Collective Perception Using UAVs: Autonomous Aerial Reconnaissance in a Complex Urban Environment.

Area of Science:

• Robotics and Autonomous Systems
• Artificial Intelligence
• Urban Planning and Operations

Background:

• Complex urban environments pose challenges for reconnaissance due to obstacles and terrain.
• Occlusion frequently hinders the identification of objects of interest in such settings.
• Autonomous reconnaissance requires efficient exploration strategies.

Purpose of the Study:

• To formulate the problem of autonomous reconnaissance in complex urban environments using unmanned aerial vehicles (UAVs).
• To propose a metaheuristic algorithm for optimizing UAV reconnaissance missions.
• To maximize the monitored area and minimize operation time.

Main Methods:

• Formulation of the autonomous reconnaissance problem in urban environments.
• Development of a metaheuristic algorithm for waypoint deployment and route planning.
• Computer simulations for algorithm verification and performance analysis.

Main Results:

• The proposed algorithm effectively plans routes for UAV swarms to maximize coverage.
• Simulation results demonstrate the algorithm's efficiency in minimizing reconnaissance time.
• Comparison with benchmark instances and real-world scenarios validates the approach.

Conclusions:

• The metaheuristic algorithm provides an effective solution for autonomous urban reconnaissance.
• The method enhances the efficiency and coverage of UAV-based surveillance operations.
• The findings are applicable to real-world reconnaissance missions in challenging environments.