M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909406309908480 |
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| author | Qin, Chuhao Robins, Alexander Lillywhite-Roake, Callum Pearce, Adam Mehta, Hritik James, Scott Wong, Tsz Ho Pournaras, Evangelos |
| author_facet | Qin, Chuhao Robins, Alexander Lillywhite-Roake, Callum Pearce, Adam Mehta, Hritik James, Scott Wong, Tsz Ho Pournaras, Evangelos |
| contents | Distributed sensing by cooperative drone swarms is crucial for several Smart City applications, such as traffic monitoring and disaster response. Using an indoor lab with inexpensive drones, a testbed supports complex and ambitious studies on these systems while maintaining low cost, rigor, and external validity. This paper introduces the Multi-drone Sensing Experimentation Testbed (M-SET), a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence. M-SET addresses the limitations of existing testbeds that fail to emulate collisions, thus lacking realism in outdoor environments. By integrating a collision avoidance method based on a potential field algorithm, M-SET ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm. Extensive evaluation demonstrates accurate energy consumption estimation and a low risk of collisions, providing a robust proof-of-concept. New insights show that M-SET has significant potential to support ambitious research with minimal cost, simplicity, and high sensing quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism Qin, Chuhao Robins, Alexander Lillywhite-Roake, Callum Pearce, Adam Mehta, Hritik James, Scott Wong, Tsz Ho Pournaras, Evangelos Robotics Distributed, Parallel, and Cluster Computing Distributed sensing by cooperative drone swarms is crucial for several Smart City applications, such as traffic monitoring and disaster response. Using an indoor lab with inexpensive drones, a testbed supports complex and ambitious studies on these systems while maintaining low cost, rigor, and external validity. This paper introduces the Multi-drone Sensing Experimentation Testbed (M-SET), a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence. M-SET addresses the limitations of existing testbeds that fail to emulate collisions, thus lacking realism in outdoor environments. By integrating a collision avoidance method based on a potential field algorithm, M-SET ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm. Extensive evaluation demonstrates accurate energy consumption estimation and a low risk of collisions, providing a robust proof-of-concept. New insights show that M-SET has significant potential to support ambitious research with minimal cost, simplicity, and high sensing quality. |
| title | M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism |
| topic | Robotics Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2406.10916 |