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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2410.08784 |
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| _version_ | 1866915153590616064 |
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| author | Mikula, Jan Kulich, Miroslav |
| author_facet | Mikula, Jan Kulich, Miroslav |
| contents | This paper studies the omnidirectional sensor-placement problem (OSPP), which involves placing static sensors in a continuous 2D environment to achieve a user-defined coverage requirement while minimizing sensor count. The problem is motivated by applications in mobile robotics, particularly for optimizing visibility-based route planning tasks such as environment inspection, target search, and region patrolling. We focus on omnidirectional visibility models, which eliminate sensor orientation constraints while remaining relevant to real-world sensing technologies like LiDAR, 360-degree cameras, and multi-sensor arrays. Three key models are considered: unlimited visibility, limited-range visibility to reflect physical or application-specific constraints, and localization-uncertainty visibility to account for sensor placement uncertainty in robotics. Our first contribution is a large-scale computational study comparing classical convex-partitioning and sampling-based heuristics for the OSPP, analyzing their trade-off between runtime efficiency and solution quality. Our second contribution is a new class of hybrid accelerated-refinement (HAR) heuristics, which combine and refine outputs from multiple sensor-placement methods while incorporating preprocessing techniques to accelerate refinement. Results demonstrate that HAR heuristics significantly outperform traditional methods, achieving the lowest sensor counts and improving the runtime of sampling-based approaches. Additionally, we adapt a specific HAR heuristic to the localization-uncertainty visibility model, showing that it achieves the required coverage for small to moderate localization uncertainty. Future work may apply HAR to visibility-based route planning tasks or explore novel sensor-placement approaches to achieve formal coverage guarantees under uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08784 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Omnidirectional Sensor Placement: A Large-Scale Computational Study and Novel Hybrid Accelerated-Refinement Heuristics Mikula, Jan Kulich, Miroslav Robotics Computational Geometry This paper studies the omnidirectional sensor-placement problem (OSPP), which involves placing static sensors in a continuous 2D environment to achieve a user-defined coverage requirement while minimizing sensor count. The problem is motivated by applications in mobile robotics, particularly for optimizing visibility-based route planning tasks such as environment inspection, target search, and region patrolling. We focus on omnidirectional visibility models, which eliminate sensor orientation constraints while remaining relevant to real-world sensing technologies like LiDAR, 360-degree cameras, and multi-sensor arrays. Three key models are considered: unlimited visibility, limited-range visibility to reflect physical or application-specific constraints, and localization-uncertainty visibility to account for sensor placement uncertainty in robotics. Our first contribution is a large-scale computational study comparing classical convex-partitioning and sampling-based heuristics for the OSPP, analyzing their trade-off between runtime efficiency and solution quality. Our second contribution is a new class of hybrid accelerated-refinement (HAR) heuristics, which combine and refine outputs from multiple sensor-placement methods while incorporating preprocessing techniques to accelerate refinement. Results demonstrate that HAR heuristics significantly outperform traditional methods, achieving the lowest sensor counts and improving the runtime of sampling-based approaches. Additionally, we adapt a specific HAR heuristic to the localization-uncertainty visibility model, showing that it achieves the required coverage for small to moderate localization uncertainty. Future work may apply HAR to visibility-based route planning tasks or explore novel sensor-placement approaches to achieve formal coverage guarantees under uncertainty. |
| title | Omnidirectional Sensor Placement: A Large-Scale Computational Study and Novel Hybrid Accelerated-Refinement Heuristics |
| topic | Robotics Computational Geometry |
| url | https://arxiv.org/abs/2410.08784 |