Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning

• URL: http://arxiv.org/abs/2107.14764v1
• Date: Fri, 30 Jul 2021 17:14:52 GMT
• Title: Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning
• Abstract summary: Adaptability is achieved by optimizing over a range of off-nominal flight conditions. System maps observations directly to commanded bank angle and angle of attack rates. Minimizing the tracking error keeps the curved space line of sight to the target location aligned with the vehicle's velocity vector.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We use Reinforcement Meta Learning to optimize an adaptive guidance system suitable for the approach phase of a gliding hypersonic vehicle. Adaptability is achieved by optimizing over a range of off-nominal flight conditions including perturbation of aerodynamic coefficient parameters, actuator failure scenarios, and sensor noise. The system maps observations directly to commanded bank angle and angle of attack rates. These observations include a velocity field tracking error formulated using parallel navigation, but adapted to work over long trajectories where the Earth's curvature must be taken into account. Minimizing the tracking error keeps the curved space line of sight to the target location aligned with the vehicle's velocity vector. The optimized guidance system will then induce trajectories that bring the vehicle to the target location with a high degree of accuracy at the designated terminal speed, while satisfying heating rate, load, and dynamic pressure constraints. We demonstrate the adaptability of the guidance system by testing over flight conditions that were not experienced during optimization. The guidance system's performance is then compared to that of a linear quadratic regulator tracking an optimal trajectory.

Related papers

• Constant Time-Delay Leader Following with Neural Networks and Invariant Extended Kalman Filters for Arbitrary Trajectories [0.9176056742068813]
  This paper proposes a constant time-delay trajectory tracking method for vehicle convoys operating without inter-vehicle communication.<n>The method integrates a probabilistic sequence-to-sequence (Seq2Seq) neural network with an invariant extended Kalman filter (IEKF) to warm-start the prediction process.<n>The effectiveness of the method is validated through comparisons with a pure IEKF baseline, learning-based methods, and the ground-truth trajectory in kinematic simulations.
  arXiv  Detail & Related papers  (2026-06-17T16:04:58Z)
• Motion Cues from Image-based Point Tracking for LiDAR Scene Flow Estimation [59.420015367586615]
  We introduce TrackCue, a tracking-guided framework for improving dynamic object representation in LiDAR scene flow estimation.<n>In particular, TrackCue repurposes point tracking to obtain dense image-space trajectories anchored to LiDAR points.<n>We show that TrackCue significantly improves the precision and F1 score of dynamic labels, leading to performance gains in self-supervised scene flow estimation.
  arXiv  Detail & Related papers  (2026-05-16T10:28:54Z)
• Wind-Aware Optimal Trajectory Planning for Efficient Gliding of Fixed-Wing Aerial Systems [7.0414059106180025]
  Gliding offers small fixed-wing UAVs extended endurance and silent operation.<n>Traditional Total Energy Control Systems based controllers regulate the trade between potential and kinetic energy reactively.<n>We present a nonlinear, multi-cost trajectory planner for small UAV gliders.
  arXiv  Detail & Related papers  (2026-05-15T05:01:09Z)
• Curvature-Guided LoRA: Steering in the pretrained NTK subspace [60.35296431630704]
  We introduce the prediction alignment problem, aiming to match the predictor obtained via PEFT to that of full fine-tuning at the level of outputs.<n>We show that this objective naturally leads to a curvature-aware, second-order formulation, where optimal low-rank updates correspond to a Newton-like, curvature-whitened gradient.<n>Based on this insight, we propose Curvature-Guided LoRA (CG-LoRA), which selects and scales adaptation directions using local curvature information.
  arXiv  Detail & Related papers  (2026-03-31T14:46:39Z)
• Perception-Aware Time-Optimal Planning for Quadrotor Waypoint Flight [25.084985306310827]
  This paper introduces a unified time-optimal trajectory optimization framework for vision-based quadrotors.<n>It incorporates perception constraints alongside full nonlinear dynamics, rotor actuation limits, aerodynamic effects, camera field-of-view constraints, and convex geometric gate representations.<n>Experiments demonstrate real-world flight speeds up to 9.8 m/s with 0.07 m average tracking error, and closed-loop success rates improved from 55% to 100%.
  arXiv  Detail & Related papers  (2026-03-04T17:22:16Z)
• Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing [74.83272587893508]
  We present a framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback.<n>In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration.<n>On real hardware, tested with different tire compounds ranging from high to low friction, we obtain a 7.60% lap time improvement without explicitly parametrizing friction.
  arXiv  Detail & Related papers  (2026-02-17T15:10:44Z)
• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• Data-driven Fuzzy Control for Time-Optimal Aggressive Trajectory Following [0.7373617024876725]
  This work presents a data-driven fuzzy controller framework that is guided by a time-optimal trajectory for multicopter tracking problems. A fuzzy controller consisting of a stabilizing controller near hover conditions and an autoregressive moving average (ARMA) controller, trained to mimic the time-optimal aggressive trajectory, is constructed using the Takagi-Sugeno fuzzy framework.
  arXiv  Detail & Related papers  (2025-04-09T00:06:15Z)
• Gaussian Process Regression for Improved Underwater Navigation [13.221163846643607]
  Doppler velocity logs (DVLs) are typically used to mitigate this drift through velocity measurements. This paper proposes a data-driven alternative based on multi-output Gaussian process regression (MOGPR) to improve DVL velocity estimation. We evaluate our proposed approach using real-world AUV data and compare it against LS and a state-of-the-art deep learning model, BeamsNet.
  arXiv  Detail & Related papers  (2025-02-23T09:13:41Z)
• Real-Time Sampling-based Online Planning for Drone Interception [18.340019191662957]
  We propose a sampling-based online planning algorithm that leverages neural network inference to replace time-consuming nonlinear trajectory optimization. The proposed method is applied to the drone interception problem, where a defense drone must intercept a target while avoiding collisions and handling imperfect target predictions.
  arXiv  Detail & Related papers  (2025-02-20T03:48:38Z)
• Understanding Optimization in Deep Learning with Central Flows [95.5647720254338]
  We develop theory that can describe the dynamics of optimization in a complex regime.<n>Our results suggest that central flows can be a valuable theoretical tool for reasoning about optimization in deep learning.
  arXiv  Detail & Related papers  (2024-10-31T17:58:13Z)
• Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios [66.05091704671503]
  We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks. We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
  arXiv  Detail & Related papers  (2024-02-04T08:41:20Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles [3.200238632208686]
  Trajectory planning aims to produce a globally optimal route for Autonomous Passenger Vehicles. Existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk.
  arXiv  Detail & Related papers  (2023-09-25T22:30:18Z)
• Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic [0.9281671380673306]
  This paper presents a parameterized bi-level optimization that jointly computes the optimal behavioural decisions and the resulting trajectory. Our approach runs in real-time using a custom GPU-accelerated batch, and a Variational Autoencoder learnt warm-start strategy. Our approach outperforms state-of-the-art model predictive control and RL approaches in terms of collision rate while being competitive in driving efficiency.
  arXiv  Detail & Related papers  (2022-12-05T12:56:42Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker [0.0]
  We develop an integrated guidance and control system that can achieve precise and safe planetary landing. The seeker tracks the designated landing site by adjusting seeker elevation and azimuth angles to center the designated landing site in the sensor field of view. The seeker angles, closing speed, and range to the designated landing site are used to formulate a velocity field that is used by the guidance and control system to achieve a safe landing.
  arXiv  Detail & Related papers  (2021-12-16T00:24:58Z)
• Time-Optimal Planning for Quadrotor Waypoint Flight [50.016821506107455]
  Planning time-optimal trajectories at the actuation limit of a quadrotor is an open problem. We propose a solution while exploiting the full quadrotor's actuator potential. We validate our method in real-world flights in one of the world's largest motion-capture systems.
  arXiv  Detail & Related papers  (2021-08-10T09:26:43Z)
• Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems [29.579737941918022]
  We learn, offline from past data, an adaptive controller with an internal parametric model of nonlinear features. We meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With a nonlinear planar rotorcraft subject to wind, we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning.
  arXiv  Detail & Related papers  (2021-03-07T23:49:59Z)
• Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
  We propose a novel optimization backbone for visual SLAM systems. We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems. Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
  arXiv  Detail & Related papers  (2020-11-02T18:02:26Z)
• Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions [77.34726150561087]
  This paper investigates the use of reinforcement learning for the robust design of interplanetary trajectories in presence of severe disturbances. An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted. The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law.
  arXiv  Detail & Related papers  (2020-08-19T15:22:15Z)
• Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop [1.7663909228482466]
  We use Reinforcement Meta-Learning to optimize an adaptive integrated guidance, navigation, and control system suitable for exoatmospheric interception of a maneuvering target. The system maps observations consisting of strapdown seeker angles and rate gyro measurements directly to thruster on-off commands. We demonstrate that the optimized policy can adapt to parasitic effects including seeker angle measurement lag, thruster control lag, the parasitic attitude loop resulting from scale factor errors and Gaussian noise on angle and rotational velocity measurements.
  arXiv  Detail & Related papers  (2020-04-18T21:20:59Z)

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.