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

## TL;DR

This paper compares different path planning algorithms for drones in complex urban environments and uses Bézier curves to optimize paths.

## Contribution

The novelty lies in combining multi-strategy algorithms with Bézier curve optimization for 3D drone path planning.

## Key findings

- RRT is best for dynamic environments, ACO for global searches, and A* for structured settings.
- Combining these algorithms with Bézier curves improves path adaptability and smoothness.
- The approach is suitable for drone obstacle avoidance and robot navigation in urban areas.

## Abstract

With the growing use of drones in urban monitoring and emergency search and rescue, the three-dimensional environments they navigate are becoming more complex, including high-rise buildings, underground pipelines, and dynamic obstacles. Efficient path planning is crucial for drones to respond quickly, infiltrate covertly, and ensure mission success. This paper focuses on path planning in three-dimensional gridded urban environments, examining multi-strategy algorithms and Bézier curve optimization techniques for law enforcement operations. The study compares three algorithms: Rapidly-exploring Random Trees (RRT), Ant Colony Optimization (ACO), and A*. Each algorithm has distinct advantages: RRT is ideal for dynamic environments, ACO is effective for global searches, and A* is suited for structured environments. By evaluating these algorithms and combining them with Bézier curve optimization, this paper offers adaptable path planning strategies for applications like drone obstacle avoidance and robot navigation.