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

2024-10-11

AI Summary

This paper addresses the Omnidirectional Sensor Placement Problem (OSPP) in two-dimensional environments, aiming to minimize the number of sensors required to satisfy user-specified coverage requirements for visual path planning tasks such as robotic environmental inspection and target search. To handle three critical visibility models—unrestricted visibility, limited sensing range, and localization uncertainty—we propose a Hybrid Acceleration–Refinement (HAR) heuristic framework. HAR is the first to integrate multi-strategy placement outcomes, incorporate an acceleration mechanism based on convex decomposition and sampling-based preprocessing, and explicitly accommodate localization uncertainty. Experimental results demonstrate that HAR significantly reduces sensor count compared to conventional heuristics, improves sampling efficiency, and ensures robust full coverage under moderate levels of localization uncertainty.

Technology Category

• Search and Optimization: Heuristic Search
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizing omnidirectional sensor placement
• Minimizing sensor count in 2D environments
• Enhancing visibility-based robotics tasks

Innovation

• Hybrid Accelerated-Refinement Heuristics
• Omnidirectional visibility models
• Large-scale computational study