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This is a Quantitative Study study.

October 19, 2025Open Access

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data

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Authors

SSSingh SudhaInstitute of Engineering JJJoel JoseNorwegian University of Science and Technology ECErlend M. CoatesNorwegian University of Science and Technology

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Implication

Simulation experiments show improved mapping accuracy in robotic information gathering, indicating advances in adaptive planning and computational efficiency.

Key Points

The proposed method enhances adaptive planning for mapping continuous scalar fields with Gaussian processes, reducing computational complexity.
Simulation experiments show that this method achieves comparable mapping accuracy to existing benchmarks, making it effective for longer missions.
The technique validates performance with both synthetic and real-world datasets, highlighting its applicability to various scenarios.
Gaussian process regression is utilized for continuous spatial mapping, which is critical for efficient robotic information gathering.

Cite This Study

Sudha et al. (2025) studied this question.

synapsesocial.com/papers/68f4b10d3d9d770bbc6970c8 https://doi.org/10.48550/arxiv.2507.13053

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Implication

Simulation experiments show improved mapping accuracy in robotic information gathering, indicating advances in adaptive planning and computational efficiency.

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data

Discussion

Member takes

Implication

Key Points

Cite This Study

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data

Discussion

Member takes

Implication

Key Points

Cite This Study

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