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    ๋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•

    ๊ธฐ์—…์ธ์ˆ˜ํ•ฉ๋ณ‘๊ณต์‹œ๊ฐ€ ํ•ฉ๋ณ‘๋Œ€์ƒ๊ธฐ์—…์˜ ๋™์ข…์—…์ข… ๊ฒฝ์Ÿ๊ธฐ์—…์— ๋ฏธ์นœ ์˜ํ–ฅ์— ๊ด€ํ•œ ์‹ค์ฆ์  ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฒฝ์˜ํ•™๊ณผ ๊ฒฝ์˜ํ•™์ „๊ณต,2002.Maste
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