Skip to main content
Search

# Quadrotor Navigation

2 related articles

Subscribe to updates
  1. arXiv

    ScanDreamer gives quadrotors an online map to imagine with: 96.3% success and 3.6% collisions in unseen simulated layouts

    For quadrotor navigation through unknown clutter, ScanDreamer builds an online 2D scan-memory map from depth images and vehicle pose, and conditions its world model, actor and critic on that map, so imagined rollouts advance the pose over a frozen map and read geodesic progress and wall-contact signals from mapped geometry. Across 1560 test episodes in NVIDIA Isaac Sim spanning unseen layouts and parameter ranges, three seeds average 96.3% success and 3.6% collisions, against 59.6% success and 28.8% collisions for AirDreamer at the same online-interaction budget.

    Nanyang Technological UniversityVinUniversity
    Yunyi Gao（First author）
    ScanDreamerQuadrotor NavigationWorld ModelOccupancy MappingModel-Based Reinforcement Learning
  2. IEEE Robotics and Automation Letters

    WAND estimates wind disturbance with a TCN to drive both policy conditioning and feedforward compensation, raising quadrotor success by 8.3 percentage points on average across 12 wind-disturbed simulation settings and succeeding in 18 of 20 indoor fan tests

    The paper proposes WAND, a reinforcement learning framework for quadrotor navigation in dense obstacle fields under time-varying wind: a Temporal Convolutional Network estimates wind-induced disturbance acceleration from proprioceptive histories, the estimate is injected into the policy through a zero-initialized residual module (WindAdapter) and reused for low-level feedforward compensation, improving observed success rate by 8.3 percentage points on average over feedforward compensation alone across 12 wind-disturbed simulation settings and succeeding in 18 of 20 indoor fan-induced flight trials.

    China Telecom (China)China Telecom
    Zhonghan Tang（First author）
    Wind Disturbance EstimationQuadrotor NavigationWindAdapterTemporal Convolutional NetworkReinforcement Learning

You’re all caught up