Omnidirectional Sensor Placement: A Large-Scale Computational Study and Novel Hybrid Accelerated-Refinement Heuristics

TL;DR

This paper investigates large-scale omnidirectional sensor placement in 2D environments, comparing heuristics and introducing hybrid accelerated-refinement methods that improve solution quality and efficiency, with applications in robotics and coverage tasks.

Contribution

It provides a comprehensive computational study of existing heuristics and introduces novel hybrid heuristics that outperform traditional methods in sensor count and runtime.

Findings

HAR heuristics significantly reduce sensor count

HAR improves runtime efficiency of sampling-based methods

Effective in scenarios with moderate localization uncertainty

Abstract

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.