82 research outputs found
AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation
The robotics community is increasingly interested in autonomous aerial
transportation. Unmanned aerial vehicles with suspended payloads have
advantages over other systems, including mechanical simplicity and agility, but
pose great challenges in planning and control. To realize fully autonomous
aerial transportation, this paper presents a systematic solution to address
these difficulties. First, we present a real-time planning method that
generates smooth trajectories considering the time-varying shape and non-linear
dynamics of the system, ensuring whole-body safety and dynamic feasibility.
Additionally, an adaptive NMPC with a hierarchical disturbance compensation
strategy is designed to overcome unknown external perturbations and inaccurate
model parameters. Extensive experiments show that our method is capable of
generating high-quality trajectories online, even in highly constrained
environments, and tracking aggressive flight trajectories accurately, even
under significant uncertainty. We plan to release our code to benefit the
community.Comment: Accepted by IEEE Robotics and Automation Letter
Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision
We address one of the main challenges towards autonomous quadrotor flight in
complex environments, which is flight through narrow gaps. While previous works
relied on off-board localization systems or on accurate prior knowledge of the
gap position and orientation, we rely solely on onboard sensing and computing
and estimate the full state by fusing gap detection from a single onboard
camera with an IMU. This problem is challenging for two reasons: (i) the
quadrotor pose uncertainty with respect to the gap increases quadratically with
the distance from the gap; (ii) the quadrotor has to actively control its
orientation towards the gap to enable state estimation (i.e., active vision).
We solve this problem by generating a trajectory that considers geometric,
dynamic, and perception constraints: during the approach maneuver, the
quadrotor always faces the gap to allow state estimation, while respecting the
vehicle dynamics; during the traverse through the gap, the distance of the
quadrotor to the edges of the gap is maximized. Furthermore, we replan the
trajectory during its execution to cope with the varying uncertainty of the
state estimate. We successfully evaluate and demonstrate the proposed approach
in many real experiments. To the best of our knowledge, this is the first work
that addresses and achieves autonomous, aggressive flight through narrow gaps
using only onboard sensing and computing and without prior knowledge of the
pose of the gap
A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor
In this article, a control strategy approach is proposed for a system consisting of a quadrotor transporting a double pendulum. In our case, we attempt to achieve a swing free transportation of the pendulum, while the quadrotor closely follows a specific trajectory. This dynamic system is highly nonlinear, therefore, the fulfillment of this complex task represents a demanding challenge. Moreover, achieving dampening of the double pendulum oscillations while following a precise trajectory are conflicting goals. We apply a proportional derivative (PD) and a model predictive control (MPC) controllers for this task. Transportation of a multiple pendulum with an aerial robot is a step forward in the state of art towards the study of the transportation of loads with complex dynamics. We provide the modeling of the quadrotor and the double pendulum. For MPC we define the cost function that has to be minimized to achieve optimal control. We report encouraging positive results on a simulated environmentcomparing the performance of our MPC-PD control circuit against a PD-PD configuration, achieving a three fold reduction of the double pendulum maximum swinging angle.This work has been partially supported by FEDER funds through MINECO project TIN2017-85827-P, and project KK-202000044 of the Elkartek 2020 funding program of the Basque Government. This project has received funding from the European Unionโs Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720
Payload Grasping and Transportation by a Quadrotor with a Hook-Based Manipulator
The paper proposes an efficient trajectory planning and control approach for
payload grasping and transportation using an aerial manipulator. The proposed
manipulator structure consists of a hook attached to a quadrotor using a 1 DoF
revolute joint. To perform payload grasping, transportation, and release,
first, time-optimal reference trajectories are designed through specific
waypoints to ensure the fast and reliable execution of the tasks. Then, a
two-stage motion control approach is developed based on a robust geometric
controller for precise and reliable reference tracking and a linear--quadratic
payload regulator for rapid setpoint stabilization of the payload swing. The
proposed control architecture and design are evaluated in a high-fidelity
physical simulator with external disturbances and also in real flight
experiments.Comment: Submitted to IEEE Robotics and Automation Letters (2023
Controlling a Quadrotor Carrying a Cable-Suspended Load to Pass Through a Window
In this paper, we design an optimal control system for a quadrotor to carry a cable-suspended load flying through a window. As the window is narrower than the length of the cable, it is very challenging to design a practical control system to pass through it. Our solution includes a system identification component, a trajectory generation component, and a trajectory tracking control component. The exact dynamic model that usually derived from the first principles is assumed to be unavailable. Instead, a model identification approach is adopted, which relies on a simple but effective low order equivalent system (LOES) to describe the core dynamical characteristics of the system. After being excited by some specifically designed manoeuvres, the unknown parameters in the LOES are obtained by using a frequency based least square estimation algorithm. Based on the estimated LOES, a numerical optimization algorithm is then utilized for aggressive trajectory generation when relevant constraints are given. The generated trajectory can lead to the quadrotor and load system passing through a narrow window with a cascade PD trajectory tracking controller. Finally, a practical flight test based on an Astec Hummingbird quadrotor is demonstrated and the result validates the proposed approach
Agile load transportation systems using aerial robots
In this dissertation, we address problems that can occur during load transport using aerial robots, i.e., small scale quadrotors. First, detailed models of such transportation system are derived. These models include nonlinear models of a quadrotor, a model of a quadrotor carrying a fixed load and a model of a quadrotor carrying a suspended load. Second, the problem of quadrotor stabilization and trajectory tracking with changes of the center of gravity of the transportation system is addressed. This problem is solved using model reference adaptive control based on output feedback linearization that compensates for dynamical changes in the center of gravity of the quadrotor. The third problem we address is a problem of a swing-free transport of suspended load using quadrotors. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with suspended load flight, we present a method based on dynamic programming which is a model based offline method. The second investigated method we use is based on the Nelder-Mead algorithm which is an optimization technique used for nonlinear unconstrained optimization problems. This method is model free and it can be used for offline or online generation of the swing-free trajectories for the suspended load. Besides the swing-free maneuvers with suspended load, load trajectory tracking is another problem we solve in this dissertation. In order to solve this problem we use a Nelder-Mead based algorithm. In addition, we use an online least square policy iteration algorithm. At the end, we propose a high level algorithm for navigation in cluttered environments considering a quadrotor with suspended load. Furthermore, distributed control of multiple quadrotors with suspended load is addressed too. The proposed hierarchical architecture presented in this doctoral dissertation is an important step towards developing the next generation of agile autonomous aerial vehicles. These control algorithms enable quadrotors to display agile maneuvers while reconfiguring in real time whenever a change in the center of gravity occurs. This enables a swing-free load transport or trajectory tracking of the load in urban environments in a decentralized fashion
๋น์ ํ ์ต์ ํ๋ฅผ ์ด์ฉํ ๋ฉํฐ๋กํฐ ํ์ ์ด์ก์ ๊ฒฝ๋ก ๊ณํ ๋ฐ ์ ์ด ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2021.8. ๊นํ์ง.๊ฒฝ๋ก ๊ณํ๊ณผ ์ ์ด๋ ์์ ํ๊ณ ์์ ์ ์ผ๋ก ๋ฉํฐ๋กํฐ๋ฅผ ์ด์ฉํ๊ธฐ ์ํด์ ํ์์ ์ธ ์์์ด๋ค. ์ถฉ๋์ ํํผํ๋ฉฐ ํจ์จ์ ์ธ ๊ฒฝ๋ก๋ฅผ ์์ฑํ๊ณ ์ด๋ฅผ ์ค์ ๋ก ์ถ์ข
ํ๊ธฐ ์ํด์๋ ๋์ญํ ๋ชจ๋ธ์ด ๊ณ ๋ ค๋์ด์ผ ํ๋ค. ์ผ๋ฐ ๋ฉํฐ๋กํฐ์ ๋์ญํ ๋ชจ๋ธ์ ๋์ ์ฐจ์์ ๊ฐ์ง ๋น์ ํ์์ผ๋ก ํํ๋๋๋ฐ, ํ์ ์ด์ก ๋ฌผ์ฒด๋ฅผ ์ถ๊ฐํ ๊ฒฝ์ฐ ๊ณ์ฐ์ด ๋์ฑ ๋ณต์กํด์ง๋ค. ๋ณธ ๋
ผ๋ฌธ์ ๋ฉํฐ๋กํฐ๋ฅผ ์ด์ฉํ ํ์ ์ด์ก์ ์์ด ๊ฒฝ๋ก ๊ณํ๊ณผ ์ ์ด์ ๋ํ ํจ์จ์ ์ธ ๊ธฐ๋ฒ์ ์ ์ํ๋ค.
์ฒซ ๋ฒ์งธ๋ก ๋จ์ผ ๋ฉํฐ๋กํฐ๋ฅผ ์ด์ฉํ ํ์ ์ด์ก์ ๋ค๋ฃฌ๋ค. ๋ฌผ์ฒด๊ฐ ๋ณ๋์ ์์ธ์์ดํฐ ์์ด ์ด์ก๋ ๊ฒฝ์ฐ ๋ฌผ์ฒด๋ ๊ธฐ์ฒด์ ์์ง์์ ์ํด์๋ง ์ ์ด๊ฐ ๊ฐ๋ฅํ๋ค. ํ์ง๋ง, ๋์ญํ์์ ๋์ ๋น์ ํ์ฑ์ผ๋ก ์ด์ฉ์ ์ด๋ ค์์ด ์กด์ฌํ๋ค. ์ด๋ฅผ ๊ฒฝ๊ฐ์ํค๊ธฐ ์ํด์ ํ์ ๋์ญํ์์ ๋น์ ํ์ฑ์ ์ค์ด๊ณ ์์ธ ์ ์ด์ ์กด์ฌํ๋ ์๊ฐ ์ง์ฐ์ ๊ณ ๋ คํ์ฌ ๋์ญํ์์ ๊ฐ์ํํ๋ค. ๊ฒฝ๋ก ๊ณํ์ ์์ด์๋ ์ถฉ๋ ํํผ๋ฅผ ์ํด ๊ธฐ์ฒด, ์ผ์ด๋ธ, ๊ทธ๋ฆฌ๊ณ ์ด์ก ๋ฌผ์ฒด๋ฅผ ๋ค๋ฅธ ํฌ๊ธฐ์ ๋ชจ์์ ๊ฐ์ง ํ์์ฒด๋ค๋ก ๊ฐ์ธ๋ฉฐ, ํจ๊ณผ์ ์ด๋ฉด์๋ ๋ ๋ณด์์ ์ธ ๋ฐฉ์์ผ๋ก ์ถฉ๋ ํํผ ๊ตฌ์์กฐ๊ฑด์ ๋ถ๊ณผํ๋ค. Augmented Lagrangian ๋ฐฉ๋ฒ์ ์ด์ฉํ์ฌ ๋น์ ํ ๊ตฌ์์กฐ๊ฑด์ด ๋ถ๊ณผ๋ ๋น์ ํ ๋ฌธ์ ๋ฅผ ์ค์๊ฐ ์ต์ ํํ์ฌ ๊ฒฝ๋ก๋ฅผ ์์ฑํ๋ค. ์์ฑ๋ ๊ฒฝ๋ก๋ฅผ ์ถ์ข
ํ๊ธฐ ์ํด์ Sequential linear quadratic ์๋ฒ๋ฅผ ์ด์ฉํ ๋ชจ๋ธ ์์ธก ์ ์ด๊ธฐ๋ก ์ต์ ์ ์ด ์
๋ ฅ์ ๊ณ์ฐํ๋ค. ์ ์๋ ๊ธฐ๋ฒ์ ์ฌ๋ฌ ์๋ฎฌ๋ ์ด์
๊ณผ ์คํ์ ํตํด ๊ฒ์ฆํ๋ค.
๋ค์์ผ๋ก, ๋ค์ค ๋ฉํฐ๋กํฐ๋ฅผ ์ด์ฉํ ํ์
ํ์ ์ด์ก ์์คํ
์ ๋ค๋ฃฌ๋ค. ํด๋น ์์คํ
์ ์ํ ๋ณ์๋ ๋์ญํ์์์ ์ฐ๊ฒฐ๋(coupled) ํญ์ ๊ฐ์๋ ๊ธฐ์ฒด์ ์์ ๋น๋กํ์ฌ ์ฆ๊ฐํ๊ธฐ ๋๋ฌธ์, ํจ๊ณผ์ ์ธ ๊ธฐ๋ฒ ์์ด๋ ์ต์ ํ์ ๋ง์ ์๊ฐ์ด ์์๋๋ค. ๋์ ๋น์ ํ์ฑ์ ๊ฐ์ง ๋์ญํ์์ ๋ณต์ก์ฑ์ ๋ฎ์ถ๊ธฐ ์ํ์ฌ ๋ฏธ๋ถ ํํ์ฑ์ ์ฌ์ฉํ๋ค. ๊ฒฝ๋ก ๋ํ piece-wise Bernstein ๋คํญ์์ ์ด์ฉํ์ฌ ๋งค๊ฐ๋ณ์ํํ์ฌ ์ต์ ํ ๋ณ์์ ๊ฐ์๋ฅผ ์ค์ธ๋ค. ์ต์ ํ ๋ฌธ์ ๋ฅผ ๋ถํดํ๊ณ ์ถฉ๋ ํํผ ๊ตฌ์์กฐ๊ฑด๋ค์ ๋ํด ๋ณผ๋กํ(convexification)๋ฅผ ์ํํ์ฌ ์ด์ก ๋ฌผ์ฒด์ ๊ฒฝ๋ก์ ์ฅ๋ ฅ์ ๊ฒฝ๋ก์ ๋ํ ๋ณผ๋กํ(convex) ํ์๋ฌธ์ ๋ค์ด ๋ง๋ค์ด์ง๋ค. ์ฒซ ๋ฒ์งธ ํ์๋ฌธ์ ์ธ ๋ฌผ์ฒด ๊ฒฝ๋ก ์์ฑ์์๋, ์ฅ์ ๋ฌผ ํํผ์ ๋ฉํฐ๋กํฐ์ ๊ณต๊ฐ์ ํ๋ณดํ๊ธฐ ์ํ์ฌ ์์ ๋นํ ํต๋ก(safe flight corridor, SFC)์ ์ฌ์ ๊ฐ๊ฒฉ ๊ตฌ์์กฐ๊ฑด์ ๊ณ ๋ คํ์ฌ ์ต์ ํํ๋ค. ๋ค์์ผ๋ก, ์ฅ๋ ฅ ๋ฒกํฐ๋ค์ ๊ฒฝ๋ก๋ ์ฅ์ ๋ฌผ ํํผ์ ์ํธ ์ถฉ๋์ ๋ฐฉ์งํ๊ธฐ ์ํ์ฌ ์์ ๋นํ ์นํฐ(safe flight sector, SFS)์ ์๋ ์์ ๋นํ ์นํฐ(relative safe flight sector, RSFS) ๊ตฌ์์กฐ๊ฑด์ ๋ถ๊ณผํ์ฌ ์ต์ ํํ๋ค. ์๋ฎฌ๋ ์ด์
๊ณผ ์คํ์ผ๋ก ๋ณต์กํ ํ๊ฒฝ์์ ํจ์จ์ ์ธ ๊ฒฝ๋ก ๊ณํ ๊ธฐ๋ฒ์ ์์ฐํ๋ฉฐ ๊ฒ์ฆํ๋ค.Trajectory generation and control are fundamental requirements for safe and stable operation of multi-rotors. The dynamic model should be considered to generate efficient and collision-free trajectories with feasibility. While the dynamic model of a bare multi-rotor is expressed non-linearly with high dimensions which results in computational loads, the suspended load increases the complexity further. This dissertation presents efficient algorithms for trajectory generation and control of multi-rotors with a suspended load.
A single multi-rotor with a suspended load is addressed first. Since the load is suspended through a cable without any actuator, movement of the load must be controlled via maneuvers of the multi-rotor. However, the highly non-linear dynamics of the system results in difficulties. To relive them, the rotational dynamics is simplified to reduce the non-linearity and consider the delay in attitude control. For trajectory generation, the vehicle, cable, and load are considered as ellipsoids with different sizes and shapes, and collision-free constraints are expressed in an efficient and less-conservative way. The augmented Lagrangian method is applied to solve a nonlinear optimization problem with nonlinear constraints in real-time. Model predictive control with the sequential linear quadratic solver is used to track the generated trajectories. The proposed algorithm is validated with several simulations and experiment.
A system with multiple multi-rotors for cooperative transportation of a suspended load is addressed next. As the system has more state variables and coupling terms in the dynamic equation than the system with a single multi-rotor, optimization takes a long time without an efficient method. The differential flatness of the system is used to reduce the complexity of the highly non-linear dynamic equation. The trajectories are also parameterized using piece-wise Bernstein polynomials to decrease the number of optimization variables. By decomposing an optimization problem and performing convexification, convex sub-problems are formulated for the load and the tension trajectories optimization, respectively. In each sub-problem, a light-weight sampling method is used to find a feasible and low-cost trajectory as initialization. In the first sub-problem, the load trajectory is optimized with safe flight corridor (SFC) and clearance constraints for collision avoidance and security of space for the multi-rotors. Then, the tension histories are optimized with safe flight sector (SFS) and relative safe flight sector (RSFS) constraints for obstacle and inter-agent collision avoidance. Simulations and experiments are conducted to demonstrate efficient trajectory generation in a cluttered environment and validate the proposed algorithms.Chapter 1 Introduction 1
1.1 Literature Survey 5
1.2 Contributions 9
1.3 Outline 10
Chapter 2 Single Multi-rotor with a Suspended Load 11
2.1 Dynamics 11
2.2 Trajectory Generation 23
2.3 Optimal Control 31
Chapter 3 Multiple Multi-rotors with a Suspended Load 36
3.1 Problem Setting 36
3.2 Load Trajectory Generation 45
3.3 Tension History Generation 54
Chapter 4 Experimental Validation 68
4.1 Single Multi-rotor with a Suspended Load 68
4.2 Multiple Multi-rotors with a Suspended Load 79
Chapter 5 Conclusion 100
Appendix
A Detailed Derivation of Dierential Flatness 102
B Preliminaries of Bernstein Polynomials 108
B.1 Denition of a Bernstein Polynomial 108
B.2 Convex hull property of a Bernstein Polynomial 110
B.3 Representation of a General Polynomial with Bernstein Basis Polynomials 111
B.4 Representation of the Derivative of a Bernstein Polynomial with Bernstein Basis Polynomials 112
References 113
Abstract (in Korean) 119๋ฐ
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