ν☆: a robot path planning algorithm based on renormalised measure of probabilistic regular languages
ν☆: a robot path planning algorithm based on renormalised measure of probabilistic regular languages
复制标题
ν☆：基于概率正则语言重整化测度的机器人路径规划算法
DOI：
10.1080/00207170802343196
复制
发表时间：
2009
影响因子：
2.1
通讯作者：
A. Ray
中科院分区：
文献类型：
--
作者：
I. Chattopadhyay;Goutham Mallapragada;A. Ray
本文介绍了一种新的路径规划算法，称为ν ☆，减少了机器人路径规划的问题，优化的概率有限状态自动机。该算法利用正则语言的重规范化测度ν来规划指定目标的最优路径。虽然基本的导航模型是概率性的，但ν <$-1算法产生的路径计划可以在确定性设置中执行，并在动态不确定性下自动优化路径长度和鲁棒性之间的权衡。该算法已在实验室环境中的Segway机器人移动平台上进行了实验验证。
This article introduces a novel path planning algorithm, called ν ☆, that reduces the problem of robot path planning to optimisation of a probabilistic finite state automaton. The ν ☆-algorithm makes use of renormalised measure ν of regular languages to plan the optimal path for a specified goal. Although the underlying navigation model is probabilistic, the ν ☆-algorithm yields path plans that can be executed in a deterministic setting with automated optimal trade-off between path length and robustness under dynamic uncertainties. The ν ☆-algorithm has been experimentally validated on Segway Robotic Mobility Platforms in a laboratory environment.