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

TL;DR

This paper introduces a probabilistic visibility volume-based planning strategy for UAVs to efficiently search for moving ground targets in urban environments, accounting for occlusions and sensor uncertainties.

Contribution

It presents a novel probabilistic visibility volume approach combined with iterative deepening A* for improved search planning in occluded urban settings.

Findings

Outperforms baseline methods in cluttered environments

Effectively manages sensor false alarms

Reduces search space with probabilistic planning

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.