A Scalable Control Framework for Boundary Coverage and Cooperative Manipulation by Robotic Swarms

成群的低成本自主机器人有可能用于收集大量传感器数据和长距离运输重型有效载荷。由于最近在计算、传感、驱动、动力、控制和3D打印方面的进展，目前正在开发用于群体应用的新型机器人。然而，仍然需要有理论基础的方法来编程机器人群体，以实现现实条件下的目标感知和运输目标。在许多应用中，每个机器人的能力都非常有限，通信不可靠，没有位置信息，也没有周围环境的地图。本研究项目开发了一个框架，用于可靠地控制具有此类约束的任意大量机器人。该项目特别关注调节机器人与环境中可能存在的不同类型特征的交互问题。这些特征可以是感兴趣的区域，需要机器人沿着其周边的特定分布，也可以是需要机器人团队将其移动到指定目的地的对象。开发的框架应用了化学动力学、群居昆虫行为、动力系统、反馈控制和优化等领域的技术。它提供了一种正式的方式来为机器人群编程，以完成一系列对社会有广泛好处的任务，包括环境监测、搜索和救援行动、灾难恢复、自动化建筑和制造，甚至是纳米尺度的生物医学成像和药物输送。该项目开发了一种严格的方法，用于机器人群体动态的反馈控制和优化，仅使用局部传感和公共广播信息在未知环境中产生目标覆盖和操作行为。该方法在描述机器人的不同抽象层次上结合了随机、确定性和混合随机-确定性模型。角色，任务转换，运动和操纵动力学。它包括一种新的基于相遇的边界覆盖方法，不需要表征相遇率或环境参数的知识。基于刚载荷和力传感器实验数据的沙漠蚁群检索模型，开发了新的协作操作随机策略。控制框架通过计算机模拟和小型移动机器人的试验台实验进行了验证，每个移动机器人都配备了作为该项目一部分设计的多自由度夹持器。除了机器人技术，该项目还为深入了解生物表面覆盖过程提供了分析工具，例如蛋白质吸附，以及社会性昆虫集体运输策略背后的行为和生物力学机制。

Swarms of low-cost autonomous robots can potentially be used to collect massive amounts of sensor data and transport heavy payloads across long distances. Novel robots for swarm applications are currently being developed as a result of recent advances in computing, sensing, actuation, power, control, and 3D printing. However, there remains a need for theoretically-grounded methods of programming robotic swarms to achieve target sensing and transport objectives under realistic conditions. In many applications, each robot will have highly limited capabilities, undependable communication, no information on its location, and no map of its surroundings. This research project develops a framework for reliably controlling arbitrarily large populations of robots with such constraints. The project focuses specifically on the problem of regulating robot interactions with different types of features that may be present in their environment. These features can be regions of interest that require certain distributions of robots along their perimeters, or they may be objects that require teams of robots to move them to specified destinations. The developed framework applies techniques from the fields of chemical kinetics, social insect behavior, dynamical systems, feedback control, and optimization. It provides a formal way to program robotic swarms for a range of tasks of wide benefit to society, including environmental monitoring, search-and-rescue operations, disaster recovery, automated construction and manufacturing, and even biomedical imaging and drug delivery at the nanoscale.This project develops a rigorous methodology for feedback control and optimization of robotic swarm population dynamics to produce target coverage and manipulation behaviors in unknown environments using only local sensing and common broadcast information. The approach incorporates stochastic, deterministic, and hybrid stochastic-deterministic models at different levels of abstraction that describe the robots? roles, task transitions, motion, and manipulation dynamics. It includes a novel encounter-based approach to boundary coverage that does not require characterization of encounter rates or knowledge of environmental parameters. New stochastic strategies for cooperative manipulation are developed in part from models of desert ant group retrieval that are based on experimental data using rigid loads and ant force sensors. The control framework is validated through computer simulations and testbed experiments with small mobile robots, each equipped with a multi-degree-of-freedom gripper that is designed as part of this project. Beyond robotics, the project provides analytical tools for a deeper understanding of surface coverage processes in biology, such as protein adsorption, and the behavioral and biomechanical mechanisms that underlie collective transport strategies in social insects.