A comparative study of drones path planning and bezier curve optimization based on multi-strategy search algorithm

Area of Science:

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

Background:

• Drones are increasingly used in complex 3D urban environments for monitoring and emergency response.
• Challenges include navigating high-rise buildings, underground structures, and dynamic obstacles.
• Efficient path planning is critical for drone speed, stealth, and mission success.

Purpose of the Study:

• To investigate and compare multi-strategy algorithms for 3D urban drone path planning.
• To evaluate the effectiveness of combining algorithms with Bézier curve optimization.
• To provide adaptable path planning strategies for law enforcement and other urban applications.

Main Methods:

• Comparison of three path planning algorithms: Rapidly-exploring Random Trees (RRT), Ant Colony Optimization (ACO), and A*.
• Evaluation of algorithm performance in 3D gridded urban environments.
• Integration of Bézier curve optimization techniques with selected algorithms.

Main Results:

• RRT demonstrated suitability for dynamic urban environments.
• ACO proved effective for global path searches.
• A* was found to be efficient in structured urban settings.
• Combined approaches with Bézier curves showed promise for adaptable path planning.

Conclusions:

• Different algorithms offer unique advantages for specific urban drone navigation challenges.
• Bézier curve optimization enhances the adaptability of path planning strategies.
• The findings support improved drone obstacle avoidance and robot navigation in complex urban areas.