Abstract
Autonomous path planning for radar and sonar sensing faces significant challenges arising from dynamic targets, obstacle occlusions, and low signal-to-noise (SNR) conditions. We propose a hierarchical sensor scheduling framework that combines a long-horizon strategic planner, based on the Rapidly-exploring Random Tree star (RRT*) algorithm, with a fast-adapting tactical planner. Efficient coordination of the two planners is achieved via a novel message passing mechanism, enabling guidance of the sensor out of complex environments while maintaining effective target tracking. Additionally, we introduce an RRT∗ rerooting strategy that significantly reduces computation time and so expedites the update of the strategic plan.
| Original language | English |
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| Title of host publication | Proceedings of the 2025 28th International Conference on Information Fusion, FUSION 2025 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 1-8 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781037056239 |
| ISBN (Print) | 9798331503505 |
| DOIs | |
| Publication status | Published - 26 Aug 2025 |
| Event | 28th International Conference on Information Fusion, FUSION 2025 - Rio de Janiero, Brazil Duration: 7 Jul 2025 → 11 Jul 2025 https://fusion2025.org/ |
Publication series
| Name | International Conference on Information Fusion (FUSION) |
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| Publisher | IEEE |
| ISSN (Electronic) | 2707-8779 |
Conference
| Conference | 28th International Conference on Information Fusion, FUSION 2025 |
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| Abbreviated title | FUSION 2025 |
| Country/Territory | Brazil |
| City | Rio de Janiero |
| Period | 7/07/25 → 11/07/25 |
| Internet address |
Bibliographical note
Publisher Copyright: © 2025 ISIF.Keywords
- message passing
- path planning
- RRT
- sensor scheduling
- trajectory optimization
ASJC Scopus subject areas
- Information Systems
- Signal Processing
- Information Systems and Management
- Computer Vision and Pattern Recognition