Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs.

Area of Science:

• Robotics
• Aerospace Engineering
• Artificial Intelligence

Background:

• Current unmanned aerial vehicle (UAV) path planning methods often generate suboptimal trajectories with sharp turns.
• These trajectories are unsuitable for smooth and efficient UAV flight, especially in dynamic environments.

Purpose of the Study:

• To develop an improved UAV path planning approach for generating smooth, collision-free, and feasible trajectories.
• To enhance the efficiency and performance of UAVs in complex, unknown, and highly dynamic environments.

Main Methods:

• A novel path planning approach based on distance gradients for UAVs.
• Collision-free path generation using initial trajectory and obstacle information.
• Trajectory optimization using distance gradient information and a time adjustment method.
• Application of the Limited-memory BFGS algorithm for efficient local path optimization.

Main Results:

• The proposed method successfully generates collision-free and smooth trajectories for UAVs.
• Validation in the Robot Operating System simulation environment confirms effectiveness in complex, dynamic scenarios.
• The approach demonstrates superior performance compared to traditional methods in solution speed, trajectory length, and data volume.

Conclusions:

• The distance gradient-based path planning method offers significant improvements for UAV trajectory generation.
• This approach enhances UAV operational capabilities in challenging environments, meeting dynamic planning requirements.
• The method provides a faster, more efficient, and smoother alternative for UAV path planning.