Abstraction-Based Planning for Uncertainty-Aware Legged Navigation
Abstraction-Based Planning for Uncertainty-Aware Legged Navigation
复制标题
DOI：
10.1109/ojcsys.2023.3296000
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Jesse Jiang;S. Coogan;Ye Zhao
中科院分区：
文献类型：
--
作者：
Jesse Jiang;S. Coogan;Ye Zhao
本文研究了两足机器人在不确定环境下基于时态逻辑的规划问题。首先提出了一种双足运动的区间马尔可夫决策过程抽象(IMDP-BL)。使用堆叠高斯过程学习将来自多个不确定源的运动扰动合并到我们的模型中，以实现对系统行为的形式保证。我们考虑可以使用线性时序逻辑(LTL)指定的任务。通过将双足机器人的IMDP-BL和规范的确定性拉宾自动机(DRA)相结合的乘积IMDP构造，我们合成了允许机器人安全地穿越环境的控制策略，迭代地学习未知的动力学，直到以令人满意的概率满足规范。我们通过模拟案例研究来验证我们的方法。
This article addresses the problem of temporal-logic-based planning for bipedal robots in uncertain environments. We first propose an Interval Markov Decision Process abstraction of bipedal locomotion (IMDP-BL). Motion perturbations from multiple sources of uncertainty are incorporated into our model using stacked Gaussian process learning in order to achieve formal guarantees on the behavior of the system. We consider tasks which can be specified using Linear Temporal Logic (LTL). Through a product IMDP construction combining the IMDP-BL of the bipedal robot and a Deterministic Rabin Automaton (DRA) of the specifications, we synthesize control policies which allow the robot to safely traverse the environment, iteratively learning the unknown dynamics until the specifications can be satisfied with satisfactory probability. We demonstrate our methods with simulation case studies.