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A temporal logic optimal controlsynthesis algorithm for large-scale multi-robot systems
The International Journal of Robotics Research ( IF 9.2 ) Pub Date : 2020-04-29 , DOI: 10.1177/0278364920913922
Yiannis Kantaros 1 , Michael M Zavlanos 1
Affiliation  

This article proposes a new highly scalable and asymptotically optimal control synthesis algorithm from linear temporal logic specifications, called STyLu S * for large-Scale optimal Temporal Logic Synthesis, that is designed to solve complex temporal planning problems in large-scale multi-robot systems. Existing planning approaches with temporal logic specifications rely on graph search techniques applied to a product automaton constructed among the robots. In our previous work, we have proposed a more tractable sampling-based algorithm that builds incrementally trees that approximate the state space and transitions of the synchronous product automaton and does not require sophisticated graph search techniques. Here, we extend our previous work by introducing bias in the sampling process that is guided by transitions in the Büchi automaton that belong to the shortest path to the accepting states. This allows us to synthesize optimal motion plans from product automata with hundreds of orders of magnitude more states than those that existing optimal control synthesis methods or off-the-shelf model checkers can manipulate. We show that STyLu S * is probabilistically complete and asymptotically optimal and has exponential convergence rate. This is the first time that convergence rate results are provided for sampling-based optimal control synthesis methods. We provide simulation results that show that STyLu S * can synthesize optimal motion plans for very large multi-robot systems, which is impossible using state-of-the-art methods.

中文翻译:

一种大规模多机器人系统的时序逻辑最优控制综合算法

本文从线性时间逻辑规范中提出了一种新的高度可扩展和渐近最优控制合成算法,称为 STyLu S * for large Scale optimization Temporal Logic Synthesis,旨在解决大规模多机器人系统中的复杂时间规划问题。具有时间逻辑规范的现有规划方法依赖于应用于在机器人之间构建的产品自动机的图搜索技术。在我们之前的工作中,我们提出了一种更易于处理的基于采样的算法,该算法构建增量树来近似同步乘积自动机的状态空间和转换,并且不需要复杂的图搜索技术。这里,我们通过在采样过程中引入偏差来扩展我们之前的工作,该偏差由 Büchi 自动机中属于到接受状态的最短路径的转换引导。这使我们能够从产品自动机合成最佳运动计划,其状态比现有的最佳控制合成方法或现成的模型检查器可以操纵的状态多数百个数量级。我们证明 STyLu S * 是概率完备的和渐近最优的,并且具有指数收敛速度。这是首次为基于采样的最优控制综合方法提供收敛速度结果。我们提供的模拟结果表明,STyLu S * 可以为非常大的多机器人系统合成最佳运动计划,这是使用最先进的方法无法实现的。
更新日期:2020-04-29
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