Online social robot navigation in indoor, large and crowded environments

Silva Mendoza, Steven, Verdezoto, Nervo ORCID: https://orcid.org/0000-0001-5006-4262, Paillacho, Dennys, Millan Norman, Samuel and Hernandez, J. D. ORCID: https://orcid.org/0000-0002-9593-6789 2023. Online social robot navigation in indoor, large and crowded environments. Presented at: IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 29 May – 2 June 2023. 10.1109/ICRA48891.2023.10160603

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Abstract

New robotics applications require robots to complete tasks in social spaces (i.e. environments shared with people), thus arising the necessity of enabling robots to operate in a socially acceptable manner. Some social spaces tend to be large and crowded (e.g. museums, shopping malls), which require robots to move around while showing appropriate social behaviors (e.g. not interfering with human's comfortable areas). Moving under such conditions is generally called social robot navigation, and there are different approaches to do so. Nonetheless, current approaches are mostly limited to navigate large and outdoor spaces, where both robots and people can easily avoid each other. Other approaches have been tested in indoor environments, however, the test environments tend to be small and largely empty. In this paper, we present an online social robot navigation framework, which allow robots to navigate indoor, large and crowded environments, while showing social behaviors. Our framework consists of 3 modules: 1) world modeling that incorporates a novel Social Heatmap (SH) to represent crowded areas, 2) multilayered path planning that uses sampling-based approaches, and 3) path following control. We extensively benchmark our approach against state- of-the-art approaches in challenging simulated scenarios, and we also demonstrate its feasibility with the Pepper robot in real-world trials.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Submitted
Schools:	Computer Science & Informatics
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics
Additional Information:	Copyright IEEE.
Date of First Compliant Deposit:	1 March 2023
Date of Acceptance:	17 January 2023
Last Modified:	03 Nov 2023 02:05
URI:	https://orca.cardiff.ac.uk/id/eprint/156489

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