Aggressive Flight Research Articles

Aggressive flight control of quadrotors using incremental nonlinear dynamic inversion with a high-fidelity thrust unit model

The application of quadrotors has been expanded into aggressive tasks such as military confrontation and aerobatics shows, making it crucial to enhance flight maneuverability to adapt to rapid and large-scale command changes. Unlike hovering or low-speed modes, aggressive flight exacerbates quadrotor model uncertainty, making it challenging for controllers to balance response speed and robustness. This paper proposes an INDI control method that integrates a high-fidelity thrust model unit to address this practical need. First, based on the pole distribution theory and the application of INDI on quadrotors, it is concluded that the dynamic response of quadrotor flight is impacted by the accuracy of the thrust model. Second, consider the airflow velocity and angles, the high-fidelity modeling approach, and the aerodynamic forces and torque expression validated through wind tunnel experiments are provided. Then the flight controller for the quadrotor is designed with the established high-fidelity thrust unit model. Finally, aggressive trajectory-tracking flight tests are set to evaluate the improvement of our work. The results demonstrate that our proposed method indeed enhances the response speed and tracking accuracy, as well as the flight stability of the quadrotor.

Dynamic Event-Triggered Prescribed Performance Robust Control for Aggressive Quadrotor Flight

Aggressive flight has become increasingly important for expanding the applications of quadrotors. The typical characteristic of large and rapid changes in commands poses stringent demands on the maneuverability of quadrotors. Ensuring flight stability alone is not enough; dynamic responses must also be selectively constrained, presenting quadcopter flight control with daunting challenges. The prescribed performance control (PPC) method is seen as having the potential to solve this problem by allowing for the constrained control of specified performance, leading to extensive research. However, its practical application still faces challenges, such as the system divergence caused by errors exceeding boundaries due to sudden command mutations. This paper presents a robust dynamic event-triggered PPC (DETPPC) method for an aggressive quadrotor flight. By assessing the direction and proximity of tracking errors approaching constraint boundaries, a dynamic event-triggered compensation mechanism for performance function boundaries is established to mitigate the divergence caused by error surpassing and to preserve preset control over the targeted metrics. Controllers were designed for both the translational and rotational subsystems of the quadrotor, and stability analysis was conducted based on Lyapunov functions. Simulation tests on agile trajectory tracking and abrupt attitude control were carried out, demonstrating the effectiveness of the proposed method.

Simultaneous Planning and Execution for Quadrotors Flying Through a Narrow Gap Under Disturbance

The dynamics of a quadrotor exhibit the challenges of underactuation, strong coupling, and external disturbances. In order to handle these difficulties and enable quadrotors to align the six-degree-of-freedom (6-DOF) pose of the tilted narrow gap during the traversing maneuver, this article proposes a strategy based on simultaneous planning and execution (SPAE). In this strategy, the unknown disturbance is considered in both the planning and execution of the trajectory simultaneously. Specifically, the disturbance is estimated online during system execution and then combined with the attitude angle of the narrow gap and maximum thrust to constraint acceleration of trajectory. Given the state constraints and physical limitations in thrust, velocity, and jerk, a minimum time trajectory planning algorithm is developed based on a piecewise cubic function to realize aggressive flight. In this algorithm, the coupled maximum thrust is decoupled into three independent axes. Each axis can then generate time-efficient third-order trajectories that satisfy arbitrary state constraints and dynamic feasibility. The average computing time of the algorithm is less than 150 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> s on the STM32 embedded platform, which is suitable for quadrotors with limited onboard computing capability. Finally, simulations and outdoor experiments were conducted to verify the effectiveness of the proposed system strategy. The outdoor results demonstrate that the proposed strategy can enable the quadrotor to carry out aggressive flying through a narrow gap in a natural disturbance environment.

Adaptive trajectory tracking of UAV with a cable-suspended load using vision-inertial-based estimation

Unmanned aerial vehicle (UAV) carrying cable-suspended load is a basic method for load transportation. Tracking a given trajectory without the global positioning system or a prior environmental map with limited computation power and limited sensory is a crucial problem. In this paper, a novel paradigm/structure combining an adaptive state estimator and a trajectory tracking controller for the unmanned aerial vehicle with a cable-suspended load system is designed. An advantage of this work is the adaptive method to estimate the system state and environmental feature positions at the same time. The asymptotic convergence of our system is theoretically proved by the Lyapunov theory. The visual–inertial estimation algorithm we propose has lower computational consumption than the optimization-based methods and provides high-precision, low latency state estimation for the system executing aggressive flight. Several simulations are designed and carried out within an ODE-based simulator to verify the proposed method. Comparisons with other visual–inertial-based algorithms are also carried out in the simulator. Simulation results demonstrate the proposed algorithm’s convergence, robustness, and efficiency.

A numerically-stable trajectory generation and optimization algorithm for autonomous quadrotor UAVs

This paper introduces a novel trajectory generation and optimization algorithm (TGO) that enables agile and aggressive flight of quadrotor UAVs while considering various constraints associated with robot dynamics, actuator inputs, and flight environment. The TGO algorithm employs time-parametrized polynomial trajectories based on a predetermined sequence of waypoints to produce dynamically feasible and collision-free trajectories. This approach extends previous work that utilized differential flatness property and polynomial based trajectories by eliminating the need for iterative searching and computationally intensive sampling. One of the significant advantages of the TGO algorithm is its numerical stability for large number of waypoints and high-order polynomials. To address the ill-conditioned problem of Quadratic Programming (QP) based methods, the TGO algorithm reformulates the trajectory generation and optimization problem into an unconstrained quadratic programming (UCP) using the numerically stable null-space factorization method. The TGO algorithm produces minimum derivative trajectories and minimum waypoints arrival times, generating a wide range of aggressive trajectories that can leverage the full maneuvering capabilities of quadrotor robots. The proposed algorithm’s numerical stability and computational advantages are demonstrated through various scenarios and comparisons. An animated simulation of the TGO algorithm is available at: https://youtu.be/MvvhBG14iIg.

A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

A robust controller for the waypoint tracking of a quadrotor unmanned aerial vehicle (UAV) is proposed in this paper, in which position control and attitude control are effectively decoupled. Model predictive control (MPC) is employed in the position controller. The constraints of motors are imposed on the state and input variables of the optimization equation. This design effectively mitigates the nonlinearity of the attitude loop and enhances the planning efficiency of the position controller. The attitude controller is designed using a nonlinear and robust control law based on SO(3) space, which enables continuous control on the SO(3) manifold. By extending the differential flatness of the quadrotor-UAV to the angular acceleration level, the mapping of the control reference from the position controller to the attitude controller is achieved. Simulations are carried out to demonstrate the capability of the proposed controller. In the simulations, multiple aggressive flight trajectories and severe external disturbances are designed. The results show that the controller is robust, with superior accuracy in tracking aggressive trajectories.

Perception-Aware Image-Based Visual Servoing of Aggressive Quadrotor UAVs

The maintenance of visual features within the sensor field of view poses a significant challenge for underactuated aerial vehicles such as quadrotors, especially during aggressive maneuvers. However, the existing image-based visual servo (IBVS) control methods rely on strict target visibility assumptions or impose excessive constraints on the quadrotor's agility to meet this requirement. Furthermore, the effectiveness of the visibility constraint defined in prior works remains unverified in aggressive flight tests. To address these issues, we present a robust IBVS scheme for quadrotors to perform aggressive maneuvers while ensuring target visibility. Based on the nonlinear model-predictive control framework, we propose a novel underactuation compensation scheme to eliminate the need for a virtual camera frame, which enables us to formulate the true target visibility constraint. We then introduce a quaternion-based representation of spherical visual features to handle the nonsmooth vector field problem on the 2-sphere and derive its corresponding image kinematics. We validate our method through three challenging visual servo tasks where agile maneuvers are desired: fast landing, aggressive long-distance flight, and dynamic object tracking. Extensive simulation and experiment show that our method consistently achieves a target-visible rate of 100% in all the image frames, even under a maximum pitch of 21.04 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> . The results validate the effectiveness of our visibility constraint under large robot rotations and underscore its importance in enabling robust and aggressive flights.

Full Attitude Control of a Quadrotor Using Fast Nonsingular Terminal Sliding Mode With Angular Velocity Planning

In this article, a fast nonsingular terminal sliding mode combined with angular velocity planning (FNTSM-AVP) controller is developed to achieve high-speed, accurate, and robust attitude tracking performance for a quadrotor. The proposed controller employs a sliding surface function with a continuous arctangent function, which can effectively eliminate chattering behavior on the quadrotor while retaining the speed and accuracy of attitude tracking. Unlike conventional methods, the proposed controller preferentially recovers the roll and pitch angles at desired attitude by planning the shortest arrival path, which is effective to improve the tracking performance. Finally, the finite time convergence and zero tracking error properties of the closed-loop control system under the proposed FNTSM-AVP are analyzed and proved. The aggressive flight experiments demonstrate the advantage of faster convergence and fewer chattering behavior of the proposed method compared to the existing FNTSM controller.

Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement Learning

The ability to perform aggressive movements,which are called aggressive flights, is important for quadrotors during navigation. However, aggressive quadrotor flights are still a great challenge to practical applications. The existing solutions to aggressive flights heavily rely on a predefined trajectory, which is a time-consuming preprocessing step. To avoid such path planning, we propose a curiosity-driven reinforcement learning method for aggressive flight missions and a similarity-based curiosity module is introduced to speed up the training procedure. A branch structure exploration strategy is also applied to guarantee the robustness of the policy and to ensure the policy trained in simulations can be performed in real-world experiments directly. The experimental results in simulations demonstrate that our reinforcement learning algorithm performs well in aggressive flight tasks, speeds up the convergence process and improves the robustness of the policy. Besides, our algorithm shows a satisfactory simulated to real transferability and performs well in real-world experiments.

An Adaptive Control Framework for the Autonomous Aerobatic Maneuvers of Fixed-Wing Unmanned Aerial Vehicle

This article proposes an adaptive flight framework that integrates a discrete-time incremental nonlinear dynamic inversion controller and a neural network (NN)-based observer for maneuvering flight. The framework is built on the feedback-inversion scheme in which the adaptive neural network augments a discrete-time disturbance observer in the loop. The effects of the modeling uncertainties and the exogenous perturbations are both taken into consideration and are alleviated by the observer. By utilizing the Lyapunov synthesis method, the updating rule of the NN’s weights is introduced, which guarantees the system’s stability with enhanced tracking performance. The efficiency of the proposed scheme is presented through numerical verification of a 6-DOF fixed-wing fighter performing several aggressive flight maneuvers. Extensive simulation results illustrate that this versatile controller is more practical for aerobatic flights compared with the discontinuous sliding mode (DSM) and the nonlinear dynamic inversion (NDI) methods. Given well-generated maneuver commands, the aircraft can accurately track the aggressive reference in the presence of modeling perturbations such as changes in aerodynamic coefficient, inertial parameters, and wind gusts.

Perception-Aware Receding Horizon Trajectory Planning for Multicopters With Visual-Inertial Odometry

Visual inertial odometry (VIO) is widely used for the state estimation of multicopters, but it may function poorly in environments with few visual features or in overly aggressive flights. In this work, we propose a perception-aware collision avoidance trajectory planner for multicopters, that may be used with any feature-based VIO algorithm. Our approach is able to fly the vehicle to a goal position at high speed, avoiding obstacles in an unknown stationary environment while achieving good VIO state estimation accuracy. The proposed planner samples a group of minimum jerk trajectories and finds collision-free trajectories among them, which are then evaluated based on their speed to the goal and perception quality. Both the motion blur of features and their locations are considered for the perception quality. Our novel consideration of the motion blur of features enables automatic adaptation of the trajectory&#x2019;s aggressiveness under environments with different light levels. The best trajectory from the evaluation is tracked by the vehicle and is updated in a receding horizon manner when new images are received from the camera. Only generic assumptions about the VIO are made, so that the planner may be used with various existing systems. The proposed method can run in real time on a small embedded computer on board. We validated the effectiveness of our proposed approach through experiments in both indoor and outdoor environments. Compared to a perception-agnostic planner, the proposed planner kept more features in the camera&#x2019;s view and made the flight less aggressive, making the VIO more accurate. It also reduced VIO failures, which occurred for the perception-agnostic planner but not for the proposed planner. The ability of the proposed planner to fly through dense obstacles was also validated. The experiment video can be found at https://youtu.be/qO3LZIrpwtQ.

RAPTOR: Robust and Perception-Aware Trajectory Replanning for Quadrotor Fast Flight

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 article, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce two perception-aware planning approaches to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We release our implementation as an open-source package <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for the community.

Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight

Quadrotors can achieve aggressive flight by tracking complex maneuvers and rapidly changing directions. Planning for aggressive flight with trajectory optimization could be incredibly fast, even in higher dimensions, and can account for dynamics of the quadrotor, however, only provides a locally optimal solution. On the other hand, planning with discrete graph search can handle non-convex spaces to guarantee optimality but suffers from exponential complexity with the dimension of search. We introduce a framework for aggressive quadrotor trajectory generation with global reasoning capabilities that combines the best of trajectory optimization and discrete graph search. Specifically, we develop a novel algorithmic framework that interleaves these two methods to complement each other and generate trajectories with provable guarantees on completeness up to discretization. We demonstrate and quantitatively analyze the performance of our algorithm in challenging simulation environments with narrow gaps that create severe attitude constraints and push the dynamic capabilities of the quadrotor. Experiments show the benefits of the proposed algorithmic framework over standalone trajectory optimization and graph search-based planning techniques for aggressive quadrotor flight.

A distributed approach for lidar-based relative state estimation of multi-UAV in GPS-denied environments

In this paper, we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate, real-time trajectory estimation of multiple Unmanned Aerial Vehicles (UAVs) in GPS-denied environments. The system builds atop a factor graph, and only on-board sensors and computing power are utilized. Benefiting from the keyframe strategy, each UAV performs relative state estimation individually and broadcasts very partial information without exchanging raw data. The complete system runs in real-time and is evaluated with three experiments in different environments. Experimental results show that the proposed distributed approach offers comparable performance with a centralized method in terms of accuracy and real-time performance. The flight test demonstrates that the proposed relative state estimation framework is able to be used for aggressive flights over 5 m/s.

TGK-Planner: An Efficient Topology Guided Kinodynamic Planner for Autonomous Quadrotors

In this letter, 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 improve the efficiency of finding a safe and dynamically feasible trajectory. Then, we refine the smoothness and continuity of the trajectory in an optimization framework, which incorporates the homotopy constraint to guarantee the safety of the trajectory. The optimization program is formulated as a sequence of quadratic programmings (QPs) and can be iteratively solved in a few milliseconds. Finally, the proposed system is integrated into a fully autonomous quadrotor and validated in various simulated and real-world scenarios. Benchmark comparisons show that our method outperforms state-of-the-art methods with regard to efficiency and trajectory quality. Moreover, we will release our code as an open-source package.

Observability-Constrained VINS for MAVs Using Interacting Multiple Model Algorithm

This article presents the design of an interacting multiple model (IMM) filter for visual-inertial navigation (VIN) of MAVs. VIN of MAVs in practice typically uses a single system model for its state estimator design. However, microaerial vehicles (MAVs) can operate in different scenarios such as aggressive flights, hovering flights, and under high external disturbance requiring changing constraints imposed on the estimator model. This article proposes the use of a conventional VIN and a drag force VIN in an error-state IMM filtering framework to address the need for multiple models in the estimator. The work uses an epipolar geometry constraint for the design of the measurement model for both filters to realize computationally efficient state updates. Observability of the proposed modifications to VIN filters (drag force model and epipolar measurement model) are analyzed, and observability-based consistency rules are derived for the two filters of the IMM. Monte Carlo numerical simulations validate the performance of the observability constrained IMM, which improved the accuracy and consistency of the visual-inertial navigation system (VINS) for changing flight conditions and external wind disturbance scenarios. Experimental validation is performed using the EuRoC dataset to evaluate the performance of the proposed IMM filter design. The results show that the IMM outperforms stand-alone filters used in the IMM filtering bank by switching between the filters based on the residual likelihood of the models.

Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments

In this article, we propose a complete and robust system for the aggressive flight of autonomous quadrotors. The proposed system is built upon on the classical teach-and-repeat framework, which is widely adopted in infrastructure inspection, aerial transportation, and search-and-rescue. For these applications, a human's intention is essential for deciding the topological structure of the flight trajectory of the drone. However, poor teaching trajectories and changing environments prevent a simple teach-and-repeat system from being applied flexibly and robustly. In this article, instead of commanding the drone to precisely follow a teaching trajectory, we propose a method to automatically convert a human-piloted trajectory, which can be arbitrarily jerky, to a topologically equivalent one. The generated trajectory is guaranteed to be smooth, safe, and dynamically feasible, with a human preferable aggressiveness. Also, to avoid unmapped or moving obstacles during flights, a fast local perception method and a sliding-windowed replanning method are integrated into our system, to generate safe and dynamically feasible local trajectories onboard. We name our system as teach-repeat-replan. It can capture users' intention of a flight mission, convert an arbitrarily jerky teaching path to a smooth repeating trajectory, and generate safe local replans to avoid unexpected collisions. The proposed planning system is integrated into a complete autonomous quadrotor with global and local perception and localization submodules. Our system is validated by performing aggressive flights in challenging indoor/outdoor environments. We release all components in our quadrotor system as open-source ros packages.

Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight

With much research has been conducted into trajectory planning for quadrotors, planning with spatial and temporal optimal trajectories in real-time is still challenging. In this paper, we propose a framework for generating large-scale piecewise polynomial trajectories for aggressive autonomous flights, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the planning problem, we conduct alternating minimization between boundary conditions and time durations of trajectory pieces. In each minimization phase, we leverage the algebraic convenience of the sub-problem to escape poor local minima and achieve the lowest time consumption. Theoretical analysis for the global/local convergence rate of our proposed method is provided. Moreover, based on polynomial theory, an extremely fast feasibility check method is designed for various kinds of constraints. By incorporating the method into our alternating structure, a constrained minimization algorithm is constructed to optimize trajectories on the premise of feasibility. Benchmark evaluation shows that our algorithm outperforms state-of-the-art methods regarding efficiency, optimality, and scalability. Aggressive flight experiments in a limited space with dense obstacles are presented to demonstrate the performance of the proposed algorithm. We release our implementation as an open-source ros-package.

Robust trajectory tracking control for unmanned tail‐sitters in aggressive flight mode transitions

SummaryIn this paper, the trajectory tracking control problem is investigated for a new typical tail‐sitter. A robust hierarchical control method is proposed to achieve aggressive flight mode transitions. The proposed control method results in a composite controller including a translational controller and a rotational controller to control the position and attitude respectively. Continuous aggressive flight mode transitions can be achieved without switching on the coordinate systems or the controller structures. It is proven that the tracking errors of the designed closed‐loop system can converge into a given neighborhood of the origin in a finite time. Simulation results show the effectiveness of the proposed control strategy.

Optimal Trajectory Generation for Quadrotor Teach-and-Repeat

In this letter, we propose a novel motion planning framework for quadrotor teach-and-repeat applications. Instead of controlling the drone to precisely follow the teaching path, our method converts an arbitrary jerky human-piloted trajectory to a topologically equivalent one, which is guaranteed to be safe, smooth, and kinodynamically feasible with an expected aggressiveness. Our proposed planning framework optimizes the trajectory in both spatial and temporal aspects. In the spatial layer, a flight corridor is found to represent the free space that is topologically equivalent with the teaching path. Then, a minimum-jerk piecewise trajectory is generated within the flight corridor. In the temporal layer, the trajectory is re-parameterized to obtain a minimum-time temporal trajectory under kinodynamic constraints. The spatial and temporal optimizations are both formulated as convex programs and are done iteratively. The proposed method is integrated into a complete quadrotor system and is validated to perform aggressive flights in challenging indoor and outdoor environments.