A Local Optimization Framework for Multi-Objective Ergodic Search

Ren, Zhongqiang; Srinivasan, Akshaya Kesarimangalam; Coffin, Howard; Abraham, Ian; Choset, Howie

doi:10.15607/rss.2022.xviii.052

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…In the former cases, the objective function is purely "information-driven" (Ferrari and Wettergren 2021;Bai et al, 2021), while in the latter scenarios, the objective function balances exploration and exploitation Ramos 2012, 2014;Bai et al, 2016). The objective function can be further extended to multi-objective cases (Chen and Liu 2019;Ren et al, 2022;Dang 2020). Many objective functions have been proposed, inspired by Information Theory and Optimal Experimental Design (Charrow et al, 2015a;Carrillo et al, 2015).…”

Section: Robotic Information Gatheringmentioning

confidence: 99%

“…In the former cases, the objective function is purely “ information-driven ” (Ferrari and Wettergren 2021; Bai et al, 2021), while in the latter scenarios, the objective function balances exploration and exploitation (Marchant and Ramos 2012, 2014; Bai et al, 2016). The objective function can be further extended to multi-objective cases (Chen and Liu 2019; Ren et al, 2022; Dang 2020).…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Robotic Information Gathering via non-stationary Gaussian processes

Chen

Khardon

Liu

2023

The International Journal of Robotics Research

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Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many applications, including but not limited to autonomous exploration and mapping, 3D reconstruction or inspection, search and rescue, and environmental monitoring. A RIG system relies on a probabilistic model’s prediction uncertainty to identify critical areas for informative data collection. Gaussian processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data is typically non-stationary—different locations do not have the same degree of variability. As a result, the prediction uncertainty does not accurately reveal prediction error, limiting the success of RIG algorithms. We propose a family of non-stationary kernels named Attentive Kernel (AK), which is simple and robust and can extend any existing kernel to a non-stationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used stationary kernels and the leading non-stationary kernels. The improved uncertainty quantification guides the downstream informative planner to collect more valuable data around the high-error area, further increasing prediction accuracy. A field experiment demonstrates that the proposed method can guide an Autonomous Surface Vehicle (ASV) to prioritize data collection in locations with significant spatial variations, enabling the model to characterize salient environmental features.

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