A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters

• URL: http://arxiv.org/abs/2011.09335v3
• Date: Mon, 2 Aug 2021 03:35:12 GMT
• Title: A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters
• Authors: Sim Kuan Goh, Narendra Pratap Singh, Zhi Jun Lim and Sameer Alam
• Abstract summary: Approach and landing accidents have resulted in a significant number of hull losses worldwide. We propose a data-driven method to learn and interpret flight's approach and landing parameters.
• Score: 1.0323063834827413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Approach and landing accidents have resulted in a significant number of hull losses worldwide. Technologies (e.g., instrument landing system) and procedures (e.g., stabilized approach criteria) have been developed to reduce the risks. In this paper, we propose a data-driven method to learn and interpret flight's approach and landing parameters to facilitate comprehensible and actionable insights into flight dynamics. Specifically, we develop two variants of tunnel Gaussian process (TGP) models to elucidate aircraft's approach and landing dynamics using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight. TGP hybridizes the strengths of sparse variational Gaussian process and polar Gaussian process to learn from a large amount of data in cylindrical coordinates. We examine TGP qualitatively and quantitatively by synthesizing three complex trajectory datasets and compared TGP against existing methods on trajectory learning. Empirically, TGP demonstrates superior modeling performance. When applied to operational A-SMGCS data, TGP provides the generative probabilistic description of landing dynamics and interpretable tunnel views of approach and landing parameters. These probabilistic tunnel models can facilitate the analysis of procedure adherence and augment existing aircrew and air traffic controllers' displays during the approach and landing procedures, enabling necessary corrective actions.

Related papers

• Context-Aware Generative Models for Prediction of Aircraft Ground Tracks [0.004807514276707785]
  Trajectory prediction plays an important role in supporting the decision-making of Air Traffic Controllers. Traditional TP methods are deterministic and physics-based, with parameters calibrated using aircraft surveillance data harvested across the world. This work proposes a generative method for lateral TP, using probabilistic machine learning to model the effect of the unknown effect of pilot behaviour and ATCO intentions.
  arXiv  Detail & Related papers  (2023-09-26T14:20:09Z)
• Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures [52.25258289718559]
  We propose a probabilistic model that can learn the variability from procedural data and flight tracks collected from radar surveillance data. We show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft.
  arXiv  Detail & Related papers  (2023-03-17T13:58:06Z)
• 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)
• A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data [0.0]
  Snow and ice on runway surfaces reduces tire-pavement friction needed for retardation and directional control. XGBoost is used to create a combined runway assessment system.
  arXiv  Detail & Related papers  (2021-07-01T11:01:13Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)
• 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)
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [81.7983463275447]
  Learning-based control algorithms require data collection with abundant supervision for training. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
  arXiv  Detail & Related papers  (2020-05-09T05:57:43Z)
• Planning from Images with Deep Latent Gaussian Process Dynamics [2.924868086534434]
  Planning is a powerful approach to control problems with known environment dynamics. In unknown environments the agent needs to learn a model of the system dynamics to make planning applicable. We propose to learn a deep latent Gaussian process dynamics (DLGPD) model that learns low-dimensional system dynamics from environment interactions with visual observations.
  arXiv  Detail & Related papers  (2020-05-07T21:29:45Z)
• Localized active learning of Gaussian process state space models [63.97366815968177]
  A globally accurate model is not required to achieve good performance in many common control applications. We propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy.
  arXiv  Detail & Related papers  (2020-05-04T05:35:02Z)
• Online Parameter Estimation for Safety-Critical Systems with Gaussian Processes [6.122161391301866]
  We present a Bayesian optimization framework based on Gaussian processes (GPs) for online parameter estimation. It uses an efficient search strategy over a response surface in the parameter space for finding the global optima with minimal function evaluations. We demonstrate our technique on an actuated planar pendulum and safety-critical quadrotor in simulation with changing parameters.
  arXiv  Detail & Related papers  (2020-02-18T20:38:00Z)

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.