UAV path planning based on third-party risk modeling.

Area of Science:

• Robotics and Autonomous Systems
• Urban Planning and Risk Management
• Artificial Intelligence in Transportation

Background:

• Drones offer urban convenience but pose significant third-party risks, including casualties and property damage from potential failures.
• Current drone path planning primarily optimizes for minimal flight distance, neglecting crucial third-party risk factors.
• The increasing integration of Unmanned Aerial Vehicles (UAVs) into urban airspace necessitates advanced safety and risk mitigation strategies.

Purpose of the Study:

• To propose a comprehensive risk-cost path planning method for UAVs operating in urban environments.
• To develop a third-party risk model integrating obstacle, death, and property loss risks for urban flight.
• To enhance the safety and reliability of UAV operations by minimizing exposure to high-risk urban zones.

Main Methods:

• Development of a third-party risk model assessing urban hazards (obstacles, fatalities, property damage).
• Implementation of a Min-cost A* algorithm incorporating city-wide risk assessment for pathfinding.
• Application of an improved Floyd algorithm for smoothing the generated low-risk UAV flight paths.

Main Results:

• The proposed method effectively reduces the overall risk associated with UAV flight paths in urban settings.
• Demonstrated improvement in the reliability and safety of UAV navigation within complex urban landscapes.
• Successfully addressed the challenge of planning UAV routes that actively mitigate third-party risks.

Conclusions:

• The novel risk-cost path planning approach significantly enhances UAV safety in urban environments.
• Integrating comprehensive risk assessment into path planning is crucial for responsible drone deployment.
• This method provides a viable solution for mitigating third-party risks in autonomous urban aerial mobility.