Physical Robot Manipulator Experiments

Zhang, Yunong; Zhang, Zhijun

doi:10.1007/978-3-642-37518-7_11

Cited by 1 publication

(1 citation statement)

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“…Although the localization uncertainty can come from different sources, in perceptually degraded environments such as subterranean, perception uncertainty outweighs the others. A dedicated topic for this case is perception-aware planning (Zhang 2020). Note that localization is not necessarily positioning a mobile robot on a map (Chaplot et al, 2018); it can also be locating and tracking an object in the workspace of a manipulator with force–torque sensor measurements (Wirnshofer et al, 2020; Schneider et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

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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