Online Adaptation for Flying Quadrotors in Tight Formations

• URL: http://arxiv.org/abs/2506.17488v1
• Date: Fri, 20 Jun 2025 21:49:17 GMT
• Title: Online Adaptation for Flying Quadrotors in Tight Formations
• Authors: Pei-An Hsieh, Kong Yao Chee, M. Ani Hsieh,
• Abstract summary: Complex aerodynamic wake interactions can destabilize individual team members as well as the team.<n>We present L1 KNODE-DW MPC, an adaptive, mixed expert learning based control framework.<n>Our results show that the proposed framework is capable of enabling the three-quadrotor team to remain vertically aligned in close proximity throughout the flight.
• Score: 10.227479910430866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of flying in tight formations is challenging for teams of quadrotors because the complex aerodynamic wake interactions can destabilize individual team members as well as the team. Furthermore, these aerodynamic effects are highly nonlinear and fast-paced, making them difficult to model and predict. To overcome these challenges, we present L1 KNODE-DW MPC, an adaptive, mixed expert learning based control framework that allows individual quadrotors to accurately track trajectories while adapting to time-varying aerodynamic interactions during formation flights. We evaluate L1 KNODE-DW MPC in two different three-quadrotor formations and show that it outperforms several MPC baselines. Our results show that the proposed framework is capable of enabling the three-quadrotor team to remain vertically aligned in close proximity throughout the flight. These findings show that the L1 adaptive module compensates for unmodeled disturbances most effectively when paired with an accurate dynamics model. A video showcasing our framework and the physical experiments is available here: https://youtu.be/9QX1Q5Ut9Rs

Related papers

• Flying Quadrotors in Tight Formations using Learning-based Model Predictive Control [30.715469693232492]
  In this work, we propose a framework that combines the benefits of first-principles modeling and data-driven approaches. We show that incorporating the model into a novel learning-based predictive model control framework results in substantial performance improvements. Our framework also achieves exceptional sample efficiency, using only a total of 46 seconds of flight data for training.
  arXiv  Detail & Related papers  (2024-10-13T05:03:16Z)
• Physics Enhanced Residual Policy Learning (PERPL) for safety cruising in mixed traffic platooning under actuator and communication delay [8.172286651098027]
  Linear control models have gained extensive application in vehicle control due to their simplicity, ease of use, and support for stability analysis. Reinforcement learning (RL) models, on the other hand, offer adaptability but suffer from a lack of interpretability and generalization capabilities. This paper aims to develop a family of RL-based controllers enhanced by physics-informed policies.
  arXiv  Detail & Related papers  (2024-09-23T23:02:34Z)
• PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion [1.123472110161393]
  A core strength of Model Predictive Control (MPC) for quadrupedal locomotion has been its ability to enforce constraints.<n>We propose a framework that integrates proprioceptive planning with Reinforcement Learning (RL)<n>During deployment, the Dreamer module solves an infinite-horizon MPC problem, adapting actions and velocity commands to respect the constraints.
  arXiv  Detail & Related papers  (2024-09-14T13:51:37Z)
• MetaFollower: Adaptable Personalized Autonomous Car Following [63.90050686330677]
  We propose an adaptable personalized car-following framework - MetaFollower. We first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability.
  arXiv  Detail & Related papers  (2024-06-23T15:30:40Z)
• FullLoRA: Efficiently Boosting the Robustness of Pretrained Vision Transformers [72.83770102062141]
  Vision Transformer (ViT) model has gradually become mainstream in various computer vision tasks.<n>Existing large models tend to prioritize performance during training, potentially neglecting the robustness.<n>We develop novel LNLoRA module, incorporating a learnable layer normalization before the conventional LoRA module.<n>We propose the FullLoRA framework by integrating the learnable LNLoRA modules into all key components of ViT-based models.
  arXiv  Detail & Related papers  (2024-01-03T14:08:39Z)
• Learning to Fly in Seconds [7.259696592534715]
  We show how curriculum learning and a highly optimized simulator enhance sample complexity and lead to fast training times. Our framework enables Simulation-to-Reality (Sim2Real) transfer for direct control after only 18 seconds of training on a consumer-grade laptop.
  arXiv  Detail & Related papers  (2023-11-22T01:06:45Z)
• SO(2)-Equivariant Downwash Models for Close Proximity Flight [6.297269227845377]
  We present a novel learning-based approach for modelling the downwash forces that exploits the latent geometries (i.e. symmetries) present in the problem. We demonstrate that when trained with only 5 minutes of real-world flight data, our geometry-aware model outperforms state-of-the-art baseline models trained with more than 15 minutes of data.
  arXiv  Detail & Related papers  (2023-05-30T12:27:47Z)
• Pre-training Language Model as a Multi-perspective Course Learner [103.17674402415582]
  This study proposes a multi-perspective course learning (MCL) method for sample-efficient pre-training. In this study, three self-supervision courses are designed to alleviate inherent flaws of "tug-of-war" dynamics. Our method significantly improves ELECTRA's average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks.
  arXiv  Detail & Related papers  (2023-05-06T09:02:10Z)
• Learning a Single Near-hover Position Controller for Vastly Different Quadcopters [56.37274861303324]
  This paper proposes an adaptive near-hover position controller for quadcopters. It can be deployed to quadcopters of very different mass, size and motor constants. It also shows rapid adaptation to unknown disturbances during runtime.
  arXiv  Detail & Related papers  (2022-09-19T17:55:05Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads [69.21503033239985]
  Transporting suspended payloads is challenging for autonomous aerial vehicles. We propose a meta-learning approach that "learns how to learn" models of altered dynamics within seconds of post-connection flight data.
  arXiv  Detail & Related papers  (2020-04-23T17:43:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.