Layered Outer-Loop Control for Disturbance-Robust Multi-Waypoint UAV Arrival

• URL: http://arxiv.org/abs/2606.26315v1
• Date: Wed, 24 Jun 2026 18:58:57 GMT
• Title: Layered Outer-Loop Control for Disturbance-Robust Multi-Waypoint UAV Arrival
• Abstract summary: Disturbance-robust UAV position control is easy to demonstrate in benign simulations but hard to make fast in approach, well behaved near the target, and credible beyond a single benchmark.<n>This letter presents a layered terminal-control architecture for multi-waypoint UAV position control with a staged evaluation across PyBullet, PX4/Gazebo, and hardware.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disturbance-robust UAV position control is easy to demonstrate in benign simulations but much harder to make fast in approach, well behaved near the target, and credible beyond a single benchmark. This letter presents a layered terminal-control architecture for multi-waypoint UAV position regulation together with a staged evaluation across PyBullet, PX4/Gazebo, and hardware. Phase I uses a PyBullet benchmark with stochastic wind for rapid structural selection, identifying a controller core that separates smooth approach generation, persistent-bias compensation, and supervised near-target terminal regulation. Phase II carries only that main architecture into a more demanding PX4/Gazebo closed loop, where the outer-loop controller acts through a cascaded flight stack with delay-sensitive settling and stronger transit-to-hover coupling. This step exposes which benchmark gains survive autopilot-mediated dynamics and which refinements collapse once the loop becomes more deployment-like. In Phase I, the bare controller attains 0.024 m mean late-stage wind error. In Phase II, the final controller is selected using a transfer-oriented rule emphasizing absence of benchmark priors, cross-scenario balance, and deployable supervisory logic. Strict is used as the primary reporting reference; the supplementary retrospective Grace analysis shows that part of the residual failure set is sensitive to completion semantics rather than gross waypoint-miss behaviour. The evaluation is completed on one Vicon-tracked Tello stack through a two-level hardware study. Taken together, the results suggest that benchmark success becomes more informative when the main controller design is separated from benchmark-specific refinement and remains defensible under harder closed-loop evaluation.

Related papers

• One Step, One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control [62.82372407840088]
  We show that same-point domain gradients may remain nearly even when consecutive realized updates partially reverse one another in output space.<n>We propose OSOL, which designates a focus domain at each iteration, to rank token-level rebound risk.<n>OSOL reaches a domain-macro average of 0.4822, improving by 5.7% over the strongest compared baseline, without explicit higher-order differentiation.
  arXiv  Detail & Related papers  (2026-09-06T08:32:59Z)
• Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking [0.0]
  This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states.<n>The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories.
  arXiv  Detail & Related papers  (2026-08-11T06:27:45Z)
• Branch-resolved Pauli-block spectroscopy of residual conditional phase in two-qubit gates [0.0]
  We introduce branch-resolved Pauli-block spectroscopy to estimate the per-cycle residual ZZ-rotation angle theta_c with its sign.<n>The approach yields a low-overhead, signed per-cycle estimate of residual conditional phase that standard fidelity benchmarks underresolve at leading order.
  arXiv  Detail & Related papers  (2026-07-11T10:32:05Z)
• Phase-Conditioned Imitation Learning with Autonomous Failure Recovery for Robust Deformable Object Manipulation [5.524438437366922]
  We present a phase-conditioned, force-aware framework for deformable object manipulation.<n>A FiLM-conditioned ACT encoder modulates feature extraction based on the current task phase.<n>A multi-modal phase predictor fusing visual, force, and pose feedback estimates the phase in real time, detecting contact failures that are invisible to vision alone.
  arXiv  Detail & Related papers  (2026-05-28T05:59:53Z)
• Tightly-Coupled Estimation and Guidance for Robust Low-Thrust Rendezvous via Adaptive Homotopy [0.0]
  This paper proposes a tightly-coupled estimation and guidance architecture.<n> navigation confidence directly modulates the homotopy parameter of a receding-horizon indirect optimal control solver.<n>A composite score from the normalized innovation and MTF activity is mapped online to the homotopy parameter, allowing the controller to relax.
  arXiv  Detail & Related papers  (2026-05-06T04:17:21Z)
• Posterior Augmented Flow Matching [64.1559809786948]
  Posterior-Augmented Flow Matching (PAFM) is a generalization of flow matching (FM)<n>We show that PAFM improves over FM by up to 3.4 FID50K across different model scales.
  arXiv  Detail & Related papers  (2026-05-01T17:59:59Z)
• Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors [0.0]
  This paper presents a ROS2-native arbitration module that fuses the outputs of two controllers.<n>At each control step, both controllers propose Ackermann commands, and a PPO-trained policy predicts a continuous gate from a compact feature observation.<n>The study is intended as a command-level robustness evaluation in a modular ROS2 setting, not as a replacement for planning-level interaction reasoning.
  arXiv  Detail & Related papers  (2026-03-28T14:09:55Z)
• Meta-Adaptive Beam Search Planning for Transformer-Based Reinforcement Learning Control of UAVs with Overhead Manipulators under Flight Disturbances [8.618483849755604]
  A drone equipped with overhead manipulators offer unique capabilities for inspection, maintenance, and contact-based interaction.<n>The motion of the drone and its manipulator is tightly linked, and even small attitude changes caused by wind or control imperfections shift the end-effector away from its intended path.<n>We develop a reinforcement-learning framework with a transformer-based double deep Q learning (DDQN)<n>This allows the controller to anticipate the end-effector's motion through simulated rollouts rather than executing those actions directly on the actual model.
  arXiv  Detail & Related papers  (2026-03-27T17:08:40Z)
• Diffusion Controller: Framework, Algorithms and Parameterization [54.82539154511621]
  We present a unified control-theoretic view that casts reverse diffusion sampling as state-only control within (generalized) linearly-solvable Markov Decision Processes.<n>Under this framework, control acts by reweighting the pretrained reverse-time transition kernels, balancing terminal objectives against an $f$-divergence cost.<n>Experiments on Stable Diffusion v1.4 show consistent gains in preference-alignment win rates and improved quality-efficiency trade-offs.
  arXiv  Detail & Related papers  (2026-03-07T01:49:59Z)
• Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control [61.155940786140455]
  Reinforcement learning (RL) has shown promising results in active flow control (AFC)<n>Current AFC benchmarks rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support.<n>We introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC.
  arXiv  Detail & Related papers  (2026-01-21T14:13:44Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)
• Semi-DETR: Semi-Supervised Object Detection with Detection Transformers [105.45018934087076]
  We analyze the DETR-based framework on semi-supervised object detection (SSOD) We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector. Our method outperforms all state-of-the-art methods by clear margins.
  arXiv  Detail & Related papers  (2023-07-16T16:32:14Z)
• Designing a Robust Low-Level Agnostic Controller for a Quadrotor with Actor-Critic Reinforcement Learning [0.38073142980732994]
  We introduce domain randomization during the training phase of a low-level waypoint guidance controller based on Soft Actor-Critic. We show that, by introducing a certain degree of uncertainty in quadrotor dynamics during training, we can obtain a controller that is capable to perform the proposed task using a larger variation of quadrotor parameters.
  arXiv  Detail & Related papers  (2022-10-06T14:58:19Z)
• Actor-Critic based Improper Reinforcement Learning [61.430513757337486]
  We consider an improper reinforcement learning setting where a learner is given $M$ base controllers for an unknown Markov decision process. We propose two algorithms: (1) a Policy Gradient-based approach; and (2) an algorithm that can switch between a simple Actor-Critic scheme and a Natural Actor-Critic scheme.
  arXiv  Detail & Related papers  (2022-07-19T05:55:02Z)

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.