Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation
Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for plan...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2022.890013/full |
| _version_ | 1818235062327443456 |
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| author | Tomáš Vintr Jan Blaha Martin Rektoris Jiří Ulrich Tomáš Rouček George Broughton Zhi Yan Tomáš Krajník |
| author_facet | Tomáš Vintr Jan Blaha Martin Rektoris Jiří Ulrich Tomáš Rouček George Broughton Zhi Yan Tomáš Krajník |
| author_sort | Tomáš Vintr |
| collection | DOAJ |
| description | Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people’s natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people’s presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios. |
| first_indexed | 2024-12-12T11:48:00Z |
| format | Article |
| id | doaj.art-869080ab3e8c4f16a2c741b01a3b4916 |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-12-12T11:48:00Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-869080ab3e8c4f16a2c741b01a3b49162022-12-22T00:25:24ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442022-07-01910.3389/frobt.2022.890013890013Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware NavigationTomáš Vintr0Jan Blaha1Martin Rektoris2Jiří Ulrich3Tomáš Rouček4George Broughton5Zhi Yan6Tomáš Krajník7Laboratory of Chronorobotics, Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicLaboratory of Chronorobotics, Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicLaboratory of Chronorobotics, Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicLaboratory of Chronorobotics, Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicLaboratory of Chronorobotics, Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicLaboratory of Chronorobotics, Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicCIAD UMR 7533, Univ. Bourgogne Franche-Comté, UTBM, Montbéliard, FranceLaboratory of Chronorobotics, Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicDespite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people’s natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people’s presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.https://www.frontiersin.org/articles/10.3389/frobt.2022.890013/fulllong-term navigationplanningspatio-temporal modelinghuman-aware navigationschedulingpedestrian flows |
| spellingShingle | Tomáš Vintr Jan Blaha Martin Rektoris Jiří Ulrich Tomáš Rouček George Broughton Zhi Yan Tomáš Krajník Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation Frontiers in Robotics and AI long-term navigation planning spatio-temporal modeling human-aware navigation scheduling pedestrian flows |
| title | Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation |
| title_full | Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation |
| title_fullStr | Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation |
| title_full_unstemmed | Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation |
| title_short | Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation |
| title_sort | toward benchmarking of long term spatio temporal maps of pedestrian flows for human aware navigation |
| topic | long-term navigation planning spatio-temporal modeling human-aware navigation scheduling pedestrian flows |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2022.890013/full |
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