Effective and Safe Trajectory Planning for an Autonomous UAV Using a Decomposition-Coordination Method

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Autonomous Unmanned Aerial Vehicles (UAVs) require advanced trajectory planning for safe operation in dynamic environments.
• Existing methods often struggle with real-time reactivity and ensuring both safety and optimality simultaneously.

Purpose of the Study:

• To develop and validate a Decomposition Coordination (DC) method for safe and optimal trajectory planning for autonomous UAVs.
• To enhance UAV reactivity and collision avoidance capabilities in complex, changing environments.

Main Methods:

• A dynamic model of a quadrotor UAV was selected and a multi-objective optimization problem was formulated.
• The problem was converted into a Scalar Optimization Problem (SOP) and decomposed into smaller, parallel sub-problems using the DC principle.
• Coordination between sub-problems was managed via Lagrange multipliers, enabling real-time computation.

Main Results:

• The DC method successfully computed optimal UAV trajectories in real-time from current to target positions.
• The approach demonstrated effective collision avoidance through communication with a Ground Control Unit (GCU) via a wireless network.
• Stability and convergence analyses confirmed the algorithm's robustness.

Conclusions:

• The presented DC method offers a practical and efficient solution for safe trajectory planning of autonomous UAVs.
• The method enhances UAV performance in dynamic environments by ensuring safety, optimality, and real-time reactivity.
• Simulation results validate the method's potential for real-world applications.