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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.01308 |
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| _version_ | 1866909236367196160 |
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| author | Tran, Vu Phi Perera, Asanka G. Garratt, Matthew A. Kasmarik, Kathryn Anavatti, Sreenatha G. |
| author_facet | Tran, Vu Phi Perera, Asanka G. Garratt, Matthew A. Kasmarik, Kathryn Anavatti, Sreenatha G. |
| contents | This paper introduces a state-machine model for a multi-modal, multi-robot environmental sensing algorithm tailored to dynamic real-world settings. The algorithm uniquely combines two exploration strategies for gas source localization and mapping: (1) an initial exploration phase using multi-robot coverage path planning with variable formations for early gas field indication; and (2) a subsequent active sensing phase employing multi-robot swarms for precise field estimation. The state machine governs the transition between these two phases. During exploration, a coverage path maximizes the visited area while measuring gas concentration and estimating the initial gas field at predefined sample times. In the active sensing phase, mobile robots in a swarm collaborate to select the next measurement point, ensuring coordinated and efficient sensing. System validation involves hardware-in-the-loop experiments and real-time tests with a radio source emulating a gas field. The approach is benchmarked against state-of-the-art single-mode active sensing and gas source localization techniques. Evaluation highlights the multi-modal switching approach's ability to expedite convergence, navigate obstacles in dynamic environments, and significantly enhance gas source location accuracy. The findings show a 43% reduction in turnaround time, a 50% increase in estimation accuracy, and improved robustness of multi-robot environmental sensing in cluttered scenarios without collisions, surpassing the performance of conventional active sensing strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01308 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Active Sensing Strategy: Multi-Modal, Multi-Robot Source Localization and Mapping in Real-World Settings with Fixed One-Way Switching Tran, Vu Phi Perera, Asanka G. Garratt, Matthew A. Kasmarik, Kathryn Anavatti, Sreenatha G. Robotics Multiagent Systems This paper introduces a state-machine model for a multi-modal, multi-robot environmental sensing algorithm tailored to dynamic real-world settings. The algorithm uniquely combines two exploration strategies for gas source localization and mapping: (1) an initial exploration phase using multi-robot coverage path planning with variable formations for early gas field indication; and (2) a subsequent active sensing phase employing multi-robot swarms for precise field estimation. The state machine governs the transition between these two phases. During exploration, a coverage path maximizes the visited area while measuring gas concentration and estimating the initial gas field at predefined sample times. In the active sensing phase, mobile robots in a swarm collaborate to select the next measurement point, ensuring coordinated and efficient sensing. System validation involves hardware-in-the-loop experiments and real-time tests with a radio source emulating a gas field. The approach is benchmarked against state-of-the-art single-mode active sensing and gas source localization techniques. Evaluation highlights the multi-modal switching approach's ability to expedite convergence, navigate obstacles in dynamic environments, and significantly enhance gas source location accuracy. The findings show a 43% reduction in turnaround time, a 50% increase in estimation accuracy, and improved robustness of multi-robot environmental sensing in cluttered scenarios without collisions, surpassing the performance of conventional active sensing strategies. |
| title | Active Sensing Strategy: Multi-Modal, Multi-Robot Source Localization and Mapping in Real-World Settings with Fixed One-Way Switching |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2407.01308 |