Engineering and Computer Science Faculty Publications
UAS Collision Avoidance Algorithm Based on an Aggregate Collision Cone Approach
Document Type
Article
Publication Date
10-2011
Journal Title
Journal of Aerospace Engineering
Volume
24
Issue
4
First Page
463
Last Page
477
DOI
10.1061/(ASCE)AS.1943-5525.0000081
Abstract
A collision-avoidance (CA) algorithm is developed and implemented that is applicable to many different unmanned aerial systems (UAS), ranging from a single platform with the ability to perform all collision-avoidance functions independently to multiple vehicles performing functions as a cooperative group with collision-avoidance commands computed at a ground station. The algorithm leverages advances in several theoretical fields, including robotics, homing guidance, and airspace management, and considers several approaches to conflict detection and resolution, including the collision cone approach. The collision-avoidance system is exercised and tested by using operational hardware and platforms. Novel developments by using an aggregated collision cone approach allow each unmanned aircraft to detect and avoid collisions with two or more other aircraft simultaneously. The collision-avoidance system is implemented by using a miniature unmanned aircraft with an onboard autopilot. Various simulation and flight test cases are used to demonstrate the algorithm's robustness to different collision encounters at various engagement angles. The flight test results are compared with ideal, software-in-the-loop, and hardware-in-the-loop tests. The results presented are the first known flight tests of two or more unmanned aircraft systems equipped with the same global, three-dimensional, geometric collision-avoidance system.
Keywords
Airplanes, collision avoidance, algorithms, drone aircraft, robotics, automatic pilot, flight testing, simulation methods & models
Recommended Citation
Smith, A. L. and Harmon, Frederick G., "UAS Collision Avoidance Algorithm Based on an Aggregate Collision Cone Approach" (2011). Engineering and Computer Science Faculty Publications. 183.
https://digitalcommons.cedarville.edu/engineering_and_computer_science_publications/183