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

大量低成本的自主机器人有可能在非常大的领域和时间尺度上集体执行任务，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、致动、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这种资源受限机器人仍然是一个挑战。 该研究项目旨在通过开发一个严格的框架来克服这一挑战，以便在现实环境中对机器人群进行可扩展的控制。 该框架结合了流体动力学，信号重建，控制理论和优化领域的技术。 这项工作提供了一种理论基础的方法，用于自动编程机器人群，以执行对社会广泛有益的各种任务，包括环境监测和探索，灾难恢复，安全操作，甚至生物医学成像和纳米级的靶向癌症治疗。 该项目开发了一种正式的方法，用于分析和控制机器人群的时空动力学，这些机器人群将被部署在复杂的未知环境中。所设计的机器人控制策略包括随机行为，如随机遇到环境特征，并在指定的置信度内产生目标集体行为。的置信度估计计算使用一种新的应用程序的涡方法，最初来自流体动力学模型，最近适应获得连续的限制离散群模型，将成对的相互作用规则的维护组结构。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。 该方法是证明了一个案例研究设计控制政策的微型飞行器的任务是授粉的作物领域。 计算机模型和试验台现场实验用于验证系统性能的置信度估计的理论预测。除了机器人技术，该项目还提供了分析工具，用于更深入地了解可以用类似模型表示的系统的复杂宏观行为，包括非混合良好的化学反应网络和自然群体，如社会昆虫群体。

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.