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Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (Reasoning and Learning Lab)ORCID iD: 0009-0005-8841-181X
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (Reasoning and Learning Lab)ORCID iD: 0000-0002-8546-4431
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (Reasoning and Learning Lab)ORCID iD: 0000-0002-9595-2471
2024 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent's favor. The proposed planner, Dynamic AutonomousExploration Planner (DAEP), extends AEP [1] to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in dynamic settings, we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.

Place, publisher, year, edition, pages
2024.
Keywords [en]
3D-exploration, dynamic environments, planning under uncertainty, collision avoidance
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:liu:diva-205049OAI: oai:DiVA.org:liu-205049DiVA, id: diva2:1873231
Conference
International Conference on Robotics and Automation, Yokohama, Japan, 13-17 Maj, 2024.
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2024-06-19 Created: 2024-06-19 Last updated: 2025-02-07Bibliographically approved

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Wiman, EmilTiger, MattiasHeintz, Fredrik

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Wiman, EmilWidén, LudvigTiger, MattiasHeintz, Fredrik
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CiteExportLink to record
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