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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：機(jī)器人主動(dòng)信息收集用于快速探索隨機(jī)樹(shù)和高斯過(guò)程在線學(xué)習(xí)的空間領(lǐng)域重構(gòu)

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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有效獲取物理過(guò)程（例如，占用圖或磁場(chǎng)）的準(zhǔn)確重建所需的移動(dòng)傳感器動(dòng)作。許多最近的工作為IG提出了算法，這些算法采用高斯過(guò)程（GPs）作為過(guò)程的基礎(chǔ)模型。但是，大多數(shù)算法會(huì)將狀態(tài)空間離散化，這使它們對(duì)于具有復(fù)雜動(dòng)力學(xué)的機(jī)器人系統(tǒng)在計(jì)算上難以處理。此外，它們不適合用于在線信息收集任務(wù)，因?yàn)樗鼈兗俣ň哂杏嘘P(guān)GP參數(shù)的先驗(yàn)知識(shí)。本文提出了一種解決上述兩個(gè)問(wèn)題的新穎方法。具體來(lái)說(shuō)，我們的方法包括兩個(gè)相互交織的步驟：（i）快速探索隨機(jī)樹(shù)（RRT）搜索，該搜索使機(jī)器人可以識(shí)別未訪問(wèn)的位置并學(xué)習(xí)GP參數(shù)；（ii）基于RRT *的信息路徑規(guī)劃通過(guò)最大化收集的信息同時(shí)最小化路徑成本，將機(jī)器人引導(dǎo)到這些位置。這兩個(gè)步驟的組合允許在線實(shí)現(xiàn)算法，同時(shí)消除了離散化的需要。我們證明了我們提出的算法在仿真和實(shí)驗(yàn)室實(shí)驗(yàn)中均優(yōu)于最新技術(shù)，在該實(shí)驗(yàn)中，地面機(jī)器人探索了充滿障礙物的室內(nèi)環(huán)境中的磁場(chǎng)強(qiáng)度。

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