93 research outputs found
Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness
Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e.,
high-speed and high-acceleration) maneuvers have attracted significant
attention in the past few years. This paper focuses on accurate tracking of
aggressive quadcopter trajectories. We propose a novel control law for tracking
of position and yaw angle and their derivatives of up to fourth order,
specifically, velocity, acceleration, jerk, and snap along with yaw rate and
yaw acceleration. Jerk and snap are tracked using feedforward inputs for
angular rate and angular acceleration based on the differential flatness of the
quadcopter dynamics. Snap tracking requires direct control of body torque,
which we achieve using closed-loop motor speed control based on measurements
from optical encoders attached to the motors. The controller utilizes
incremental nonlinear dynamic inversion (INDI) for robust tracking of linear
and angular accelerations despite external disturbances, such as aerodynamic
drag forces. Hence, prior modeling of aerodynamic effects is not required. We
rigorously analyze the proposed control law through response analysis, and we
demonstrate it in experiments. The controller enables a quadcopter UAV to track
complex 3D trajectories, reaching speeds up to 12.9 m/s and accelerations up to
2.1g, while keeping the root-mean-square tracking error down to 6.6 cm, in a
flight volume that is roughly 18 m by 7 m and 3 m tall. We also demonstrate the
robustness of the controller by attaching a drag plate to the UAV in flight
tests and by pulling on the UAV with a rope during hover.Comment: To be published in IEEE Transactions on Control Systems Technology.
Revision: new set of experiments at increased speed (up to 12.9 m/s), updated
controller design using quaternion representation, new video available at
https://youtu.be/K15lNBAKDC
Grasping, Perching, And Visual Servoing For Micro Aerial Vehicles
Micro Aerial Vehicles (MAVs) have seen a dramatic growth in the consumer market because of their ability to provide new vantage points for aerial photography and videography. However, there is little consideration for physical interaction with the environment surrounding them. Onboard manipulators are absent, and onboard perception, if existent, is used to avoid obstacles and maintain a minimum distance from them. There are many applications, however, which would benefit greatly from aerial manipulation or flight in close proximity to structures. This work is focused on facilitating these types of close interactions between quadrotors and surrounding objects. We first explore high-speed grasping, enabling a quadrotor to quickly grasp an object while moving at a high relative velocity. Next, we discuss planning and control strategies, empowering a quadrotor to perch on vertical surfaces using a downward-facing gripper. Then, we demonstrate that such interactions can be achieved using only onboard sensors by incorporating vision-based control and vision-based planning. In particular, we show how a quadrotor can use a single camera and an Inertial Measurement Unit (IMU) to perch on a cylinder. Finally, we generalize our approach to consider objects in motion, and we present relative pose estimation and planning, enabling tracking of a moving sphere using only an onboard camera and IMU
Multi-Layered Optimal Navigation System For Quadrotors UAV
Purpose
This paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV).
Design/methodology/approach
The proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS).
Findings
All the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system.
Practical implications
The proposed controllers are easily implementable on-board and are computationally efficient.
Originality/value
The originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly
Advanced UAVs Nonlinear Control Systems and Applications
Recent development of different control systems for UAVs has caught the attention of academic and industry, due to the wide range of their applications such as in surveillance, delivery, work assistant, and photography. In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB®/Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. Moreover, the practical results coincided with the results of the simulation results
Opportunities and Challenges with Autonomous Micro Aerial Vehicles, The Int.
Abstract We survey the recent work on micro-UAVs, a fast-growing field in robotics, outlining the opportunities for research and applications, along with the scientific and technological challenges. Micro-UAVs can operate in three-dimensional environments, explore and map multi-story buildings, manipulate and transport objects, and even perform such tasks as assembly. While fixed-base industrial robots were the main focus in the first two decades of robotics, and mobile robots enabled most of the significant advances during the next two decades, it is likely that UAVs, and particularly micro-UAVs will provide a major impetus for the third phase of development
Path Following by a Quadrotor Using Virtual Target Pursuit Guidance
Quadrotors, being more agile than fixed-wing vehicles, are the ideal candidates for autonomous missions in small, compact spaces. The immense challenge to navigate such environments is fulfilled by the concept of path following. Path following is the method of tracking/tracing a fixed, pre-defined path with minimum position error while exerting the lowest possible control effort.
In this work, the missile guidance technique of pure pursuit is adopted and modified for a 3D quadrotor model to follow fixed, compact trajectories. A specialized hardware testing platform is developed to test this algorithm. The results obtained from simulation and flight tests are compared to results from another technique called differential flatness. A small part of this thesis also deals with the stability analysis of the modified 3D pure pursuit algorithm to track trajectories expending lower control effort
Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing
In drone racing, the time-minimum trajectory is affected by the drone's
capabilities, the layout of the race track, and the configurations of the gates
(e.g., their shapes and sizes). However, previous studies neglect the
configuration of the gates, simply rendering drone racing a waypoint-passing
task. This formulation often leads to a conservative choice of paths through
the gates, as the spatial potential of the gates is not fully utilized. To
address this issue, we present a time-optimal planner that can faithfully model
gate constraints with various configurations and thereby generate a more
time-efficient trajectory while considering the single-rotor-thrust limits. Our
approach excels in computational efficiency which only takes a few seconds to
compute the full state and control trajectories of the drone through tracks
with dozens of different gates. Extensive simulations and experiments confirm
the effectiveness of the proposed methodology, showing that the lap time can be
further reduced by taking into account the gate's configuration. We validate
our planner in real-world flights and demonstrate super-extreme flight
trajectory through race tracks
- …