Enforcing safety on precise trajectory tracking is critical for aerial robotics subject to wind disturbances. In this paper, we present a learning-based safety-preserving cascaded quadratic programming control (SPQC) for safe trajectory tracking under wind disturbances. The SPQC controller consists of a position-level controller and an attitude-level controller. Gaussian Processes (GPs) are utilized to estimate the uncertainties caused by wind disturbances, and then a nominal Lyapunov-based cascaded quadratic program (QP) controller is designed to track the reference trajectory. To avoid unexpected obstacles when tracking, safety constraints represented by control barrier functions (CBFs) are enforced on each nominal QP controller in a way of minimal modification. The performance of the proposed SPQC controller is illustrated through numerical validations of (a) trajectory tracking under different wind disturbances, and (b) trajectory tracking in a cluttered environment with a dense time-varying obstacle field under wind disturbances.
King Fahd University of Petroleum and Minerals（法赫德石油和矿产大学）
In this work, the autonomous control of a quadrotor-manipulator unmanned aerial vehicle is treated using an extended dynamic model. Due to persistent aerodynamic disturbances and dynamic couplings, the control of a quadrotor-manipulator unit is cumbersome. Active disturbance rejection control (ADRC) is proposed for the cancellation of the undesirable dynamics while a PD controller is developed for set-point tracking. Parameters were tuned using Gradient Descent-Sequential Quadratic Programming in Simulink design optimization
Design and Control of an Actively Morphing Quadrotor with Vertically Foldable Arms
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University and State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis（自动化与智能感知学院，上海交通大学；航空集成与航空系统-系统综合国家重点实验室）
In this paper, we tackle the problem of flying a quadrotor using time-optimal control policies that can be replanned online when the environment changes or when encountering unknown disturbances. This problem is challenging as the time-optimal trajectories that consider the full quadrotor dynamics are computationally expensive to generate (order of minutes or even hours). We introduce a sampling-based method for efficient generation of time-optimal paths of a point-mass model. These paths are then tracked using a Model Predictive Contouring Control approach that considers the full quadrotor dy…
University of Michigan（密歇根大学）；University of Arizona（亚利桑那大学）
This paper develops and experimentally evaluates a navigation function for quadrotor formation flight that is resilient to abrupt quadrotor failures and other obstacles. The navigation function is based on modeling healthy quadrotors as particles in an ideal fluid flow. We provide three key contributions: (i) A Containment Exclusion Mode (CEM) safety theorem and proof which guarantees safety and formally specifies a minimum safe distance between quadrotors in formation, (ii) A real-time, computationally efficient CEM navigation algorithm, (iii) Simulation and experimental algorithm validation.…
This study has developed a fault-tolerant controller that is able to recover a quadrotor from arbitrary initial orientations and angular velocities, despite the complete failure of a rotor. This cascaded control method includes a position/altitude controller, an almost-global convergence attitude controller, and a control allocation method based on quadratic programming. As a major novelty, a constraint of undesirable angular velocity is derived and fused into the control allocator, which significantly improves the recovery performance. For validation, we have conducted a set of Monte-Carlo si…
DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models
Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）；Department of Cognitive Robotics, Delft University of Technology（代尔夫特理工大学认知机器人系）；Department of Computer Science, Aalto University（艾尔沃斯大学计算机科学系）
Ability to recover from faults and continue mission is desirable for many quadrotor applications. The quadrotor's rotor may fail while performing a mission and it is essential to develop recovery strategies so that the vehicle is not damaged. In this paper, we develop a model-free deep reinforcement learning approach for a quadrotor to recover from a single rotor failure. The approach is based on Soft-actor-critic that enables the vehicle to hover, land, and perform complex maneuvers. Simulation results are presented to validate the proposed approach using a custom simulator. The results show …
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. We use reinforcement learning to train policies in simulation that transfer remarkably well to multiple different physical quadrotors. Our policies are low-level, i.e., we map the rotorcrafts' state directly to the motor outputs. The trained control policies are very robust to external disturbances and can withstand harsh initial conditions such as throws. We show how different training methodologies (change of the cost function, modeling of noise, use of domain randomization) might affect flight…
ETH Zurich（苏黎世联邦理工学院）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem…
As the market for commercially available unmanned aerial vehicles (UAVs) booms, there is an increasing number of small, teleoperated or autonomous aircraft found in protected or sensitive airspace. Existing solutions for removal of these aircraft are either military-grade and too disruptive for domestic use, or compose of cumbersomely teleoperated counter-UAV vehicles that have proven ineffective in high-profile domestic cases. In this work, we examine the use of a quadrotor for autonomously targeting semi-stationary and moving aerial objects with little or no prior knowledge of the target's f…
Zhejiang University（浙江大学）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）；National Engineering Research Center for Industrial Automation (Ningbo Institute)（工业自动化国家工程研究中心（宁波研究院））
The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectiv…
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）
Purpose: Real-life applications using quadrotors introduce a number of disturbances and time-varying properties that pose a challenge to flight controllers. We observed that, when a quadrotor is tasked with picking up and dropping a payload, traditional PID and RL-based controllers found in literature struggle to maintain flight after the vehicle changes its dynamics due to interaction with this external object. Methods: In this work, we introduce domain randomization during the training phase of a low-level waypoint guidance controller based on Soft Actor-Critic. The resulting controller is e…
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；LAAS-CNRS（法国国家科学研究中心图卢兹自动化与系统分析实验室）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）
In this paper, we prove that the dynamical model of a quadrotor subject to linear rotor drag effects is differentially flat in its position and heading. We use this property to compute feed-forward control terms directly from a reference trajectory to be tracked. The obtained feed-forward terms are then used in a cascaded, nonlinear feedback control law that enables accurate agile flight with quadrotors. Compared to state-of-the-art control methods, which treat the rotor drag as an unknown disturbance, our method reduces the trajectory tracking error significantly. Finally, we present a method…
Quadrotors are one of the popular unmanned aerial vehicles (UAVs) due to their versatility and simple design. However, the tuning of gains for quadrotor flight controllers can be laborious, and accurately stable control of trajectories can be difficult to maintain under exogenous disturbances and uncertain system parameters. This paper introduces a novel robust and adaptive control synthesis methodology for a quadrotor robot's attitude and altitude stabilization. The developed method is based on the fuzzy reinforcement learning and Strictly Negative Imaginary (SNI) property. The first stage of…
Autonomous UAV Pipeline Near-proximity Inspection via Disturbance-Aware Predictive Visual Servoing
School of Automation, Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University（自动化学院、CSE测量与控制重点实验室、教育部、东南大学）；Department of Aeronautical and Automotive Engineering, Loughborough University（航空与汽车工程系、罗汉布大学）；Research Centre for Low Altitude Economy and the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（低空经济研究中心和航空与航空工程系、香港理工大学）
University of Zurich（苏黎世大学）；Delft University of Technology（代尔夫特理工大学）
Fault-tolerant control is crucial for safety-critical systems, such as quadrotors. State-of-art flight controllers can stabilize and control a quadrotor even when subjected to the complete loss of a rotor. However, these methods rely on external sensors, such as GPS or motion capture systems, for state estimation. To the best of our knowledge, this has not yet been achieved with only onboard sensors. In this paper, we propose the first algorithm that combines fault-tolerant control and onboard vision-based state estimation to achieve position control of a quadrotor subjected to complete failur…
A Sliced Learning Framework for Online Disturbance Identification in Quadrotor SO(3) Attitude Control
Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Japan（国家先进工业科学与技术研究院智能系统研究所，日本）；Graduate School of Systems and Information Engineering, University of Tsukuba, Japan（茨大大学系统与信息工程研究生院，日本）
In this paper, we propose a lightweight yet effective Topology Guided Kinodynamic planner (TGK-Planner) for quadrotor aggressive flights with limited onboard computing resources. The proposed system follows the traditional hierarchical planning workflow, with novel designs to improve the robustness and efficiency in both the pathfinding and trajectory optimization sub-modules. Firstly, we propose the topology guided graph, which roughly captures the topological structure of the environment and guides the state sampling of a sampling-based kinodynamic planner. In this way, we significantly impr…
Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）
Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this paper, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight. A path-guided optimization (PGO) approach that incorporates multiple topolog…
Shape-Adaptive Planning and Control for a Deformable Quadrotor
State Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术状态重点实验室）；Huzhou Institute, Zhejiang University（浙江大学湖州市院）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）
International Institute of Information Technology Hyderabad（国际信息技术学院海得拉巴分校）
With the advent of intelligent transport, quadrotors are becoming an attractive solution while lifting or dropping payloads during emergency evacuations, construction works, etc. During such operations, dynamic variations in (possibly unknown) payload cause considerable changes in the system dynamics. However, a systematic control solution to tackle such varying dynamical behaviour is still missing. In this work, two control solutions are proposed to solve two specific problems in aerial transportation of payload, as mentioned below. In the first work, we explore the tracking control problem f…
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 n…
We tackle the problem of flying time-optimal trajectories through multiple waypoints with quadrotors. State-of-the-art solutions split the problem into a planning task - where a global, time-optimal trajectory is generated - and a control task - where this trajectory is accurately tracked. However, at the current state, generating a time-optimal trajectory that considers the full quadrotor model requires solving a difficult time allocation problem via optimization, which is computationally demanding (in the order of minutes or even hours). This is detrimental for replanning in presence of dist…
