Collision Detection for UAVs Based on GeoSOT-3D Grids

Weixin Zhai,Chengqi Cheng,Fuhu Ren,Xiaochong Tong,Shuangxi Miao

doi:10.3390/ijgi8070299

Abstract

The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation increases exponentially with an increasing number of spatial entities; second, existing grid-based approaches are unsuitable for complicated scenarios with a large number of objects moving at high speeds. In the proposed model, we first identified UAVs and other spatial objects with GeoSOT-3D grids. Second, the nonrelational spatial database was initialized with a multitable strategy, and spatiotemporal data were inserted with the GeoSOT-3D grid codes as the primary key. Third, the collision detection procedure was transformed from a pairwise calculation to a multilevel query. Four simulation experiments were conducted to verify the feasibility and efficiency of the proposed collision detection model for UAVs in different environments. The results also indicated that 64 m GeoSOT-3D grids are the most suitable basic grid size, and the reduction in the time consumption compared with traditional methods reached approximately 50–80% in different scenarios.

Highlights

Over the last decade, unmanned aerial vehicles (UAVs) have been utilized in a wide range of military and civilian applications, including surveillance [1,2], monitoring [3,4], imaging [5,6], and reconnaissance [7]
Collision detection with three-dimensional coordinates is based on the pairwise calculation approach, in which the collision is detected by computing the Euclidean distance between every two UAVs and comparing the calculated distance with the threshold (UAV size)
Collision detection with simple 64 m grids is based on traversing the grids and identifying whether there is more than one UAV in a GeoSOT-3D grid

Summary

Introduction

Over the last decade, unmanned aerial vehicles (UAVs) have been utilized in a wide range of military and civilian applications, including surveillance [1,2], monitoring [3,4], imaging [5,6], and reconnaissance [7]. UAVs boast many advantages, including remarkable flexibility, low energy consumption, high efficiency and the capacity for real-time monitoring. Two categories of approaches have been employed for collision detection. The first category includes simple collision detection methods based mainly on sensing technologies, such as automatic dependent surveillance-broadcast (ADS-B) technology, visual sensors, optical flow, radar, and light detection and ranging (LiDAR), to alter the course of a flying vehicle toward another direction in the short term to avoid collisions. Sabatini et al.’s research provided an obstacle detection solution especially suitable for low-altitude UAVs with the remarkable angular resolution and accuracy of LiDAR [12]. Becker and Bouabdallah [13] employed four ultrasound sensors for collision detection and a camera system based on above-ground optical flow computations for positioning. It is challenging for these methods to develop a flight plan ahead of time

Objectives

Methods

Results

Conclusion