82 research outputs found

    AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation

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    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

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    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

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    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 Unionโ€™s 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

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    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

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    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

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    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

    ๋น„์„ ํ˜• ์ตœ์ ํ™”๋ฅผ ์ด์šฉํ•œ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ํ˜„์ˆ˜ ์šด์†ก์˜ ๊ฒฝ๋กœ ๊ณ„ํš ๋ฐ ์ œ์–ด ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2021.8. ๊น€ํ˜„์ง„.๊ฒฝ๋กœ ๊ณ„ํš๊ณผ ์ œ์–ด๋Š” ์•ˆ์ „ํ•˜๊ณ  ์•ˆ์ •์ ์œผ๋กœ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๋ฅผ ์šด์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ•„์ˆ˜์ ์ธ ์š”์†Œ์ด๋‹ค. ์ถฉ๋Œ์„ ํšŒํ”ผํ•˜๋ฉฐ ํšจ์œจ์ ์ธ ๊ฒฝ๋กœ๋ฅผ ์ƒ์„ฑํ•˜๊ณ  ์ด๋ฅผ ์‹ค์ œ๋กœ ์ถ”์ข…ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋™์—ญํ•™ ๋ชจ๋ธ์ด ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ์ผ๋ฐ˜ ๋ฉ€ํ‹ฐ๋กœํ„ฐ์˜ ๋™์—ญํ•™ ๋ชจ๋ธ์€ ๋†’์€ ์ฐจ์›์„ ๊ฐ€์ง„ ๋น„์„ ํ˜•์‹์œผ๋กœ ํ‘œํ˜„๋˜๋Š”๋ฐ, ํ˜„์ˆ˜ ์šด์†ก ๋ฌผ์ฒด๋ฅผ ์ถ”๊ฐ€ํ•  ๊ฒฝ์šฐ ๊ณ„์‚ฐ์ด ๋”์šฑ ๋ณต์žกํ•ด์ง„๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๋ฅผ ์ด์šฉํ•œ ํ˜„์ˆ˜ ์šด์†ก์— ์žˆ์–ด ๊ฒฝ๋กœ ๊ณ„ํš๊ณผ ์ œ์–ด์— ๋Œ€ํ•œ ํšจ์œจ์ ์ธ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ ๋‹จ์ผ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๋ฅผ ์ด์šฉํ•œ ํ˜„์ˆ˜ ์šด์†ก์„ ๋‹ค๋ฃฌ๋‹ค. ๋ฌผ์ฒด๊ฐ€ ๋ณ„๋„์˜ ์—‘์ธ„์—์ดํ„ฐ ์—†์ด ์šด์†ก๋  ๊ฒฝ์šฐ ๋ฌผ์ฒด๋Š” ๊ธฐ์ฒด์˜ ์›€์ง์ž„์— ์˜ํ•ด์„œ๋งŒ ์ œ์–ด๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค. ํ•˜์ง€๋งŒ, ๋™์—ญํ•™์‹์˜ ๋†’์€ ๋น„์„ ํ˜•์„ฑ์œผ๋กœ ์šด์šฉ์— ์–ด๋ ค์›€์ด ์กด์žฌํ•œ๋‹ค. ์ด๋ฅผ ๊ฒฝ๊ฐ์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ ํšŒ์ „ ๋™์—ญํ•™์‹์˜ ๋น„์„ ํ˜•์„ฑ์„ ์ค„์ด๊ณ  ์ž์„ธ ์ œ์–ด์— ์กด์žฌํ•˜๋Š” ์‹œ๊ฐ„ ์ง€์—ฐ์„ ๊ณ ๋ คํ•˜์—ฌ ๋™์—ญํ•™์‹์„ ๊ฐ„์†Œํ™”ํ•œ๋‹ค. ๊ฒฝ๋กœ ๊ณ„ํš์— ์žˆ์–ด์„œ๋Š” ์ถฉ๋Œ ํšŒํ”ผ๋ฅผ ์œ„ํ•ด ๊ธฐ์ฒด, ์ผ€์ด๋ธ”, ๊ทธ๋ฆฌ๊ณ  ์šด์†ก ๋ฌผ์ฒด๋ฅผ ๋‹ค๋ฅธ ํฌ๊ธฐ์™€ ๋ชจ์–‘์„ ๊ฐ€์ง„ ํƒ€์›์ฒด๋“ค๋กœ ๊ฐ์‹ธ๋ฉฐ, ํšจ๊ณผ์ ์ด๋ฉด์„œ๋„ ๋œ ๋ณด์ˆ˜์ ์ธ ๋ฐฉ์‹์œผ๋กœ ์ถฉ๋Œ ํšŒํ”ผ ๊ตฌ์†์กฐ๊ฑด์„ ๋ถ€๊ณผํ•œ๋‹ค. Augmented Lagrangian ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋น„์„ ํ˜• ๊ตฌ์†์กฐ๊ฑด์ด ๋ถ€๊ณผ๋œ ๋น„์„ ํ˜• ๋ฌธ์ œ๋ฅผ ์‹ค์‹œ๊ฐ„ ์ตœ์ ํ™”ํ•˜์—ฌ ๊ฒฝ๋กœ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ์ƒ์„ฑ๋œ ๊ฒฝ๋กœ๋ฅผ ์ถ”์ข…ํ•˜๊ธฐ ์œ„ํ•ด์„œ Sequential linear quadratic ์†”๋ฒ„๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ธ ์˜ˆ์ธก ์ œ์–ด๊ธฐ๋กœ ์ตœ์  ์ œ์–ด ์ž…๋ ฅ์„ ๊ณ„์‚ฐํ•œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ ์—ฌ๋Ÿฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ๋‹ค์ค‘ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๋ฅผ ์ด์šฉํ•œ ํ˜‘์—… ํ˜„์ˆ˜ ์šด์†ก ์‹œ์Šคํ…œ์„ ๋‹ค๋ฃฌ๋‹ค. ํ•ด๋‹น ์‹œ์Šคํ…œ์˜ ์ƒํƒœ ๋ณ€์ˆ˜๋‚˜ ๋™์—ญํ•™์‹์—์„œ ์—ฐ๊ฒฐ๋œ(coupled) ํ•ญ์˜ ๊ฐœ์ˆ˜๋Š” ๊ธฐ์ฒด์˜ ์ˆ˜์— ๋น„๋ก€ํ•˜์—ฌ ์ฆ๊ฐ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ํšจ๊ณผ์ ์ธ ๊ธฐ๋ฒ• ์—†์ด๋Š” ์ตœ์ ํ™”์— ๋งŽ์€ ์‹œ๊ฐ„์ด ์†Œ์š”๋œ๋‹ค. ๋†’์€ ๋น„์„ ํ˜•์„ฑ์„ ๊ฐ€์ง„ ๋™์—ญํ•™์‹์˜ ๋ณต์žก์„ฑ์„ ๋‚ฎ์ถ”๊ธฐ ์œ„ํ•˜์—ฌ ๋ฏธ๋ถ„ ํ‰ํƒ„์„ฑ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๊ฒฝ๋กœ ๋˜ํ•œ piece-wise Bernstein ๋‹คํ•ญ์‹์„ ์ด์šฉํ•˜์—ฌ ๋งค๊ฐœ๋ณ€์ˆ˜ํ™”ํ•˜์—ฌ ์ตœ์ ํ™” ๋ณ€์ˆ˜์˜ ๊ฐœ์ˆ˜๋ฅผ ์ค„์ธ๋‹ค. ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ๋ถ„ํ•ดํ•˜๊ณ  ์ถฉ๋Œ ํšŒํ”ผ ๊ตฌ์†์กฐ๊ฑด๋“ค์— ๋Œ€ํ•ด ๋ณผ๋กํ™”(convexification)๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ์šด์†ก ๋ฌผ์ฒด์˜ ๊ฒฝ๋กœ์™€ ์žฅ๋ ฅ์˜ ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ๋ณผ๋กํ•œ(convex) ํ•˜์œ„๋ฌธ์ œ๋“ค์ด ๋งŒ๋“ค์–ด์ง„๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•˜์œ„๋ฌธ์ œ์ธ ๋ฌผ์ฒด ๊ฒฝ๋กœ ์ƒ์„ฑ์—์„œ๋Š”, ์žฅ์• ๋ฌผ ํšŒํ”ผ์™€ ๋ฉ€ํ‹ฐ๋กœํ„ฐ์˜ ๊ณต๊ฐ„์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์•ˆ์ „ ๋น„ํ–‰ ํ†ต๋กœ(safe flight corridor, SFC)์™€ ์—ฌ์œ  ๊ฐ„๊ฒฉ ๊ตฌ์†์กฐ๊ฑด์„ ๊ณ ๋ คํ•˜์—ฌ ์ตœ์ ํ™”ํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์žฅ๋ ฅ ๋ฒกํ„ฐ๋“ค์˜ ๊ฒฝ๋กœ๋Š” ์žฅ์• ๋ฌผ ํšŒํ”ผ์™€ ์ƒํ˜ธ ์ถฉ๋Œ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์•ˆ์ „ ๋น„ํ–‰ ์„นํ„ฐ(safe flight sector, SFS)์™€ ์ƒ๋Œ€ ์•ˆ์ „ ๋น„ํ–‰ ์„นํ„ฐ(relative safe flight sector, RSFS) ๊ตฌ์†์กฐ๊ฑด์„ ๋ถ€๊ณผํ•˜์—ฌ ์ตœ์ ํ™”ํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹คํ—˜์œผ๋กœ ๋ณต์žกํ•œ ํ™˜๊ฒฝ์—์„œ ํšจ์œจ์ ์ธ ๊ฒฝ๋กœ ๊ณ„ํš ๊ธฐ๋ฒ•์„ ์‹œ์—ฐํ•˜๋ฉฐ ๊ฒ€์ฆํ•œ๋‹ค.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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