CSR: Small: Incremental Sampling Methods for Online Reactive Motion Planning with Temporal Logic Specifications

CSR：小型：具有时间逻辑规范的在线反应运动规划的增量采样方法

• 批准号: 1016213
• 负责人: Emilio Frazzoli
• 金额: $ 50万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016213&HistoricalAwards=false
• 关键词: CSR; Small; Incremental; Sampling; Methods

The objective of this project is to develop a new class of real-time motion planning and control algorithms, enabling mobile cyber-physical systems such as autonomous cars, aircraft, or mobile robots to behave in a provably safe, reliable and efficient fashion, in dynamically-changing, uncertain environments, shared with humans and other independent agents. Safe interaction of such systems with humans, human-controlled systems, or other automated systems will require the ability to behave according to accepted protocols and rules stated in high-level "human-like" languages, such as those arising from the rules of the road, ethical and privacy concerns, as well as rules of engagement in military or security applications. The core of the project is a new approach to the synthesis of real-time planning and control algorithms for mobile cyber-physical systems, combining incremental sampling algorithms for trajectory generation with efficient local model checking techniques. The proposed approach is intended to provide, in addition to provably correct and safe closed-loop behaviors, both asymptotic optimality guarantees and reactive planning capabilities. As a concrete target application area, autonomous automobiles are considered. In particular, a concept is proposed for autonomy-enabled mobility-on-demand system, which could help reducing traffic congestion in metropolitan areas by providing low-cost, convenient, private, and flexible transportation options to city dwellers.

该项目的目标是开发一类新的实时运动规划和控制算法，使移动的网络物理系统，如自动驾驶汽车，飞机，或移动的机器人，以可证明的安全，可靠和有效的方式，在动态变化，不确定的环境中，与人类和其他独立代理共享。 此类系统与人类、人类控制系统或其他自动化系统的安全交互将需要能够根据以高级“类人”语言声明的公认协议和规则行事，例如来自交通规则、道德和隐私问题，以及军事或安全应用的参与规则。 该项目的核心是一种新的方法来综合实时规划和控制算法的移动的网络物理系统，结合增量采样算法的轨迹生成与有效的本地模型检查技术。所提出的方法是为了提供，除了可证明的正确和安全的闭环行为，渐近最优性保证和反应规划能力。 作为具体的目标应用领域，考虑了自动驾驶汽车。特别是，提出了一个概念，使移动按需系统，它可以帮助减少大都市地区的交通拥堵，提供低成本，方便，私人，灵活的交通选择，城市居民。