Publications
5 results found
Wang Y, Qian Q, O'Keeffe J, et al., 2023, QuaDUE-CCM: Interpretable Distributional Reinforcement Learning using Uncertain Contraction Metrics for Precise Quadrotor Trajectory Tracking, Pages: 2306-2316
Accuracy and stability are common requirements for quadrotor trajectory tracking systems. Designing an accurate and stable tracking controller remains challenging, particularly in unknown and dynamic environments with complex aerodynamic disturbances. We propose a Quantile-approximation-based Distributional-reinforced Uncertainty Estimator (QuaDUE) to accurately identify the effects of aerodynamic disturbances, i.e., the uncertainties between the true and estimated Control Contraction Metrics (CCMs). Taking inspiration from contraction theory and integrating the QuaDUE for uncertainties, our novel CCM-based trajectory tracking framework tracks any feasible reference trajectory precisely whilst guaranteeing exponential convergence. More importantly, the convergence and training acceleration of the distributional RL are guaranteed and analyzed, respectively, from theoretical perspectives. We also demonstrate our system under unknown and diverse aerodynamic forces. Under large aerodynamic forces (>2 m/s2), compared with the classic data-driven approach, our QuaDUE-CCM achieves at least a 56.6% improvement in tracking error. Compared with QuaDRED-MPC, a distributional RL-based approach, QuaDUE-CCM achieves at least a 3 times improvement in contraction rate.
Wang Y, O'Keeffe J, Qian Q, et al., 2022, KinoJGM: A framework for efficient and accurate quadrotor trajectory generation and tracking in dynamic environments, Pages: 11036-11043, ISSN: 1050-4729
Unmapped areas and aerodynamic disturbances render autonomous navigation with quadrotors extremely challenging. To fly safely and efficiently, trajectory planners and trackers must be able to navigate unknown environments with unpredictable aerodynamic effects in real-time. When encountering aerodynamic effects such as strong winds, most current approaches to quadrotor trajectory planning and tracking will not attempt to deviate from a determined plan, even if it is risky, in the hope that any aerodynamic disturbances can be resisted by a robust controller. This paper presents a novel systematic trajectory planning and tracking framework for autonomous quadrotors. We propose a Kinodynamic Jump Space Search (Kino-JSS) to generate a safe and efficient route in unknown environments with aerodynamic disturbances. A real-time Gaussian Process is employed to model the errors caused by aerodynamic disturbances, which we then integrate with a Model Predictive Controller to achieve efficient and accurate trajectory optimization and tracking. We demonstrate our system to improve the efficiency of trajectory generation in unknown environments by up to 75% in the cases tested, compared with recent state-of-the-art. We also show that our system improves the accuracy of tracking in selected environments with unpredictable aerodynamic effects. Our implementation is available in an open source package11https://github.com/Alex-yanranwang/Imperial-KinoJGM.
Wang Y, O'Keeffe J, Qian Q, et al., 2022, Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems, Pages: 3335-3342, ISSN: 0743-1546
This paper presents a novel trajectory tracker for autonomous quadrotor navigation in dynamic and complex environments. The proposed framework integrates a distributional Reinforcement Learning (RL) estimator for unknown aerodynamic effects into a Stochastic Model Predictive Controller (SMPC) for trajectory tracking. Aerodynamic effects derived from drag forces and moment variations are difficult to model directly and accurately. Most current quadrotor tracking systems therefore treat them as simple 'disturbances' in conventional control approaches. We propose Quantile-approximation-based Distributional Reinforced-disturbance-estimator, an aerodynamic disturbance estimator, to accurately identify disturbances, i.e., uncertainties between the true and estimated values of aerodynamic effects. Simplified Affine Disturbance Feedback is employed for control parameterization to guarantee convexity, which we then integrate with a SMPC to achieve sufficient and non-conservative control signals. We demonstrate our system to improve the cumulative tracking errors by at least 66% with unknown and diverse aerodynamic forces compared with recent state-of-the-art. Concerning traditional Reinforcement Learning's non-interpretability, we provide convergence and stability guarantees of Distributional RL and SMPC, respectively, with non-zero mean disturbances.
Xiao G, Wang Y, He F, 2019, Research on safety modeling and analysis in information fusion system, Aerospace Systems, Vol: 2, Pages: 51-60, ISSN: 2523-3947
Wang Y, Xiao G, Dai Z, 2017, Integrated display and simulation for automatic dependent surveillance–broadcast and traffic collision avoidance system data fusion, Sensors, Vol: 17, Pages: 1-23, ISSN: 1424-8220
Automatic Dependent Surveillance–Broadcast (ADS-B) is the direction of airspace surveillance development. Research analyzing the benefits of Traffic Collision Avoidance System (TCAS) and ADS-B data fusion is almost absent. The paper proposes an ADS-B minimum system from ADS-B In and ADS-B Out. In ADS-B In, a fusion model with a variable sampling Variational Bayesian-Interacting Multiple Model (VSVB-IMM) algorithm is proposed for integrated display and an airspace traffic situation display is developed by using ADS-B information. ADS-B Out includes ADS-B Out transmission based on a simulator platform and an Unmanned Aerial Vehicle (UAV) platform. This paper describes the overall implementation of ADS-B minimum system, including theoretical model design, experimental simulation verification, engineering implementation, results analysis, etc. Simulation and implementation results show that the fused system has better performance than each independent subsystem and it can work well in engineering applications.
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