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Uncertainty Constrained Robotic Exploration: An Integrated Exploration Planner

机译:不确定性约束的机器人探索:综合探索计划器

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Efficient robotic exploration of unknown sensor limited global-information-deficient environments poses unique challenges to path planning algorithms. In these difficult environments, no deterministic guarantees on path completion and mission success can be made in general. Integrated exploration (IE), which strives to combine localization and exploration, must be solved in order to create an autonomous robotic system capable of long-term operation in new and challenging environments. This paper formulates a probabilistic framework that allows the creation of exploration algorithms providing probabilistic guarantees of success. A novel connection is made between the Hamiltonian path problem and exploration. The guaranteed probabilistic information explorer (G-PIE) is developed for the IE problem, providing a probabilistic guarantee on path completion, and asymptotic optimality of exploration. A receding horizon probabilistic information explorer (RH-PIE) is presented, which addresses the exponential complexity present in G-PIE. Finally, RH-PIE planner is verified via autonomous hardware-in-the-loop experiments.
机译:对未知传感器受限的全球信息不足环境的有效机器人探索对路径规划算法提出了独特的挑战。在这些困难的环境中,通常无法对路径完成和任务成功做出确定性保证。为了创建能够在新的和充满挑战的环境中长期运行的自主机器人系统,必须解决将本地化和探索结合起来的综合探索(IE)。本文提出了一个概率框架,该框架允许创建探索算法,从而为成功提供概率保证。哈密​​顿路径问题与探索之间建立了新颖的联系。保证概率信息浏览器(G-PIE)是针对IE问题而开发的,它为路径完成和探索的渐近最优性提供了概率保证。提出了一种渐进式地平线概率信息浏览器(RH-PIE),它解决了G-PIE中存在的指数复杂性。最后,RH-PIE规划器通过自主的硬件在环实验进行了验证。

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