9 research outputs found

    Body Lift and Drag for a Legged Millirobot in Compliant Beam Environment

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    Much current study of legged locomotion has rightly focused on foot traction forces, including on granular media. Future legged millirobots will need to go through terrain, such as brush or other vegetation, where the body contact forces significantly affect locomotion. In this work, a (previously developed) low-cost 6-axis force/torque sensing shell is used to measure the interaction forces between a hexapedal millirobot and a set of compliant beams, which act as a surrogate for a densely cluttered environment. Experiments with a VelociRoACH robotic platform are used to measure lift and drag forces on the tactile shell, where negative lift forces can increase traction, even while drag forces increase. The drag energy and specific resistance required to pass through dense terrains can be measured. Furthermore, some contact between the robot and the compliant beams can lower specific resistance of locomotion. For small, light-weight legged robots in the beam environment, the body motion depends on both leg-ground and body-beam forces. A shell-shape which reduces drag but increases negative lift, such as the half-ellipsoid used, is suggested to be advantageous for robot locomotion in this type of environment.Comment: First three authors contributed equally. Accepted to ICRA 201

    Simulation of Flapping-wing Unmanned Aerial Vehicle using X-plane and Matlab/Simulink

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    This paper presents the simulation of flapping-wing unmanned aerial vehicle model using X-plane and Matlab/ Simulink. The flapping-wing ornithopter model (i.e. an aircraft that flies by flapping its wings) has been developed in plane maker software and executed in the X-plane environment. The key idea of flapping-wing mechanism in X-plane software is by varying its dihedral angle sinusoidally. This sinusoidally varying dihedral angle of wing creates upward and downward stroke moments inturn this creates a lift and a forward thrust for flying the flapping-wing model. Here pitch, roll, yaw and throttle (flapping rate) is fed as reference input through the user datagram protocol (UDP) port. The difference between the reference inputs, the simulated outputs are again fed back to simulator through UDP port and the gains are observed for the responses of flapping-wing unmanned aerial vehicle in Matlab/Simulink environment. Here various gains are used to monitor the optimized flying of flapping-wing model.Defence Science Journal, Vol. 64, No. 4, July 2014, pp.327-331, DOI:http://dx.doi.org/10.14429/dsj.64.493

    Detection of Slippery Terrain with a Heterogeneous Team of Legged Robots

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    Legged robots come in a range of sizes and capabilities. By combining these robots into heterogeneous teams, joint locomotion and perception tasks can be achieved by utilizing the diversified features of each robot. In this work we present a framework for using a heterogeneous team of legged robots to detect slippery terrain. StarlETH, a large and highly capable quadruped uses the VelociRoACH as a novel remote probe to detect regions of slippery terrain. StarlETH localizes the team using internal state estimation. To classify slippage of the VelociRoACH, we develop several Support Vector Machines (SVM) based on data from both StarlETH and VelociRoACH. By combining the teamโ€™s information about the motion of VelociRoACH, a classifier was built which could detect slippery spots with 92% (125/135) accuracy using only four features

    Aerodynamic performance of a flyable flapping wing rotor with passive pitching angle variation

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    The present work was based on an experimental study on the aerodynamic performance of a flapping wing rotor (FWR) and enhancement by passive pitching angle variation (PPAV) associated with powered flapping motion. The PPAV (in this study 10o~50o) was realized by a specially designed sleeve-pin unit as part of a U-shape flapping mechanism. Through experiment and analysis, it was found that the average lift produced by an FWR of PPAV was >100% higher than the baseline model, the same FWR of a constant pitching angle 30o under the same input power. It was also noted that the lift-voltage relationship for the FWR of PPAV was almost linear and the aerodynamic efficiency was also over 100% higher than the baseline FWR when the input voltage was under 6V. The aerodynamic lift or efficiency of the FWR of PPAV can be also increased significantly by reducing the weight of the wings. An FWR model was fabricated and achieved vertical take-off and free flight powered by 9V input voltage. The mechanism of PPAV function provides a feasible solution for aerodynamic improvement of a bio-inspired FWR and potential application to micro-air-vehicles (MAVs)

    ๊ผฌ๋ฆฌ๋‚ ๊ฐœ ์—†๋Š” ๋‚ ๊ฐฏ์ง“ ์ดˆ์†Œํ˜• ๋น„ํ–‰์ฒด์˜ ์ž์„ธ์กฐ์ ˆ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2020. 8. ๊น€ํ˜„์ง„.์ตœ๊ทผ ์ƒ์ฒด๋ชจ๋ฐฉ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์ปค์ง€๋ฉด์„œ ์ƒ๋ช…์ฒด์˜ ๊ตฌ์กฐ, ์™ธํ˜•, ์›€์ง์ž„, ํ–‰๋™์„ ๋ถ„์„ํ•˜์—ฌ ๊ทธ๋“ค์˜ ์žฅ์ ์„ ๋กœ๋ด‡์— ์ ์šฉ์‹œ์ผœ ๊ธฐ์กด์˜ ๋กœ๋ด‡์ด ํ•ด๊ฒฐํ•  ์ˆ˜ ์—†๊ฑฐ๋‚˜ ํŠน๋ณ„ํ•œ ์ž„๋ฌด๋ฅผ ์ข€ ๋” ํšจ๊ณผ, ํšจ์œจ์ ์œผ๋กœ ํ•ด๊ฒฐํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ๋„๋Š” ๋ฌด์ธ๋น„ํ–‰์ฒด ๊ฐœ๋ฐœ์—๋„ ์ ์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ ๋‚ ๊ฐฏ์ง“ ๋น„ํ–‰์ฒด๊ฐ€ ์ด์— ํ•ด๋‹น๋œ๋‹ค. ๋‚ ๊ฐœ์ง“ ๋น„ํ–‰์ฒด๋Š” ๋‚ ๊ฐœ์˜ ๋ฐ˜๋ณต์šด๋™์„ ํ†ตํ•ด ๋ฐœ์ƒํ•˜๋Š” ํž˜์„ ํ†ตํ•ด ๋น„ํ–‰ํ•˜๋Š” ๋น„ํ–‰์ฒด๋กœ ์ผ๋ฐ˜์ ์œผ๋กœ ๊ผฌ๋ฆฌ๋‚ ๊ฐœ์˜ ์œ ๋ฌด์— ๋”ฐ๋ผ ์ƒˆ๋ฅผ ๋ชจ๋ฐฉํ•œ ๋น„ํ–‰์ฒด(๋ฏธ์ตํ˜• ๋น„ํ–‰์ฒด)์™€ ๊ณค์ถฉ์„ ๋ชจ๋ฐฉํ•œ ๋น„ํ–‰์ฒด(๋ฌด๋ฏธ์ตํ˜• ๋น„ํ–‰์ฒด)๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฌด๋ฏธ์ตํ˜• ๋น„ํ–‰์ฒด์˜ ๊ฒฝ์šฐ ์ œ์ž๋ฆฌ ๋น„ํ–‰์„ ํ•  ์ˆ˜ ์žˆ๊ณ , ํฌ๊ธฐ๊ฐ€ ์ž‘๊ณ  ๋ฌด๊ฒŒ๊ฐ€ ๊ฐ€๋ฒผ์›Œ ๊ณต๊ธฐ์ €ํ•ญ๋„ ์ค„์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋‚ ๋ ตํ•œ ๋น„ํ–‰์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์ด ์žˆ์ง€๋งŒ, ์ˆ˜๋™ ์•ˆ์ •์„ฑ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•œ ์ œ์–ด๋ฉด์ด ์ถฉ๋ถ„ํ•˜์ง€ ์•Š๊ณ  ์ถ”๋ ฅ ์ƒ์„ฑ๊ณผ ๋™์‹œ์— 3์ถ•์œผ๋กœ์˜ ์ œ์–ด ๋ชจ๋ฉ˜ํŠธ๋ฅผ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋Š” ๋ณต์žกํ•œ ๋งค์ปค๋‹ˆ์ฆ˜์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค๋Š” ํŠน์ง•์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ €์ž์˜ ๋ฏธ์ตํ˜• ๋น„ํ–‰์ฒด์˜ ์—ฐ๊ตฌ๊ฐœ๋ฐœ ์‚ฌ๋ก€๋ฅผ ํ† ๋Œ€๋กœ ์ž์œจ ๋น„ํ–‰์„ ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌด๋ฏธ์ตํ˜• ๋น„ํ–‰์ฒด๋ฅผ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•œ ์š”์†Œ๊ธฐ์ˆ ๋“ค๊ณผ ์ดˆ๊ธฐ ๋น„ํ–‰์ฒด ๊ฐœ๋ฐœ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ํ•ด๋‹น ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ์ €์ž๋Š” ์‹œ์ค‘์—์„œ ํŒ๋งค๋˜๊ณ  ์žˆ๋Š” RC์žฅ๋‚œ๊ฐ์„ ํ™œ์šฉํ•ด 30 gram ์ดํ•˜์˜ ๋ฌด๊ฒŒ๋ฅผ ๊ฐ€์ง€๊ณ  30cm3 ์ด๋‚ด์˜ ํฌ๊ธฐ๋ฅผ ๊ฐ€์ง€๋Š” ๋ฌด๋ฏธ์ตํ˜• ๋‚ ๊ฐฏ์ง“ ๋น„ํ–‰์ฒด๋ฅผ ๊ฐœ๋ฐœ์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋น„ํ–‰์ฒด ๋‚ด๋ถ€์—๋Š” ๊ตฌ๋™๊ธฐ๋กœ DC ๋ชจํ„ฐ์™€ ์„œ๋ณด๋ชจํ„ฐ๊ฐ€ ์กด์žฌํ•˜๋ฉฐ, DC ๋ชจํ„ฐ๋Š” ๋‚ ๊ฐฏ์ง“์„ ์ผ์œผํ‚ค๋Š” ๊ธฐ์–ด ๋ฐ•์Šค๋ฅผ ์ž‘๋™์‹œ์ผœ ๋น„ํ–‰์ฒด์˜ ๋ฌด๊ฒŒ๋ฅผ ์ง€ํƒฑํ•˜๊ธฐ ์œ„ํ•œ thrust๋ฅผ ์ƒ์„ฑํ•˜๋ฉฐ roll์ถ• ๋ฐฉํ–ฅ์œผ๋กœ์˜ moment ์ƒ์„ฑ์— ๊ด€์—ฌํ•˜๋ฉฐ, ์„œ๋ณด๋ชจํ„ฐ๋Š” ๋‚ ๊ฐฏ์ง“์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ขŒ์šฐ thrust์˜ ๋ฐฉํ–ฅ์„ ์กฐ์ ˆํ•˜์—ฌ pitch ์™€ yaw ์ถ•์œผ๋กœ์˜ ๋ชจ๋ฉ˜ํŠธ๋ฅผ ์ƒ์„ฑํ•˜๋Š”๋ฐ ์‚ฌ์šฉ๋œ๋‹ค. ๋น„ํ–‰์ฒด ๋‚ด๋ถ€์—๋Š” ์•„๋‘์ด๋…ธ ๋ณด๋“œ ๊ธฐ๋ฐ˜์˜ ๋งˆ์ดํฌ๋กœํ”„๋กœ์„ธ์„œ๊ฐ€ ํƒ‘์žฌ๋˜์–ด ์žˆ์–ด ๋น„ํ–‰์ฒด๋ฅผ ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•œ ์‹ ํ˜ธ๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ๋ธ”๋ฃจํˆฌ์Šค ํ†ต์‹  ๋ชจ๋“ˆ์„ ๊ฐ€์ง€๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์™ธ๋ถ€์™€ ํ†ต์‹  ์—ญ์‹œ ๊ฐ€๋Šฅํ•˜๋‹ค. ๋น„ํ–‰์ฒด์˜ ์ž์„ธ๋ฅผ ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ตฌ๋™๊ธฐ์˜ ์ƒํ˜ธ์ž‘์šฉ์œผ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ํž˜์˜ ๋ฌผ๋ฆฌ๋Ÿ‰์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋‚ ๊ฐฏ์ง“ ๋ฉ”์ปค๋‹ˆ์ฆ˜์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํž˜์„ ์ธก์ •ํ•˜๋Š” ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ธก์ •์‹คํ—˜์„ ํ†ตํ•ด DC๋ชจํ„ฐ ์ž…๋ ฅ ๋Œ€๋น„ thrust ํฌ๊ธฐ, ์„œ๋ณด๋ชจํ„ฐ command ์ž…๋ ฅ ๋Œ€๋น„ moment ํฌ๊ธฐ ๋“ฑ์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋‚ ๊ฐฏ์ง“ ๋น„ํ–‰์ฒด๋ฅผ ๊ณต์ค‘์— ๋„์šธ ์ˆ˜ ์žˆ๋Š” ์ถฉ๋ถ„ํ•œ ํฌ๊ธฐ์˜ thrust๋ฅผ ๋ฐœ์ƒํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ ์ž์„ธ ์ œ์–ด๋ฅผ ์œ„ํ•œ ๋ชจ๋ฉ˜ํŠธ ์ƒ์„ฑ ์—ญ์‹œ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋น„ํ–‰์ฒด์˜ ์ž์„ธ๋ฅผ ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” 3์ถ• ๋ฐฉํ–ฅ์œผ๋กœ์˜ ์šด๋™๋ฐฉ์ •์‹์„ ์œ ๋„ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด roll, pitch, yaw ์ถ• ๋ฐฉํ–ฅ์œผ๋กœ ๋น„ํ–‰์ฒด์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํž˜๊ณผ ํšŒ์ „ ์šด๋™๊ณผ ๊ด€๋ จํ•œ ์šด๋™๋ฐฉ์ •์‹์„ ์œ ๋„ํ–ˆ์œผ๋ฉฐ ์ด๋ฅผ ํ†ตํ•ด ๋น„ํ–‰์ฒด์˜ ์ž์„ธ๋ฅผ ์•ˆ์ •ํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” PID ์ œ์–ด๊ธฐ ํ˜•ํƒœ์˜ ์ œ์–ด๊ธฐ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๋น„ํ–‰์ฒด์˜ ๊ถค์ ์ถ”์ข… ์ œ์–ด๋ฅผ ์œ„ํ•ด ๋‚ด๋ถ€์˜ ์ž์„ธ ์ œ์–ด๊ธฐ์— ๋น„ํ–‰์ฒด์˜ ์œ„์น˜๋ฅผ ํ† ๋Œ€๋กœ ๊ณ„์‚ฐ๋˜๋Š” ์ถ”๊ฐ€์ ์ธ ์™ธ๋ถ€ ์ œ์–ด๊ธฐ๋ฅผ ์„ค๊ณ„ํ•˜์—ฌ ์ด์ค‘๋ฃจํ”„ ์ œ์–ด๊ธฐ ํ˜•ํƒœ๋ฅผ ์ ์šฉ์‹œ์ผœ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ๋น„ํ–‰์ฒด์˜ ์ž์„ธ ์ œ์–ด์™€ ๊ถค์  ์ถ”์ข… ์ œ์–ด๊ฐ€ ์ด๋ฃจ์–ด์ง์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœํ•œ ๋น„ํ–‰์ฒด์™€ ์•ž์„œ ์„ค๊ณ„ํ•œ ์ œ์–ด๊ธฐ๊ฐ€ ์‚ฌ์šฉ์ž์˜ ์˜๋„์— ๋งž๋Š” ์„ฑ๋Šฅ์„ ๋‚ด๋Š”์ง€ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ์ž์ด๋กœ ์‹คํ—˜์žฅ์น˜๋ฅผ ์ œ์ž‘ํ•˜์—ฌ ์ž์„ธ ์ œ์–ด ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํ•ด๋‹น ์‹คํ—˜์žฅ์น˜๋Š” roll, pitch, yaw ์ถ•์œผ๋กœ ํšŒ์ „์ด ๊ฐ€๋Šฅํ•˜๋„๋ก ์ œ์ž‘ํ•˜์˜€์œผ๋ฉฐ ์‹คํ—˜์žฅ์น˜ ์ž์ฒด์˜ ๋ฌด๊ฒŒ๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด MDF ์†Œ์žฌ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ตฌ์กฐ๋ฌผ๋ฅผ ๋งŒ๋“ค์—ˆ๋‹ค. roll, pitch, yaw 3์ถ•์ด ๊ฐ๊ฐ ๋…๋ฆฝ์ ์œผ๋กœ ์ œ์–ดํ•˜๋Š” ๊ฒƒ๊ณผ 3์ถ•์„ ๋™์‹œ์— ์ œ์–ดํ•˜๋Š” 2๊ฐ€์ง€ ์ƒํ™ฉ์„ ๊ณ ๋ คํ•˜์˜€์œผ๋ฉฐ ์•ž์„œ ์„ค๊ณ„ํ•œ ์ œ์–ด๊ธฐ๊ฐ€ ํ•ด๋‹น ์‹คํ—˜ ์žฅ์น˜ ๋‚ด๋ถ€์—์„œ ์‚ฌ์šฉ์ž์˜ ์˜๋„์— ๋งž๊ฒŒ ์ œ์–ด ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š”์ง€ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ถค์  ์ถ”์ข…์ œ์–ด๋ฅผ ์œ„ํ•ด์„œ๋Š” 2๊ฐ€์ง€ ๋น„ํ–‰ ์ƒํ™ฉ์„ ์„ค์ •ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ๊ฒฝ์šฐ, ์ฒœ์žฅ๊ณผ ๋น„ํ–‰์ฒด ์ƒ๋‹จ๋ถ€์— ์‹ค์„ ์—ฐ๊ฒฐํ•˜์—ฌ 2D ํ‰๋ฉด์ƒ์—์„œ ๋น„ํ–‰์ฒด๊ฐ€ ์ฃผ์›Œ์ง„ ๊ถค์ ์— ๋”ฐ๋ผ ์›€์ง์ด๋Š”์ง€, ๋‘ ๋ฒˆ์งธ ๊ฒฝ์šฐ, ๋น„ํ–‰์ฒด ์ƒ๋‹จ๋ถ€์— ํ—ฌ๋ฅจ์ด ์ฃผ์ž…๋œ ํ’์„ ์„ ์—ฐ๊ฒฐ์‹œ์ผœ 3D ๊ณต๊ฐ„์ƒ์—์„œ ์ฃผ์›Œ์ง„ ๊ถค์ ์„ ๋”ฐ๋ผ ์ถ”์ข… ๋น„ํ–‰ํ•˜๋Š”์ง€๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š” ์ƒํ™ฉ์ด๋‹ค. ๋‘ ๊ฐ€์ง€ ์ƒํ™ฉ์—์„œ ๋ชจ๋‘ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ๊ถค์ ์„ ๋น„ํ–‰์ฒด๊ฐ€ ์ž˜ ์ถ”์ข…ํ•˜๋Š”์ง€๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋์œผ๋กœ, ์™ธ๋ถ€ ์žฅ์น˜(์‹ค, ํ’์„ )๋ฅผ ์ œ๊ฑฐํ•˜์—ฌ ๊ณต์ค‘์—์„œ ๋น„ํ–‰์ฒด๊ฐ€ ์ œ์ž๋ฆฌ ๋น„ํ–‰์„ ํ•  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ๊ฒ€์ฆํ•˜๋Š” ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, 15์ดˆ๊ฐ€๋Ÿ‰ 1m3 ๊ณต๊ฐ„ ๋‚ด์—์„œ ์ œ์ž๋ฆฌ ๋น„ํ–‰์ด ์ด๋ฃจ์–ด์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.Flapping wing micro air vehicles (FWMAVs) that generate thrust and lift by flapping their wings are regarded as promising flight vehicles because of their advantages in terms of similar appearance and maneuverability to natural creatures. Reducing weight and air resistance, insect-inspired tailless FWMAVs are an attractive aerial vehicle rather than bird-inspired FWMAVs. However, they are challenging platforms to achieve autonomous flight because they have insufficient control surfaces to secure passive stability and a complicated wing mechanism for generating three-axis control moments simultaneously. In this thesis, as preliminary autonomous flight research, I present the study of an attitude regulation and trajectory tracking control of a tailless FWMAV developed. For these tasks, I develop my platform, which includes two DC motors for generating thrust to support its weight and servo motors for generating three-axis control moments to regulate its flight attitude. First, I conduct the force and moment measurement experiment to confirm the magnitude and direction of the lift and moment generated from the wing mechanism. From the measurement test, it is confirmed that the wing mechanism generates enough thrust to float the vehicle and control moments for attitude regulation. Through the dynamic equations in the three-axis direction of the vehicle, a controller for maintaining a stable attitude of the vehicle can be designed. To this end, a dynamic equation related to the rotational motion in the roll, pitch, and yaw axes is derived. Based on the derived dynamic equations, we design a proportional-integral-differential controller (PID) type controller to compensate for the attitude of the vehicle. Besides, we use a multi-loop control structure (inner-loop: attitude control, outer-loop: position control) to track various trajectories. Simulation results show that the designed controller is effective in regulating the platforms attitude and tracking a trajectory. To check whether the developed vehicle and the designed controller are operating effectively to regulate its attitude, I design a lightweight gyroscope apparatus using medium-density-fiberboard (MDF) material. The rig is capable of freely rotating in the roll, pitch, and yaw axes. I consider two situations in which each axis is controlled independently, and all axes are controlled simultaneously. In both cases, attitude regulation is properly performed. Two flight situations are considered for the trajectory tracking experiment. In the first case, a string connects between the ceiling and the top of the platform. In the second case, the helium-filled balloon is connected to the top of the vehicle. In both cases, the platform tracks various types of trajectories well in error by less than 10 cm. Finally, an experiment is conducted to check whether the tailless FWMAV could fly autonomously in place by removing external devices (string, balloon), and the tailless FWMAV flies within 1 m^3 space for about 15 seconds1.Introduction 1 1.1 Background & Motivation 1 1.2 Literature review 3 1.3 Thesis contribution 7 1.4 Thesis outline 8 2.Design of tailless FWMAV 13 2.1 Platform appearance 13 2.2 Flight control system 17 2.3 Principle of actuator mechanism 18 3.Force measurement experiment 28 3.1 Measurement setup 28 3.2 Measurement results 30 4.Dynamics & Controller design 37 4.1 Preliminary 37 4.2 Dynamics & Attitude control 39 4.2.1 Roll direction 41 4.2.2 Pitch direction 43 4.2.3 Yaw direction 45 4.2.4 PID control 47 4.3 Trajectory tracking control 48 5.Attitude regulation experiments 50 5.1 Design of gyroscope testbed 50 5.2 Experimental environment 52 5.3 Roll axis free 53 5.3.1 Simulation 54 5.3.2 Experiment 55 5.4 Pitch axis free 56 5.4.1 Simulation 57 5.4.2 Experiment 58 5.5 Yaw axis free 59 5.5.1 Simulation 59 5.5.2 Experiment 60 5.6 All axes free 60 5.6.1 Simulation 60 5.6.2 Experiment 61 5.7 Design of universal joint testbed & Experiment 64 6.Trajectory tracking 68 6.1 Simulation 68 6.2 Preliminary 69 6.3 Experiment: Tied-to-the-ceiling 70 6.4 Experiment: Hung-to-a-balloon 71 6.5 Summary 72 6.6 Hovering flight 73 7.Conclusion 83 A Appendix: Wing gearbox 85 A.1 4-bar linkage structure 85 B Appendix: Disturbance observer (DOB) 87 B.1 DOB controller 87 B.2 Simulation 89 B.2.1 Step input 89 B.2.2 Sinusoid input 91 B.3 Experiment 92 References 95Docto

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ํ•˜์ˆœํšŒ.๊ฐ€๊นŒ์šด ๋ฏธ๋ž˜์—๋Š” ๋‹ค์–‘ํ•œ ๋กœ๋ด‡์ด ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•˜๋‚˜์˜ ์ž„๋ฌด๋ฅผ ํ˜‘๋ ฅํ•˜์—ฌ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ชจ์Šต์€ ํ”ํžˆ ๋ณผ ์ˆ˜ ์žˆ๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ๋กœ ์ด๋Ÿฌํ•œ ๋ชจ์Šต์ด ์‹คํ˜„๋˜๊ธฐ์—๋Š” ๋‘ ๊ฐ€์ง€์˜ ์–ด๋ ค์›€์ด ์žˆ๋‹ค. ๋จผ์ € ๋กœ๋ด‡์„ ์šด์šฉํ•˜๊ธฐ ์œ„ํ•œ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๋ช…์„ธํ•˜๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ๋Œ€๋ถ€๋ถ„ ๊ฐœ๋ฐœ์ž๊ฐ€ ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด์™€ ์†Œํ”„ํŠธ์›จ์–ด์— ๋Œ€ํ•œ ์ง€์‹์„ ์•Œ๊ณ  ์žˆ๋Š” ๊ฒƒ์„ ๊ฐ€์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋กœ๋ด‡์ด๋‚˜ ์ปดํ“จํ„ฐ์— ๋Œ€ํ•œ ์ง€์‹์ด ์—†๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ์—ฌ๋Ÿฌ ๋Œ€์˜ ๋กœ๋ด‡์ด ํ˜‘๋ ฅํ•˜๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ž‘์„ฑํ•˜๊ธฐ๋Š” ์‰ฝ์ง€ ์•Š๋‹ค. ๋˜ํ•œ, ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•  ๋•Œ ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด์˜ ํŠน์„ฑ๊ณผ ๊ด€๋ จ์ด ๊นŠ์–ด์„œ, ๋‹ค์–‘ํ•œ ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ๋„ ๊ฐ„๋‹จํ•˜์ง€ ์•Š๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ƒ์œ„ ์ˆ˜์ค€์˜ ๋ฏธ์…˜ ๋ช…์„ธ์™€ ๋กœ๋ด‡์˜ ํ–‰์œ„ ํ”„๋กœ๊ทธ๋ž˜๋ฐ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ƒˆ๋กœ์šด ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ๋ณธ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ๋กœ๋ด‡๋ถ€ํ„ฐ ๊ณ„์‚ฐ ๋Šฅ๋ ฅ์ด ์ถฉ๋ถ„ํ•œ ๋กœ๋ด‡๋“ค์ด ์„œ๋กœ ๊ตฐ์ง‘์„ ์ด๋ฃจ์–ด ๋ฏธ์…˜์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์›ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด๋‚˜ ์†Œํ”„ํŠธ์›จ์–ด์— ๋Œ€ํ•œ ์ง€์‹์ด ๋ถ€์กฑํ•œ ์‚ฌ์šฉ์ž๋„ ๋กœ๋ด‡์˜ ๋™์ž‘์„ ์ƒ์œ„ ์ˆ˜์ค€์—์„œ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋Š” ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์–ธ์–ด๋Š” ๊ธฐ์กด์˜ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด์—์„œ๋Š” ์ง€์›ํ•˜์ง€ ์•Š๋Š” ๋„ค ๊ฐ€์ง€์˜ ๊ธฐ๋Šฅ์ธ ํŒ€์˜ ๊ตฌ์„ฑ, ๊ฐ ํŒ€์˜ ์„œ๋น„์Šค ๊ธฐ๋ฐ˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ, ๋™์ ์œผ๋กœ ๋ชจ๋“œ ๋ณ€๊ฒฝ, ๋‹ค์ค‘ ์ž‘์—…(๋ฉ€ํ‹ฐ ํƒœ์Šคํ‚น)์„ ์ง€์›ํ•œ๋‹ค. ์šฐ์„  ๋กœ๋ด‡์€ ํŒ€์œผ๋กœ ๊ทธ๋ฃน ์ง€์„ ์ˆ˜ ์žˆ๊ณ , ๋กœ๋ด‡์ด ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋Šฅ์„ ์„œ๋น„์Šค ๋‹จ์œ„๋กœ ์ถ”์ƒํ™”ํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋ณตํ•ฉ ์„œ๋น„์Šค๋ฅผ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋กœ๋ด‡์˜ ๋ฉ€ํ‹ฐ ํƒœ์Šคํ‚น์„ ์œ„ํ•ด 'ํ”Œ๋žœ' ์ด๋ผ๋Š” ๊ฐœ๋…์„ ๋„์ž…ํ•˜์˜€๊ณ , ๋ณตํ•ฉ ์„œ๋น„์Šค ๋‚ด์—์„œ ์ด๋ฒคํŠธ๋ฅผ ๋ฐœ์ƒ์‹œ์ผœ์„œ ๋™์ ์œผ๋กœ ๋ชจ๋“œ๊ฐ€ ๋ณ€ํ™˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ์—ฌ๋Ÿฌ ๋กœ๋ด‡์˜ ํ˜‘๋ ฅ์ด ๋”์šฑ ๊ฒฌ๊ณ ํ•˜๊ณ , ์œ ์—ฐํ•˜๊ณ , ํ™•์žฅ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด, ๊ตฐ์ง‘ ๋กœ๋ด‡์„ ์šด์šฉํ•  ๋•Œ ๋กœ๋ด‡์ด ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋„์ค‘์— ๋ฌธ์ œ๊ฐ€ ์ƒ๊ธธ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ƒํ™ฉ์— ๋”ฐ๋ผ ๋กœ๋ด‡์„ ๋™์ ์œผ๋กœ ๋‹ค๋ฅธ ํ–‰์œ„๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋™์ ์œผ๋กœ๋„ ํŒ€์„ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๊ณ , ์—ฌ๋Ÿฌ ๋Œ€์˜ ๋กœ๋ด‡์ด ํ•˜๋‚˜์˜ ์„œ๋น„์Šค๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ทธ๋ฃน ์„œ๋น„์Šค๋ฅผ ์ง€์›ํ•˜๊ณ , ์ผ๋Œ€ ๋‹ค ํ†ต์‹ ๊ณผ ๊ฐ™์€ ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ์„ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด์— ๋ฐ˜์˜ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ํ™•์žฅ๋œ ์ƒ์œ„ ์ˆ˜์ค€์˜ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋Š” ๋น„์ „๋ฌธ๊ฐ€๋„ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ํ˜‘๋ ฅ ์ž„๋ฌด๋ฅผ ์‰ฝ๊ฒŒ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋‹ค. ๋กœ๋ด‡์˜ ํ–‰์œ„๋ฅผ ํ”„๋กœ๊ทธ๋ž˜๋ฐํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ”„๋ ˆ์ž„์›Œํฌ๊ฐ€ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์žฌ์‚ฌ์šฉ์„ฑ๊ณผ ํ™•์žฅ์„ฑ์„ ์ค‘์ ์œผ๋กœ ๋‘” ์—ฐ๊ตฌ๋“ค์ด ์ตœ๊ทผ ๋งŽ์ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์˜ ์ด๋“ค ์—ฐ๊ตฌ๋Š” ๋ฆฌ๋ˆ…์Šค ์šด์˜์ฒด์ œ์™€ ๊ฐ™์ด ๋งŽ์€ ํ•˜๋“œ์›จ์–ด ์ž์›์„ ํ•„์š”๋กœ ํ•˜๋Š” ์šด์˜์ฒด์ œ๋ฅผ ๊ฐ€์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ, ํ”„๋กœ๊ทธ๋žจ์˜ ๋ถ„์„ ๋ฐ ์„ฑ๋Šฅ ์˜ˆ์ธก ๋“ฑ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์—, ์ž์› ์ œ์•ฝ์ด ์‹ฌํ•œ ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•˜๊ธฐ์—๋Š” ์–ด๋ ต๋‹ค. ๊ทธ๋ž˜์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž„๋ฒ ๋””๋“œ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ์„ค๊ณ„ํ•  ๋•Œ ์“ฐ์ด๋Š” ์ •ํ˜•์ ์ธ ๋ชจ๋ธ์„ ์ด์šฉํ•œ๋‹ค. ์ด ๋ชจ๋ธ์€ ์ •์  ๋ถ„์„๊ณผ ์„ฑ๋Šฅ ์˜ˆ์ธก์ด ๊ฐ€๋Šฅํ•˜์ง€๋งŒ, ๋กœ๋ด‡์˜ ํ–‰์œ„๋ฅผ ํ‘œํ˜„ํ•˜๊ธฐ์—๋Š” ์ œ์•ฝ์ด ์žˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์™ธ๋ถ€์˜ ์ด๋ฒคํŠธ์— ์˜ํ•ด ์ˆ˜ํ–‰ ์ค‘๊ฐ„์— ํ–‰์œ„๋ฅผ ๋ณ€๊ฒฝํ•˜๋Š” ๋กœ๋ด‡์„ ์œ„ํ•ด ์œ ํ•œ ์ƒํƒœ ๋จธ์‹  ๋ชจ๋ธ๊ณผ ๋ฐ์ดํ„ฐ ํ”Œ๋กœ์šฐ ๋ชจ๋ธ์ด ๊ฒฐํ•ฉํ•˜์—ฌ ๋™์  ํ–‰์œ„๋ฅผ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋Š” ํ™•์žฅ๋œ ๋ชจ๋ธ์„ ์ ์šฉํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋”ฅ๋Ÿฌ๋‹๊ณผ ๊ฐ™์ด ๊ณ„์‚ฐ๋Ÿ‰์„ ๋งŽ์ด ํ•„์š”๋กœ ํ•˜๋Š” ์‘์šฉ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด, ๋ฃจํ”„ ๊ตฌ์กฐ๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์—ฌ๋Ÿฌ ๋กœ๋ด‡์˜ ํ˜‘์—… ์šด์šฉ์„ ์œ„ํ•ด ๋กœ๋ด‡ ์‚ฌ์ด์— ๊ณต์œ ๋˜๋Š” ์ •๋ณด๋ฅผ ๋‚˜ํƒ€๋‚ด๊ธฐ ์œ„ํ•ด ๋‘ ๊ฐ€์ง€ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๋จผ์ € ์ค‘์•™์—์„œ ๊ณต์œ  ์ •๋ณด๋ฅผ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ํƒœ์Šคํฌ๋ผ๋Š” ํŠน๋ณ„ํ•œ ํƒœ์Šคํฌ๋ฅผ ํ†ตํ•ด ๊ณต์œ  ์ •๋ณด๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋˜ํ•œ, ๋กœ๋ด‡์ด ์ž์‹ ์˜ ์ •๋ณด๋ฅผ ๊ฐ€๊นŒ์šด ๋กœ๋ด‡๋“ค๊ณผ ๊ณต์œ ํ•˜๊ธฐ ์œ„ํ•ด ๋ฉ€ํ‹ฐ์บ์ŠคํŒ…์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ํฌํŠธ๋ฅผ ์ถ”๊ฐ€ํ•œ๋‹ค. ์ด๋ ‡๊ฒŒ ํ™•์žฅ๋œ ์ •ํ˜•์ ์ธ ๋ชจ๋ธ์€ ์‹ค์ œ ๋กœ๋ด‡ ์ฝ”๋“œ๋กœ ์ž๋™ ์ƒ์„ฑ๋˜์–ด, ์†Œํ”„ํŠธ์›จ์–ด ์„ค๊ณ„ ์ƒ์‚ฐ์„ฑ ๋ฐ ๊ฐœ๋ฐœ ํšจ์œจ์„ฑ์— ์ด์ ์„ ๊ฐ€์ง„๋‹ค. ๋น„์ „๋ฌธ๊ฐ€๊ฐ€ ๋ช…์„ธํ•œ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋Š” ์ •ํ˜•์ ์ธ ํƒœ์Šคํฌ ๋ชจ๋ธ๋กœ ๋ณ€ํ™˜ํ•˜๊ธฐ ์œ„ํ•ด ์ค‘๊ฐ„ ๋‹จ๊ณ„์ธ ์ „๋žต ๋‹จ๊ณ„๋ฅผ ์ถ”๊ฐ€ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด, ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์—ฌ๋Ÿฌ ๋Œ€์˜ ์‹ค์ œ ๋กœ๋ด‡์„ ์ด์šฉํ•œ ํ˜‘์—…ํ•˜๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋Œ€ํ•ด ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค.In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming, which is called service-oriented and model-based (SeMo) framework. Also, it can support distributed robot systems, swarm robots, and their hybrid. For mission specification, a novel scripting language is proposed with the expression capability. It involves team composition and service-oriented behavior specification of each team, allowing dynamic mode change of operation and multi-tasking. Robots are grouped into teams, and the behavior of each team is defined with a composite service. The internal behavior of a composite service is defined by a sequence of services that the robots will perform. The notion of plan is applied to express multi-tasking. And the robot may have various operating modes, so mode change is triggered by events generated in a composite service. Moreover, to improve the robustness, scalability, and flexibility of robot collaboration, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. We assume that any robot fails during the execution of scenarios, and the grouping of robots can be made at run-time dynamically. Therefore, the extended mission specification enables a casual user to specify various types of cooperative missions easily. For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. To specify the dynamic behavior of the robot, we apply an extended task model that supports a hybrid specification of dataflow and finite state machine models. Furthermore, we propose a novel extension to allow the explicit specification of loop structures. This extension helps the compute-intensive application, which contains a lot of loop structures, to specify explicitly and analyze at compile time. Two types of information sharing, global information sharing and local knowledge sharing, are supported for robot collaboration in the dataflow graph. For global information, we use the library task, which supports shared resource management and server-client interaction. On the other hand, to share information locally with near robots, we add another type of port for multicasting and use the knowledge sharing technique. The actual robot code per robot is automatically generated from the associated task graph, which minimizes the human efforts in low-level robot programming and improves the software design productivity significantly. By abstracting the tasks or algorithms as services and adding the strategy description layer in the design flow, the mission specification is refined into task-graph specification automatically. The viability of the proposed methodology is verified with preliminary experiments with three cooperative mission scenarios with heterogeneous robot platforms and robot simulator.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Contribution 7 1.3 Dissertation Organization 9 Chapter 2. Background and Existing Research 11 2.1 Terminologies 11 2.2 Robot Software Development Frameworks 25 2.3 Parallel Embedded Software Development Framework 31 Chapter 3. Overview of the SeMo Framework 41 3.1 Motivational Examples 45 Chapter 4. Robot Behavior Programming 47 4.1 Related works 48 4.2 Model-based Task Graph Specification for Individual Robots 56 4.3 Model-based Task Graph Specification for Cooperating Robots 70 4.4 Automatic Code Generation 74 4.5 Experiments 78 Chapter 5. High-level Mission Specification 81 5.1 Service-oriented Mission Specification 82 5.2 Strategy Description 93 5.3 Automatic Task Graph Generation 96 5.4 Related works 99 5.5 Experiments 104 Chapter 6. Conclusion 114 6.1 Future Research 116 Bibliography 118 Appendices 133 ์š”์•ฝ 158Docto

    Design of Flying Robots for Collision Absorption and Self-Recovery

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    Flying robots have the unique advantage of being able to move through the air unaffected by the obstacles or precipices below them. This ability quickly becomes a disadvantage, however, as the amount of free space is reduced and the risk of collisions increases. Their sensitivity to any contact with the environment have kept them from venturing beyond large open spaces and obstacle-free skies. Recent efforts have concentrated on improving obstacle detection and avoidance strategies, modeling the environment and intelligent planning to navigate ever tighter spaces while remaining airborne. Though this strategy is yielding impressive and improving results, it is limited by the quality of the information that can be provided by on-board sensors. As evidenced by insects that collide with windows, there will always be situations in which sensors fail and a flying platform will collide with the obstacles around it. It is this fact that inspired the topic of this thesis: enabling flying platforms to survive and recover from contact with their environment through intelligent mechanical design. There are three main challenges tackled in this thesis: robustness to contact, self-recovery and integration into flight systems. Robustness to contact involves the protection of fast-spinning propellers, the stiff inner frame of a flying robot and its embedded sensors from damage through the elastic absorption of collision energy. A method is presented for designing protective structures that transfer the lowest possible amount of force to the platform's frame while simultaneously minimizing weight and thus their effect on flight performance. The method is first used to design a teardrop-shaped spring configuration for absorbing head-on collisions typically experienced by winged platforms. The design is implemented on a flying platform that can survive drops from a height of 2 m. A second design is then presented, this time using springs in a tetrahedral configuration that absorb energy through buckling. When embedded into a hovering platform the tetrahedral protective mechanisms are able to absorb dozens of high-speed collisions while significantly reducing the forces on the platforms frame compared to foam-based protection typically used on other platforms. Surviving a collision is only half of the equation and is only useful if a flying platform can subsequently return to flight without requiring human intervention, a process called self-recovery. The theory behind self-recovery as it applies to many types of flying platforms is first presented, followed by a method for designing and optimizing different types of self-recovery mechanisms. A gravity-based mechanism is implemented on an ultra-light (20.5 g) wing-based platform whose morphology and centre of gravity are optimized to always land on its side after a collision, ready to take off again. Such a mechanism, however, is limited to surfaces that are flat and obstacle-free and requires clear space in front of the platform to return to the air. A second, leg-based self-recovery mechanism is thus designed and integrated into a second hovering platform, allowing it to upright into a vertical takeoff position. The mechanism is successful in returning the platform to the air in a variety of complex environments, including sloped surfaces, corners and surface textures ranging from smooth hardwood to gravel and rocks. In a final chapter collision energy absorption and self-recovery mechanisms are integrated into a single hovering platform, the first example of a flying robot capable of crashing into obstacles, falling to the ground, uprighting and returning to the air, all without human intervention. These abilities are first demonstrated through a contact-based random search behaviour in which the platform explores a small enclosed room in complete darkness. After each collision with a wall the platform falls to the ground, recovers and then continues exploring. In a second experiment the platform is programmed with a basic phototaxis behaviour. Using only four photodiodes that provide a rough idea of the bearing to a source of light the platform is able to consistently cross a 13x2.2mcorridor and traverse a doorway without using any obstacle avoidance, modeling or planning
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