Deep reinforcement learning (DRL) has significantly advanced online path planning for unmanned aerial vehicles (UAVs). Nonetheless, DRL-based methods often encounter reduced performance when dealing with unfamiliar scenarios. This decline is mainly caused by the presence of non-causal and domain-specific elements within visual representations, which negatively impact the learning of policies. Present techniques generally depend on predefined augmentation or regularization methods intended to direct the model toward identifying causal and domain-invariant components, thereby enhancing the model’s ability to generalize. However, these manually crafted approaches are intrinsically constrained in their coverage and do not consider the entire spectrum of possible scenarios, resulting in less effective performance in new environments. Unlike prior studies, this work prioritizes representation learning and presents a novel method for causal representation disentanglement. The approach successfully distinguishes between causal and non-causal elements in visual data. By concentrating on causal aspects during the policy learning phase, the impact of non-causal factors is minimized, thereby improving the generalizability of DRL models. Experimental results demonstrate that our technique achieves reliable navigation and effective collision avoidance in unseen scenarios, surpassing state-of-the-art deep reinforcement learning algorithms.


    Access

    Download


    Export, share and cite



    Title :

    UAV Collision Avoidance in Unknown Scenarios with Causal Representation Disentanglement


    Contributors:
    Zhun Fan (author) / Zihao Xia (author) / Che Lin (author) / Gaofei Han (author) / Wenji Li (author) / Dongliang Wang (author) / Yindong Chen (author) / Zhifeng Hao (author) / Ruichu Cai (author) / Jiafan Zhuang (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    PEDESTRIAN COLLISION AVOIDANCE SYSTEM FOR SCENARIOS WITH OCCLUSIONS

    Schraner, Markus / Bouton, Maxime / Kochenderfer, Mykel J. et al. | British Library Conference Proceedings | 2019


    Pedestrian Collision Avoidance System for Scenarios with Occlusions

    Schraner, Markus / Bouton, Maxime / Kochenderfer, Mykel J. et al. | IEEE | 2019


    Collaborative Collision Avoidance for CAVs in Unpredictable Scenarios

    Patel, Dhruvkumar / Zalila-Wenkstern, Rym | IEEE | 2020



    STRETCH: Generating Challenging Scenarios for Testing Collision Avoidance Systems

    Scheuer, Franz / Gambi, Alessio / Arcaini, Paolo | IEEE | 2023