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Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes

機(jī)譯：用于快速探索隨機(jī)樹(shù)的空間現(xiàn)場(chǎng)重建的機(jī)器人主動(dòng)信息，以及高斯過(guò)程的在線學(xué)習(xí)

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摘要

Information gathering (IG) algorithms aim to intelligently select a mobile sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for IG that employ Gaussian processes (GPs) as underlying model of the process. However, most algorithms discretize the state space, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they are not suited for online information gathering tasks as they assume prior knowledge about GP parameters. This paper presents a novel approach that tackles the two aforementioned issues. Specifically, our approach includes two intertwined steps: (i) a Rapidly-Exploring Random Tree (RRT) search that allows a robot to identify unvisited locations, and to learn the GP parameters, and (ii) an RRT*-based informative path planning that guides the robot towards those locations by maximizing the information gathered while minimizing path cost. The combination of the two steps allows an online realization of the algorithm, while eliminating the need for discretization. We demonstrate that our proposed algorithm outperforms state-of-the-art both in simulations, and in a lab experiment in which a ground-based robot explores the magnetic field intensity within an indoor environment populated with obstacles.
機(jī)譯：信息收集（IG）算法旨在智能地選擇所需的移動(dòng)傳感器動(dòng)作，以有效地獲得對(duì)物理過(guò)程的精確重建，例如占用圖或磁場(chǎng)。最近的許多工程已經(jīng)提出了IG的算法，該算法采用高斯過(guò)程（GPS）作為該過(guò)程的基礎(chǔ)模型。然而，大多數(shù)算法離散化狀態(tài)空間，這使得它們可以在具有復(fù)雜動(dòng)態(tài)的機(jī)器人系統(tǒng)來(lái)計(jì)算地難以解決。此外，它們并不適用于在線信息收集任務(wù)，因?yàn)樗麄兗僭O(shè)關(guān)于GP參數(shù)的先驗(yàn)知識(shí)。本文介紹了一種新穎的方法，可以解決兩個(gè)上述問(wèn)題。具體而言，我們的方法包括兩個(gè)交織的步驟：（i）快速探索的隨機(jī)樹(shù)（RRT）搜索，允許機(jī)器人識(shí)別不受檢測(cè)的位置，并學(xué)習(xí)GP參數(shù)，以及（ii）rRT *基于的信息路徑規(guī)劃通過(guò)最大化在最小化路徑成本的同時(shí)收集的信息來(lái)指導(dǎo)機(jī)器人朝向這些位置。兩步的組合允許在線實(shí)現(xiàn)算法，同時(shí)消除了對(duì)離散化的需求。我們展示了我們所提出的算法在模擬中優(yōu)于最先進(jìn)的，并且在實(shí)驗(yàn)室實(shí)驗(yàn)中，其中基于地面的機(jī)器人探討了填充障礙物的室內(nèi)環(huán)境內(nèi)的磁場(chǎng)強(qiáng)度。

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