Towards Adaptive Environment Perception and Understanding for Autonomous Mobile Robots

Hofmann C, Taliercio F, Walter J, Franke J, Reitelshöfer S (2023)

Publication Type: Conference contribution

Publication year: 2023

Publisher: IEEE

City/Town: New York City

Conference Proceedings Title: 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI)

Event location: Bonn

DOI: 10.1109/SDF-MFI59545.2023.10361496

Abstract

Autonomous mobile robots (AMRs) are increasingly used in various applications like intralogistics, hospitality and agriculture. Though, their software and abilities are still mainly designed and implemented for specific environments and tasks, impeding an even more widespread deployment of AMRs. Their environment perception and understanding lacks automated adaption to specific operating environments and conditions.In this paper, we propose a software architecture that allows the automated adaption of AMRs’ environment perception and understanding to different operating environments. Further, we provide a concise review of state-of-the-art approaches and technologies needed. We present an exemplary implementation and evaluate the proposed architecture. On this basis, advantages and open research questions of the architecture are derived.

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How to cite

APA:

Hofmann, C., Taliercio, F., Walter, J., Franke, J., & Reitelshöfer, S. (2023). Towards Adaptive Environment Perception and Understanding for Autonomous Mobile Robots. In 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI). Bonn, DE: New York City: IEEE.

MLA:

Hofmann, Christian, et al. "Towards Adaptive Environment Perception and Understanding for Autonomous Mobile Robots." Proceedings of the 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration, Bonn New York City: IEEE, 2023.

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