Occlusion-Aware Ground Target Search by a UAV in an Urban Environment

2025-11-11

AI Summary

To address occlusion-induced line-of-sight (LoS) limitations for unmanned aerial vehicles (UAVs) searching mobile points of interest (POIs) along urban roads, this paper proposes a path planning method based on time-evolving probabilistic visibility volumes (PVVs). The method introduces a dynamic PVV model that jointly incorporates Dubins motion constraints and LoS sensor characteristics; adaptive variable-time-step planning is achieved via max-pooling over temporal PVV sequences, balancing short-term observability and long-term coverage efficiency. An iterative-deepening A* algorithm, enhanced with heuristic probability estimation, optimizes detection success probability. Monte Carlo simulations demonstrate that the proposed approach significantly outperforms baseline methods under high false-alarm rates and dense occlusion conditions, achieving markedly improved target discovery efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This paper considers the problem of searching for a point of interest (POI) moving along an urban road network with an uncrewed aerial vehicle (UAV). The UAV is modeled as a variable-speed Dubins vehicle with a line-of-sight sensor in an urban environment that may occlude the sensor's view of the POI. A search strategy is proposed that exploits a probabilistic visibility volume (VV) to plan its future motion with iterative deepening $A^ast$. The probabilistic VV is a time-varying three-dimensional representation of the sensing constraints for a particular distribution of the POI's state. To find the path most likely to view the POI, the planner uses a heuristic to optimistically estimate the probability of viewing the POI over a time horizon. The probabilistic VV is max-pooled to create a variable-timestep planner that reduces the search space and balances long-term and short-term planning. The proposed path planning method is compared to prior work with a Monte-Carlo simulation and is shown to outperform the baseline methods in cluttered environments when the UAV's sensor has a higher false alarm probability.

Problem

• Searching for moving ground targets in urban environments using UAVs
• Addressing sensor occlusion challenges in complex urban settings
• Planning optimal paths to maximize target detection probability

Innovation

• Uses probabilistic visibility volume for planning
• Employs iterative deepening A* path optimization
• Max-pools visibility for variable-timestep planning