# Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

## TL;DR

This paper introduces OA-IPP, an obstacle-aware adaptive path planning algorithm for UAVs that improves target search efficiency in cluttered environments by balancing information gain, coverage, and collision avoidance.

## Contribution

The paper presents a novel layered planning strategy with adaptive replanning for UAV target search, integrating Gaussian Process models and real-time obstacle awareness.

## Key findings

- Outperforms existing planners in simulations
- Effective in urban search and rescue scenarios
- Balances multiple objectives for efficient search

## Abstract

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.