This paper presents an innovative collision avoid-ance and path planning framework for unmanned aerial vehicles (UAVs) using minimal camera-based inputs. The system leverages visual data to predict the future trajectories of nearby flying objects and compute low collision risk paths while maintaining progress toward designated targets. This solution extracts only two essential parameters from the vi-sual feed-bearing and pixel area-enabling practical obstacle detection and avoidance. Furthermore, our approach avoids the target observability problem without relying on extensive ownship maneuvers, allowing collision avoidance with minimal movement. Designed for UAVs operating in shared airspace with manned aircraft, the proposed framework emphasizes autonomous decision-making to improve operational safety. Simulation results demonstrate the system's capability to effec-tively plan avoidance maneuvers and generate feasible routes in complex and dynamic environments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vision-Based Collision Avoidance and Path Planning for UAVs Using Bearing and Pixel Area


    Contributors:


    Publication date :

    2025-05-14


    Size :

    807392 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Path Planning Method for Collision Avoidance of Multiple UAVs

    Kim, Hyeok / Kwak, Jeonghoon / Sim, Guichang et al. | Springer Verlag | 2017



    Path Planning Based on Improved MPC for Fixed Wing UAVs with Collision Avoidance

    Su, Meimei / Liu, Huixia / Hu, Jinwen et al. | British Library Conference Proceedings | 2022


    Path Planning Based on Improved MPC for Fixed Wing UAVs with Collision Avoidance

    Su, Meimei / Liu, Huixia / Hu, Jinwen et al. | TIBKAT | 2022


    Path Planning Based on Improved MPC for Fixed Wing UAVs with Collision Avoidance

    Su, Meimei / Liu, Huixia / Hu, Jinwen et al. | Springer Verlag | 2021