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Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments
International Journal of Advanced Robotic Systems ( IF 2.3 ) Pub Date : 2021-04-23 , DOI: 10.1177/1729881421999587
Larkin Folsom 1 , Masahiro Ono 2 , Kyohei Otsu 2 , Hyoshin Park 1
Affiliation  

Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route.



中文翻译:

不确定环境中流动直升机队的可扩展信息理论路径规划

对不确定环境的关键任务探索需要可靠而强大的机制来获取信息。诸如Shannon熵和KL散度之类的信息获取的典型度量无法区分不同的双峰概率分布,也无法向双峰概率分布的一种模式引入偏差。使用标准差(SD)度量标准可减少偏差,同时保留区分较高和较低风险分布的能力。通过使用自主火星直升机进行观察,可以安全地探索高标清区域,从而为陆上漫游者提供更安全,更快捷的路径规划。首先,本研究提出了一种用于高度相关不确定环境的基于单代理信息理论效用的路径规划方法。然后,提出了一种信息理论的两阶段多主体快速探索随机树框架,该框架指导火星直升机穿越高标清区域,以减少流动站的不确定性。在蒙特卡洛模拟中,我们将信息理论框架与仅流动站的方法和幼稚的方法进行了比较,在这种方法中,直升机沿流动站的计划路径在流动站之前侦察。最后,该模型在火星Jezero地区的案例研究中得到了证明。结果表明,与单独的漫游车或沿漫游车最初计划的路线在前方侦察的直升机相比,信息理论上的直升机平均缩短了漫游车的行驶时间。在蒙特卡洛模拟中,我们将信息理论框架与仅流动站的方法和幼稚的方法进行了比较,在这种方法中,直升机沿流动站的计划路径在流动站之前侦察。最后,该模型在火星Jezero地区的案例研究中得到了证明。结果表明,与单独的漫游车或沿漫游车最初计划的路线在前方侦察的直升机相比,信息理论上的直升机平均缩短了漫游车的行驶时间。在蒙特卡洛模拟中,我们将信息理论框架与仅流动站的方法和幼稚的方法进行了比较,在这种方法中,直升机沿流动站的计划路径在流动站之前侦察。最后,该模型在火星Jezero地区的案例研究中得到了证明。结果表明,与单独的漫游车或沿漫游车最初计划的路线在前方侦察的直升机相比,信息理论上的直升机平均缩短了漫游车的行驶时间。

更新日期:2021-04-23
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