With the increase of autonomy in aircraft systems over the past decade, the question of whether autonomous agents provide better performance when compared to human operators is of utmost importance. Current research using machine learning for obstacle avoidance in small Unmanned Aircraft Systems has gained extensive attention in recent years, but who is faster, the human or the machine? This study leverages programmable coding languages using Unmanned Aircraft Systems (UAS) to navigate a fabricated obstacle course environment. By applying various methods of intelligent agent training, human pilots were compared to the performance of the automated system. The Artificial Intelligence (AI) training ensured that the automated flight path and speed for the Unmanned Aircraft System were optimized for the course environment. We collected a dataset from a sample size of 12 participants, including zero knowledge of flight training, to experienced pilots. The findings of this work aimed to show the performance comparison, including Key System Attributes and Key Performance Parameters, by considering drone speed, pilot skill level, and course crashes between man and machine to determine which is a safer and faster solution.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Human Vs. Autonomous Agents: Drone racing and Obstacle Avoidance


    Contributors:


    Publication date :

    2022-09-18


    Size :

    1862829 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset

    Delmerico, Jeffrey / Cieslewski, Titus / Rebecq, Henri et al. | BASE | 2019

    Free access

    DRONE CONTROLLING METHOD FOR OBSTACLE AVOIDANCE

    European Patent Office | 2024

    Free access

    Drone Racing Frame

    RHEE JUN WHI | European Patent Office | 2020

    Free access

    Drone obstacle avoidance using real-time wind estimation

    DE LA GUARDIA GONZALEZ RAFAEL / POHL DANIEL / GOMEZ GUTIERREZ DAVID et al. | European Patent Office | 2021

    Free access

    Surveillance FPV Drone with Obstacle Avoidance System

    Sunay Dharwadkar / Sarvesh Harmalkar / Chinmay Nagzarkar et al. | BASE | 2020

    Free access