77 research outputs found

    Differential-Flatness and Control of Quadrotor(s) with a Payload Suspended through Flexible Cable(s)

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    We present the coordinate-free dynamics of three different quadrotor systems : (a) single quadrotor with a point-mass payload suspended through a flexible cable; (b) multiple quadrotors with a shared point-mass payload suspended through flexible cables; and (c) multiple quadrotors with a shared rigid-body payload suspended through flexible cables. We model the flexible cable(s) as a finite series of links with spherical joints with mass concentrated at the end of each link. The resulting systems are thus high-dimensional with high degree-of-underactuation. For each of these systems, we show that the dynamics are differentially-flat, enabling planning of dynamically feasible trajectories. For the single quadrotor with a point-mass payload suspended through a flexible cable with five links (16 degrees-of-freedom and 12 degrees-of-underactuation), we use the coordinate-free dynamics to develop a geometric variation-based linearized equations of motion about a desired trajectory. We show that a finite-horizon linear quadratic regulator can be used to track a desired trajectory with a relatively large region of attraction

    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

    A framework to design interaction control of aerial slung load systems: transfer from existing flight control of under-actuated aerial vehicles

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    This paper establishes a framework within which interaction control is designed for the aerial slung load system composed of an underactuated aerial vehicle, a cable and a load. Instead of developing a new control law for the system, we propose the interaction control scheme by the controllers for under-actuated aerial systems. By selecting the deferentially flat output as the configuration, the equations of motion of the two systems are described in an identical form. The flight control task of the under-actuated aerial vehicle is thus converted into the control of the aerial slung load system. With the help of an admittance filter, the compliant trajectory is generated for the load subject to external interaction force. Moreover, the convergence of the whole system is proved by using the boundedness of the tracking error of vehicle attitude tracking as well as the estimation error of external force. Based on the developed theoretical results, an example is provided to illustrate the design algorithm of interaction controller for the aerial slung load via an existing flight controller directly. The correctness and applicability of the obtained results are demonstrated via the illustrative numerical example

    ๋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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