Obstacle-Free Path Planning for Autonomous Drones Using Floyd Algorithm

TL;DR

This paper evaluates the Floyd algorithm's effectiveness in planning shortest obstacle-free paths for autonomous drones in large fields, demonstrating its efficiency and specific computational characteristics.

Contribution

It introduces the application of Floyd algorithm for UAV path planning in obstacle-rich environments and analyzes its computational performance.

Findings

Floyd algorithm effectively plans shortest obstacle-free paths for UAVs.

Time complexity of Floyd algorithm is O(n^3).

Path planning time correlates cubically with field size, not obstacles or UAV count.

Abstract

This research investigates the efficiency of Floyd algorithm for obstacle-free path planning for autonomous aerial vehicles (UAVs) or drones. Floyd algorithm is used to generate the shortest paths for UAVs to fly from any place to the destination in a large-scale field with obstacles which UAVs cannot fly over. The simulation results demonstrated that Floyd algorithm effectively plans the shortest obstacle-free paths for UAVs to fly to a destination. It is verified that Floyd algorithm holds a time complexity of O(n3). This research revealed a correlation of a cubic polynomial relationship between the time cost and the size of the field, no correlation between the time cost and the number of obstacles, and no correlation between the time cost and the number of UAVs in the tested field. The applications of the research results are discussed in the paper as well.