QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

Abstract

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations.

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

“…The method described in [135] utilizes Euclidean Geometry to estimate intercepting points by using a specified safe perimeter in the form of a square area. Research [133] incorporated a sliding-mode-based reactive control algorithm and a dynamic programming-based global path planning system to operate UAVs in 3D environments.…”

“…Several notable contributions propose aviation dispute resolution strategies. Geometric [135], [143], force field [131], [132], optimum trajectory [119], and Markov Decision Process (MDP) [108], [110] approaches are the most used [144]. Numerous suggested algorithms using mathematical approaches [5], [72], [89], [105], [129] and ML [100], [128] need a strong processor and memory space, which cannot accommodate most UAVs operating in the real world.…”

“…The complexity, variety, and technological advancements of modern UAV missions drive their pursuit of greater and flying stability [136]. As a result, autonomous UAVs with pre-programmed routes, identification, and avoiding obstacles algorithms on computers have been developed [135], [145], [146]. In the bulk of the reviewed algorithms, supervised learning methods are used to identify and avoid obstacles.…”