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

该项目的目标是开发一种新型的实时运动规划和控制算法，使移动网络物理系统（如自动驾驶汽车、飞机或移动机器人）能够在动态变化、不确定的环境中以可证明的安全、可靠和高效的方式运行，并与人类和其他独立代理共享。这些系统与人类、人类控制的系统或其他自动化系统的安全交互将需要能够按照用高级“类人”语言陈述的公认协议和规则行事，例如来自道路规则、道德和隐私问题的规则，以及军事或安全应用中的交战规则。该项目的核心是一种综合移动网络物理系统实时规划和控制算法的新方法，将轨迹生成的增量采样算法与有效的局部模型检查技术相结合。所提出的方法除了提供可证明的正确和安全的闭环行为外，还旨在提供渐近最优性保证和响应性规划能力。自动驾驶汽车作为一个具体的目标应用领域。特别是，为城市居民提供廉价、方便、私人、灵活的交通选择，有助于缓解首都圈交通拥堵的“自主机动系统”概念。

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.