论文标题
谨慎的象征感知的谨慎计划：设计经过验证的反应驾驶演习
Cautious Planning with Incremental Symbolic Perception: Designing Verified Reactive Driving Maneuvers
论文作者
论文摘要
这项工作提出了利用对机器人周围环境的逐步改善的象征感知知识的一步，以证明适用于自动驾驶问题的正确反应性控制综合。结合了运动控制和信息收集的抽象模型，我们表明假设保证规格（线性时间逻辑的子类）可用于定义和解决谨慎计划的流量规则。我们提出了一种称为符号改进树的新颖表示，以捕获有关环境的增量知识，并体现了各种符号感知输入之间的关系。利用增量知识来合成机器人的验证反应性计划。案例研究表明，即使在部分遮挡的环境中，拟议方法在合成控制输入方面的疗效。
This work presents a step towards utilizing incrementally-improving symbolic perception knowledge of the robot's surroundings for provably correct reactive control synthesis applied to an autonomous driving problem. Combining abstract models of motion control and information gathering, we show that assume-guarantee specifications (a subclass of Linear Temporal Logic) can be used to define and resolve traffic rules for cautious planning. We propose a novel representation called symbolic refinement tree for perception that captures the incremental knowledge about the environment and embodies the relationships between various symbolic perception inputs. The incremental knowledge is leveraged for synthesizing verified reactive plans for the robot. The case studies demonstrate the efficacy of the proposed approach in synthesizing control inputs even in case of partially occluded environments.
