除了机器人学，该项目还提供了分析工具，以更深入地了解系统的复杂宏观行为，这些系统可以用类似的模型来表示，包括非良好混合的化学反应网络和自然群体，如群居昆虫群体。

Massive populations, or swarms, of low-cost autonomous robots have the potential to collectively perform tasksover very large domains and time scales, succeeding even in the presence of failures, errors, and disturbances. It is becoming feasible to create robotic swarms in practice due to ongoing advances in computing, sensing, actuation, power, control, and 3D printing technologies. In recent years, the miniaturization of these technologies has led to many novel robot platforms for swarm applications, including micro aerial vehicles. However, it remains a challenge to reliably control arbitrary numbers of such resource-constrained robots in unknown environments where global information and communication are limited or undependable. This research project aims to overcome this challenge by developing a rigorous framework for the scalable control of robotic swarms in realistic environments. The framework combines techniques from the fields of fluid dynamics, signal reconstruction, control theory, and optimization. This work provides a theoretically grounded approach for automatically programming robotic swarms to perform a diverse set of tasks of wide benefit to society, including environmental monitoring and exploration, disaster recovery, security operations, and even biomedical imaging and targeted cancer therapies at the nanoscale. This project develops a formal methodology for analyzing and controlling the spatiotemporal dynamics of robotic swarms that are to be deployed in complex unknown environments. The designed robot control policies incorporate stochastic behaviors such as random encounters with environmental features and produce target collective behaviors within a specified degree of confidence. The confidence estimates are computed using a novel application of vortex methods, originally derived for fluid dynamic models and recently adapted to obtain continuum limits of discrete swarm models that incorporate pairwise interaction rules for maintenance of group structure. The control approach uses new computational algorithms for compressive sensing to reconstruct scalar environmental fields from sparse robot sensor data and to design efficient strategies for robot data collection. The methodology is demonstrated with a case study on designing control policies for micro aerial vehicles that are tasked to pollinate a crop field. Both computer models and testbed field experiments are used to validate theoretical predictions for the confidence estimates on system performance. Beyond robotics, the project provides analytical tools for a deeper understanding of the complex macroscopic behaviors of systems that can be represented with similar models, including non-well-mixed chemical reaction networks and natural swarms such as social insect colonies.