3,110 research outputs found

    Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining

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    This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern

    Aerospace medicine and biology: A continuing bibliography with indexes (supplement 320)

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    This bibliography lists 125 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

    Robotic and clinical evaluation of upper limb motor performance in patients with Friedreich's Ataxia: an observational study

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    Background: Friedreichโ€™s ataxia (FRDA) is the most common hereditary autosomal recessive form of ataxia. In this disease there is early manifestation of gait ataxia, and dysmetria of the arms and legs which causes impairment in daily activities that require fine manual dexterity. To date there is no cure for this disease. Some novel therapeutic approaches are ongoing in different steps of clinical trial. Development of sensitive outcome measures is crucial to prove therapeutic effectiveness. The aim of the study was to assess the reliability and sensitivity of quantitative and objective assessment of upper limb performance computed by means of the robotic device and to evaluate the correlation with clinical and functional markers of the disease severity. Methods: Here we assess upper limb performances by means of the InMotion Arm Robot, a robot designed for clinical neurological applications, in a cohort of 14 children and young adults affected by FRDA, matched for age and gender with 18 healthy subjects. We focused on the analysis of kinematics, accuracy, smoothness, and submovements of the upper limb while reaching movements were performed. The robotic evaluation of upper limb performance consisted of planar reaching movements performed with the robotic system. The motors of the robot were turned off, so that the device worked as a measurement tool. The status of the disease was scored using the Scale for the Assessment and Rating of Ataxia (SARA). Relationships between robotic indices and a range of clinical and disease characteristics were examined. Results: All our robotic indices were significantly different between the two cohorts except for two, and were highly and reliably discriminative between healthy and subjects with FRDA. In particular, subjects with FRDA exhibited slower movements as well as loss of accuracy and smoothness, which are typical of the disease. Duration of Movement, Normalized Jerk, and Number of Submovements were the best discriminative indices, as they were directly and easily measurable and correlated with the status of the disease, as measured by SARA. Conclusions: Our results suggest that outcome measures obtained by means of robotic devices can improve the sensitivity of clinical evaluations of patientsโ€™ dexterity and can accurately and efficiently quantify changes over time in clinical trials, particularly when functional scales appear to be no longer sensitive

    An intelligent, free-flying robot

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    The ground based demonstration of the extensive extravehicular activity (EVA) Retriever, a voice-supervised, intelligent, free flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The major objective of the EVA Retriever Project is to design, develop, and evaluate an integrated robotic hardware and on-board software system which autonomously: (1) performs system activation and check-out; (2) searches for and acquires the target; (3) plans and executes a rendezvous while continuously tracking the target; (4) avoids stationary and moving obstacles; (5) reaches for and grapples the target; (6) returns to transfer the object; and (7) returns to base

    ๊ตฌ์กฐ๋กœ๋ด‡์„ ์œ„ํ•œ ๊ฐ•๊ฑดํ•œ ๊ณ„์ธต์  ๋™์ž‘ ๊ณ„ํš ๋ฐ ์ œ์–ด

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2021.8. ๋ฐ•์ข…์šฐ.Over the last several years, robotics has experienced a striking development, and a new generation of robots has emerged that shows great promise in being able to accomplish complex tasks associated with human behavior. Nowadays the objectives of the robots are no longer restricted to the automaton in the industrial process but are changing into explorers for hazardous, harsh, uncooperative, and extreme environments. As these robots usually operate in dynamic and unstructured environments, they should be robust, adaptive, and reactive under various changing operation conditions. We propose online hierarchical optimization-based planning and control methodologies for a rescue robot to execute a given mission in such a highly unstructured environment. A large number of degrees of freedom is provided to robots in order to achieve diverse kinematic and dynamic tasks. However, accomplishing such multiple objectives renders on-line reactive motion planning and control problems more difficult to solve due to the incompatible tasks. To address this problem, we exploit a hierarchical structure to precisely resolve conflicts by creating a priority in which every task is achieved as much as possible according to the levels. In particular, we concentrate on the reasoning about the task regularization to ensure the convergence and robustness of a solution in the face of singularity. As robotic systems with real-time motion planners or controllers often execute unrehearsed missions, a desired task cannot always be driven to a singularity free configuration. We develop a generic solver for regularized hierarchical quadratic programming without resorting to any off-the-shelf QP solver to take advantage of the null-space projections for computational efficiency. Therefore, the underlying principles are thoroughly investigated. The robust optimal solution is obtained under both equality and inequality tasks or constraints while addressing all problems resulting from the regularization. Especially as a singular value decomposition centric approach is leveraged, all hierarchical solutions and Lagrange multipliers for properly handling the inequality constraints are analytically acquired in a recursive procedure. The proposed algorithm works fast enough to be used as a practical means of real-time control system, so that it can be used for online motion planning, motion control, and interaction force control in a single hierarchical optimization. Core system design concepts of the rescue robot are presented. The goals of the robot are to safely extract a patient and to dispose a dangerous object instead of humans. The upper body is designed humanoid in form with replaceable modularized dual arms. The lower body is featured with a hybrid tracked and legged mobile platform to simultaneously acquire versatile manipulability and all-terrain mobility. Thus, the robot can successfully execute a driving task, dangerous object manipulation, and casualty extraction missions by changing the pose and modularized equipments in an optimized manner. Throughout the dissertation, all proposed methods are validated through extensive numerical simulations and experimental tests. We highlight precisely how the rescue robot can execute a casualty extraction and a dangerous object disposal mission both in indoor and outdoor environments that none of the existing robots has performed.์ตœ๊ทผ์— ๋“ฑ์žฅํ•œ ์ƒˆ๋กœ์šด ์„ธ๋Œ€์˜ ๋กœ๋ด‡์€ ๊ธฐ์กด์—๋Š” ์ธ๊ฐ„๋งŒ์ด ํ•  ์ˆ˜ ์žˆ์—ˆ๋˜ ๋ณต์žกํ•œ ์ผ์„ ๋กœ๋ด‡ ๋˜ํ•œ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ํŠนํžˆ DARPA Robotics Challenge๋ฅผ ํ†ตํ•ด ์ด๋Ÿฌํ•œ ์‚ฌ์‹ค์„ ์ž˜ ํ™•์ธํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด ๋กœ๋ด‡๋“ค์€ ๊ณต์žฅ๊ณผ ๊ฐ™์€ ์ •ํ˜•ํ™”๋œ ํ™˜๊ฒฝ์—์„œ ์ž๋™ํ™”๋œ ์ผ์„ ๋ฐ˜๋ณต์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•˜๋˜ ์ž„๋ฌด์—์„œ ๋” ๋‚˜์•„๊ฐ€ ๊ทนํ•œ์˜ ํ™˜๊ฒฝ์—์„œ ์ธ๊ฐ„์„ ๋Œ€์‹ ํ•˜์—ฌ ์œ„ํ—˜ํ•œ ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋ฐœ์ „ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋ž˜์„œ ์‚ฌ๋žŒ๋“ค์€ ์žฌ๋‚œํ™˜๊ฒฝ์—์„œ ์•ˆ์ „ํ•˜๊ณ  ์‹œ์˜ ์ ์ ˆํ•˜๊ฒŒ ๋Œ€์‘ํ•  ์ˆ˜ ์žˆ๋Š” ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ๋Œ€์•ˆ ์ค‘์—์„œ ์‹คํ˜„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ๋Œ€์ฒ˜ ๋ฐฉ์•ˆ์œผ๋กœ ๋กœ๋ด‡์„ ์ƒ๊ฐํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด๋Ÿฌํ•œ ๋กœ๋ด‡์€ ๋™์ ์œผ๋กœ ๋ณ€ํ™”ํ•˜๋Š” ๋น„์ •ํ˜• ํ™˜๊ฒฝ์—์„œ ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ถˆํ™•์‹ค์„ฑ์— ๋Œ€ํ•ด ๊ฐ•๊ฑดํ•ด์•ผํ•˜๊ณ , ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ ์กฐ๊ฑด์—์„œ ๋Šฅ๋™์ ์œผ๋กœ ๋ฐ˜์‘์„ ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋กœ๋ด‡์ด ๋น„์ •ํ˜• ํ™˜๊ฒฝ์—์„œ ๊ฐ•๊ฑดํ•˜๋ฉด์„œ๋„ ์ ์‘์ ์œผ๋กœ ๋™์ž‘ํ•  ์ˆ˜ ์žˆ๋Š” ์‹ค์‹œ๊ฐ„ ์ตœ์ ํ™” ๊ธฐ๋ฐ˜์˜ ๋™์ž‘ ๊ณ„ํš ๋ฐ ์ œ์–ด ๋ฐฉ๋ฒ•๊ณผ ๊ตฌ์กฐ ๋กœ๋ด‡์˜ ์„ค๊ณ„ ๊ฐœ๋…์„ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ธ๊ฐ„์€ ๋งŽ์€ ์ž์œ ๋„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ํ•˜๋‚˜์˜ ์ „์‹  ๋™์ž‘์„ ์ƒ์„ฑํ•  ๋•Œ ๋‹ค์–‘ํ•œ ๊ธฐ๊ตฌํ•™ ํ˜น์€ ๋™์—ญํ•™์  ํŠน์„ฑ์„ ๊ฐ€์ง€๋Š” ์„ธ๋ถ€ ๋™์ž‘ ํ˜น์€ ์ž‘์—…์„ ์ •์˜ํ•˜๊ณ , ์ด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ข…ํ•ฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ•™์Šต์„ ํ†ตํ•ด ๊ฐ ๋™์ž‘ ์š”์†Œ๋“ค์„ ์ตœ์ ํ™”ํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ƒํ™ฉ ์— ๋”ฐ๋ผ ๊ฐ ๋™์ž‘ ์š”์†Œ์— ์šฐ์„ ์ˆœ์œ„๋ฅผ ๋ถ€์—ฌํ•˜์—ฌ ์ด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๊ฒฐํ•ฉํ•˜๊ฑฐ๋‚˜ ๋ถ„๋ฆฌํ•˜์—ฌ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ตœ์ ์˜ ๋™์ž‘์„ ์ƒ์„ฑํ•˜๊ณ  ์ œ์–ดํ•œ๋‹ค. ์ฆ‰, ์ƒํ™ฉ์— ๋”ฐ๋ผ ์ค‘์š”ํ•œ ๋™์ž‘์š”์†Œ๋ฅผ ์šฐ์„ ์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•˜๊ณ  ์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋‚ฎ์€ ๋™์ž‘์š”์†Œ๋Š” ๋ถ€๋ถ„ ํ˜น์€ ์ „์ฒด์ ์œผ๋กœ ํฌ๊ธฐํ•˜๊ธฐ๋„ ํ•˜๋ฉด์„œ ๋งค์šฐ ์œ ์—ฐํ•˜๊ฒŒ ์ „์ฒด ๋™์ž‘์„ ์ƒ์„ฑํ•˜๊ณ  ์ตœ์ ํ™” ํ•œ๋‹ค. ์ธ๊ฐ„๊ณผ ๊ฐ™์ด ๋‹ค์ž์œ ๋„๋ฅผ ๋ณด์œ ํ•œ ๋กœ๋ด‡ ๋˜ํ•œ ๊ธฐ๊ตฌํ•™๊ณผ ๋™์—ญํ•™์  ํŠน์„ฑ์„ ๊ฐ€์ง€๋Š” ๋‹ค์–‘ํ•œ ์„ธ๋ถ€ ๋™์ž‘ ํ˜น์€ ์ž‘์—…์„ ์ž‘์—…๊ณต๊ฐ„(task space) ํ˜น์€ ๊ด€์ ˆ๊ณต๊ฐ„(configuration space)์—์„œ ์ •์˜ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์šฐ์„ ์ˆœ์œ„์— ๋”ฐ๋ผ ์ด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๊ฒฐํ•ฉํ•˜์—ฌ ์ „์ฒด ๋™์ž‘์„ ์ƒ ์„ฑํ•˜๊ณ  ์ œ์–ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์„œ๋กœ ์–‘๋ฆฝํ•˜๊ธฐ ์–ด๋ ค์šด ๋กœ๋ด‡์˜ ๋™์ž‘ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋™์ž‘๋“ค ์‚ฌ์ด์— ์šฐ์„ ์ˆœ์œ„๋ฅผ ๋ถ€์—ฌํ•˜์—ฌ ๊ณ„์ธต์„ ์ƒ์„ฑํ•˜๊ณ , ์ด์— ๋”ฐ๋ผ ๋กœ๋ด‡์˜ ์ „์‹  ๋™์ž‘์„ ๊ตฌํ˜„ํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์˜ค๋žซ๋™์•ˆ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์–ด ์™”๋‹ค. ์ด๋Ÿฌํ•œ ๊ณ„์ธต์  ์ตœ์ ํ™”๋ฅผ ์ด์šฉํ•˜๋ฉด ์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋†’์€ ๋™์ž‘๋ถ€ํ„ฐ ์ˆœ์ฐจ์ ์œผ๋กœ ์‹คํ–‰ํ•˜์ง€๋งŒ, ์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋‚ฎ์€ ๋™์ž‘์š”์†Œ๋“ค๋„ ๊ฐ€๋Šฅํ•œ ๋งŒ์กฑ์‹œํ‚ค๋Š” ์ตœ์ ์˜ ํ•ด๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ด€์ ˆ์˜ ๊ตฌ๋™ ๋ฒ”์œ„์™€ ๊ฐ™์€ ๋ถ€๋“ฑ์‹์˜ ์กฐ๊ฑด์ด ํฌํ•จ๋œ ๊ณ„์ธต์  ์ตœ์ ํ™” ๋ฌธ์ œ์—์„œ ํŠน์ด์ ์— ๋Œ€ํ•œ ๊ฐ•๊ฑด์„ฑ๊นŒ์ง€ ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด์„œ๋Š” ์•„์ง๊นŒ์ง€ ๋งŽ์€ ๋ถ€๋ถ„์ด ๋ฐ ํ˜€์ง„ ๋ฐ”๊ฐ€ ์—†๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋“ฑ์‹๊ณผ ๋ถ€๋“ฑ์‹์œผ๋กœ ํ‘œํ˜„๋˜๋Š” ๊ตฌ์†์กฐ๊ฑด ํ˜น์€ ๋™์ž‘์š”์†Œ๋ฅผ ๊ณ„์ธต์  ์ตœ์ ํ™”์— ๋™์‹œ์— ํฌํ•จ์‹œํ‚ค๊ณ , ํŠน์ด์ ์ด ์กด์žฌํ•˜๋”๋ผ๋„ ๊ฐ•๊ฑด์„ฑ๊ณผ ์ˆ˜๋ ด์„ฑ์„ ๋ณด์žฅํ•˜๋Š” ๊ด€์ ˆ๊ณต๊ฐ„์—์„œ์˜ ์ตœ์ ํ•ด๋ฅผ ํ™•๋ณดํ•˜๋Š”๋ฐ ์ง‘์ค‘ํ•œ๋‹ค. ์™œ๋‚˜ํ•˜๋ฉด ๋น„์ •ํ˜• ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋กœ๋ด‡์€ ์‚ฌ์ „์— ๊ณ„ํš๋œ ๋™์ž‘์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹Œ ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ์กฐ๊ฑด์— ๋”ฐ๋ผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋™์ž‘์„ ๊ณ„ํšํ•˜๊ณ  ์ œ์–ดํ•ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํŠน์ด์ ์ด ์—†๋Š” ์ž์„ธ๋กœ ๋กœ๋ด‡์„ ํ•ญ์ƒ ์ œ์–ดํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋ ‡๊ฒŒ ํŠน์ด์ ์„ ํšŒํ”ผํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋กœ๋ด‡์„ ์ œ์–ดํ•˜๋Š” ๊ฒƒ์€ ๋กœ๋ด‡์˜ ์šด์šฉ์„ฑ์„ ์‹ฌ๊ฐํ•˜๊ฒŒ ์ €ํ•ด์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ํŠน์ด์  ๊ทผ๋ฐฉ์—์„œ์˜ ํ•ด์˜ ๊ฐ•๊ฑด์„ฑ์ด ๋ณด์žฅ๋˜์ง€ ์•Š์œผ๋ฉด ๋กœ๋ด‡ ๊ด€์ ˆ์— ๊ณผ๋„ํ•œ ์†๋„ ํ˜น์€ ํ† ํฌ๊ฐ€ ๋ฐœ์ƒํ•˜์—ฌ ๋กœ๋ด‡์˜ ์ž„๋ฌด ์ˆ˜ํ–‰์ด ๋ถˆ๊ฐ€๋Šฅํ•˜๊ฑฐ๋‚˜ ํ™˜๊ฒฝ๊ณผ ๋กœ๋ด‡์˜ ์†์ƒ์„ ์ดˆ๋ž˜ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋‚˜์•„๊ฐ€ ๋กœ๋ด‡๊ณผ ํ•จ๊ป˜ ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ์‚ฌ๋žŒ์—๊ฒŒ ์ƒํ•ด๋ฅผ ๊ฐ€ํ•  ์ˆ˜๋„ ์žˆ๋‹ค. ํŠน์ด์ ์— ๋Œ€ํ•œ ๊ฐ•๊ฑด์„ฑ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด ์šฐ์„ ์ˆœ์œ„ ๊ธฐ๋ฐ˜์˜ ๊ณ„์ธต์  ์ตœ์ ํ™”์™€ ์ •๊ทœํ™” (regularization)๋ฅผ ํ†ตํ•ฉํ•˜์—ฌ ์ •๊ทœํ™”๋œ ๊ณ„์ธต์  ์ตœ์ ํ™” (RHQP: Regularized Hierarchical Quadratic Program) ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ๋ถ€๋“ฑ์‹์ด ํฌํ•จ๋œ ๊ณ„์ธต์  ์ตœ์ ํ™”์— ์ •๊ทœํ™”๋ฅผ ๋™์‹œ์— ๊ณ ๋ คํ•จ์œผ๋กœ์จ ์•ผ๊ธฐ๋˜๋Š” ๋งŽ์€ ๋ฌธ์ œ์ ๋“ค์„ ํ•ด๊ฒฐํ•˜๊ณ  ํ•ด์˜ ์ตœ์ ์„ฑ๊ณผ ๊ฐ•๊ฑด์„ฑ์„ ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ํŠนํžˆ ์™ธ๋ถ€์˜ ์ตœ์ ํ™” ํ”„๋กœ๊ทธ๋žจ์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ์ˆ˜์น˜์  ์ตœ์ ํ™” (numerical optimization) ์ด๋ก ๊ณผ ์šฐ์„ ์ˆœ์œ„์— ๊ธฐ๋ฐ˜์„ ๋‘๋Š” ์—ฌ์œ ์ž์œ ๋„ ๋กœ๋ด‡์˜ ํ•ด์„ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๊ณ„์‚ฐ์˜ ํšจ์œจ์„ฑ์„ ๊ทน๋Œ€ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ์ด์ฐจ ํ”„๋กœ๊ทธ๋žจ(quadratic programming)์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ ์ด์™€ ๋™์‹œ์— ์ •๊ทœํ™”๋œ ๊ณ„์ธต์  ์ตœ์ ํ™” ๋ฌธ์ œ์˜ ์ด๋ก ์  ๊ตฌ์กฐ๋ฅผ ์ฒ ์ €ํ•˜๊ฒŒ ๋ถ„์„ํ•œ๋‹ค. ํŠนํžˆ ํŠน์ด๊ฐ’ ๋ถ„ํ•ด (singular value decomposition)๋ฅผ ํ†ตํ•ด ์ตœ์ ํ•ด์™€ ๋ถ€๋“ฑ์‹ ์กฐ๊ฑด์„ ์ฒ˜๋ฆฌํ•˜๋Š”๋ฐ ํ•„์š”ํ•œ ๋ผ๊ทธ๋ž‘์ง€ ์Šน์ˆ˜๋ฅผ ์žฌ๊ท€์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ํ•ด์„์  ํ˜•ํƒœ๋กœ ๊ตฌํ•จ์œผ๋กœ์จ ๊ณ„์‚ฐ์˜ ํšจ์œจ์„ฑ์„ ์ฆ๋Œ€์‹œํ‚ค๊ณ  ๋™์‹œ์— ๋ถ€๋“ฑ์‹์˜ ์กฐ๊ฑด์„ ์˜ค๋ฅ˜ ์—†์ด ์ •ํ™•ํ•˜๊ฒŒ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ •๊ทœํ™”๋œ ๊ณ„์ธต์  ์ตœ์ ํ™”๋ฅผ ํž˜์ œ์–ด๊นŒ์ง€ ํ™•์žฅํ•˜์—ฌ ํ™˜๊ฒฝ๊ณผ ๋กœ๋ด‡์˜ ์•ˆ์ „ํ•œ ์ƒํ˜ธ์ž‘์šฉ์„ ๋ณด์žฅํ•˜์—ฌ ๋กœ๋ด‡์ด ์ ์ ˆํ•œ ํž˜์œผ๋กœ ํ™˜๊ฒฝ๊ณผ ์ ‘์ด‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋ถˆํ™•์‹ค์„ฑ์ด ์กด์žฌํ•˜๋Š” ๋น„์ •ํ˜• ํ™˜๊ฒฝ์—์„œ ๋น„์ •ํ˜• ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๊ตฌ์กฐ๋กœ๋ด‡์˜ ํ•ต์‹ฌ ์„ค๊ณ„ ๊ฐœ๋…์„ ์ œ์‹œํ•œ๋‹ค. ๋น„์ •ํ˜• ํ™˜๊ฒฝ์—์„œ์˜ ์กฐ์ž‘ ์„ฑ๋Šฅ๊ณผ ์ด๋™ ์„ฑ๋Šฅ์„ ๋™์‹œ์— ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋Š” ํ˜•์ƒ์œผ๋กœ ๋กœ๋ด‡์„ ์„ค๊ณ„ํ•˜์—ฌ ๊ตฌ์กฐ ๋กœ๋ด‡์œผ๋กœ ํ•˜์—ฌ๊ธˆ ์ตœ์ข… ๋ชฉ์ ์œผ๋กœ ์„ค์ •๋œ ์ธ๊ฐ„์„ ๋Œ€์‹ ํ•˜์—ฌ ๋ถ€์ƒ์ž๋ฅผ ๊ตฌ์กฐํ•˜๊ณ  ์œ„ํ—˜๋ฌผ์„ ์ฒ˜๋ฆฌํ•˜๋Š” ์ž„๋ฌด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ๊ตฌ์กฐ ๋กœ๋ด‡์— ํ•„์š”ํ•œ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋Š” ๋ถ€์ƒ์ž ๊ตฌ์กฐ ์ž„๋ฌด์™€ ์œ„ํ—˜๋ฌผ ์ฒ˜๋ฆฌ ์ž„๋ฌด์— ๋”ฐ๋ผ ๊ต์ฒด ๊ฐ€๋Šฅํ•œ ๋ชจ๋“ˆํ˜•์œผ๋กœ ์„ค๊ณ„ํ•˜์—ฌ ๊ฐ๊ฐ์˜ ์ž„๋ฌด์— ๋”ฐ๋ผ ์ตœ์ ํ™”๋œ ๋งค๋‹ˆํ“ฐ ๋ ˆ์ดํ„ฐ๋ฅผ ์žฅ์ฐฉํ•˜์—ฌ ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ฒด๋Š” ํŠธ๋ž™๊ณผ ๊ด€์ ˆ์ด ๊ฒฐํ•ฉ๋œ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ํ˜•ํƒœ๋ฅผ ์ทจํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ฃผํ–‰ ์ž„๋ฌด์™€ ์กฐ์ž‘์ž„๋ฌด์— ๋”ฐ๋ผ ํ˜•์ƒ์„ ๋ณ€๊ฒฝํ•  ์ˆ˜ ์žˆ๋‹ค. ํ˜•์ƒ ๋ณ€๊ฒฝ๊ณผ ๋ชจ๋“ˆํ™”๋œ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋ฅผ ํ†ตํ•ด์„œ์กฐ์ž‘ ์„ฑ๋Šฅ๊ณผ ํ—˜ํ•œ ์ง€ํ˜•์—์„œ ์ด๋™ํ•  ์ˆ˜ ์žˆ๋Š” ์ฃผํ–‰ ์„ฑ๋Šฅ์„ ๋™์‹œ์— ํ™•๋ณดํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ๊ตฌ์กฐ๋กœ๋ด‡์˜ ์„ค๊ณ„์™€ ์‹ค์‹œ๊ฐ„ ๊ณ„์ธต์  ์ œ์–ด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋น„์ •ํ˜• ์‹ค๋‚ด์™ธ ํ™˜๊ฒฝ์—์„œ ๊ตฌ์กฐ๋กœ๋ด‡์ด ์ฃผํ–‰์ž„๋ฌด, ์œ„ํ—˜๋ฌผ ์กฐ์ž‘์ž„๋ฌด, ๋ถ€์ƒ์ž ๊ตฌ์กฐ ์ž„๋ฌด๋ฅผ ์„ฑ๊ณต์ ์œผ๋กœ ์ˆ˜ ํ–‰ํ•  ์ˆ˜ ์žˆ์Œ์„ ํ•ด์„๊ณผ ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ ์ž…์ฆํ•จ์œผ๋กœ์จ ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ์„ค๊ณ„์™€ ์ •๊ทœํ™”๋œ ๊ณ„์ธต์  ์ตœ์ ํ™” ๊ธฐ๋ฐ˜์˜ ์ œ์–ด ์ „๋žต์˜ ์œ ์šฉ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.1 Introduction 1 1.1 Motivations 1 1.2 Related Works and Research Problems for Hierarchical Control 3 1.2.1 Classical Approaches 3 1.2.2 State-of-the-Art Strategies 4 1.2.3 Research Problems 7 1.3 Robust Rescue Robots 9 1.4 Research Goals 12 1.5 Contributions of ThisThesis 13 1.5.1 Robust Hierarchical Task-Priority Control 13 1.5.2 Design Concepts of Robust Rescue Robot 16 1.5.3 Hierarchical Motion and ForceControl 17 1.6 Dissertation Preview 18 2 Preliminaries for Task-Priority Control Framework 21 2.1 Introduction 21 2.2 Task-Priority Inverse Kinematics 23 2.3 Recursive Formulation of Null Space Projector 28 2.4 Conclusion 31 3 Robust Hierarchical Task-Priority Control 33 3.1 Introduction 33 3.1.1 Motivations 35 3.1.2 Objectives 36 3.2 Task Function Approach 37 3.3 Regularized Hierarchical Optimization with Equality Tasks 41 3.3.1 Regularized Hierarchical Optimization 41 3.3.2 Optimal Solution 45 3.3.3 Task Error and Hierarchical Matrix Decomposition 49 3.3.4 Illustrative Examples for Regularized Hierarchical Optimization 56 3.4 Regularized Hierarchical Optimization with Inequality Constraints 60 3.4.1 Lagrange Multipliers 61 3.4.2 Modified Active Set Method 66 3.4.3 Illustrative Examples of Modified Active Set Method 70 3.4.4 Examples for Hierarchical Optimization with Inequality Constraint 72 3.5 DLS-HQP Algorithm 79 3.6 Concluding Remarks 80 4 Rescue Robot Design and Experimental Results 83 4.1 Introduction 83 4.2 Rescue Robot Design 85 4.2.1 System Design 86 4.2.2 Variable Configuration Mobile Platform 92 4.2.3 Dual Arm Manipulators 95 4.2.4 Software Architecture 97 4.3 Performance Verification for Hierarchical Motion Control 99 4.3.1 Real-Time Motion Generation 99 4.3.2 Task Specifications 103 4.3.3 Singularity Robust Task Priority 106 4.3.4 Inequality Constraint Handling and Computation Time 111 4.4 Singularity Robustness and Inequality Handling for Rescue Mission 117 4.5 Field Tests 122 4.6 Concluding Remarks 126 5 Hierarchical Motion and Force Control 129 5.1 Introduction 129 5.2 Operational Space Control 132 5.3 Acceleration-Based Hierarchical Motion Control 134 5.4 Force Control 137 5.4.1 Force Control with Inner Position Loop 141 5.4.2 Force Control with Inner Velocity Loop 144 5.5 Motion and Force Control 145 5.6 Numerical Results for Acceleration-Based Motion and Force Control 148 5.6.1 Task Specifications 150 5.6.2 Force Control Performance 151 5.6.3 Singularity Robustness and Inequality Constraint Handling 155 5.7 Velocity Resolved Motion and Force Control 160 5.7.1 Velocity-Based Motion and Force Control 161 5.7.2 Experimental Results 163 5.8 Concluding Remarks 167 6 Conclusion 169 6.1 Summary 169 6.2 Concluding Remarks 173 A Appendix 175 A.1 Introduction to PID Control 175 A.2 Inverse Optimal Control 176 A.3 Experimental Results and Conclusion 181 Bibliography 183 Abstract 207๋ฐ•

    NASA space station automation: AI-based technology review

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    Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

    Analysis and experimental evaluation of a Stewart platform-based force/torque sensor

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    The kinematic analysis and experimentation of a force/torque sensor whose design is based on the mechanism of the Stewart Platform are discussed. Besides being used for measurement of forces/torques, the sensor also serves as a compliant platform which provides passive compliance during a robotic assembly task. It consists of two platforms, the upper compliant platform (UCP) and the lower compliant platform (LCP), coupled together through six spring-loaded pistons whose length variations are measured by six linear voltage differential transformers (LVDT) mounted along the pistons. Solutions to the forward and inverse kinematics of the force sensor are derived. Based on the known spring constant and the piston length changes, forces/torques applied to the LCP gripper are computed using vector algebra. Results of experiments conducted to evaluate the sensing capability of the force sensor are reported and discussed

    Safe and effective physical human-robot interaction: Approaches to variable compliance via soft joints and soft grippers

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    The work described in this thesis focusses on designing and building two novel physical devices in a robotic arm structure. The arm is intended for human-robot interaction in the domestic assistive robotics area. The first device aims at helping to ensure the safety of the human user. It acts as a mechanical fuse and disconnects the robotic arm link from its motor in case of collision. The device behaves in a rigid manner in normal operational times and in a compliant manner in case of potentially harmful collisions: it relies on a variable compliance. The second device is the end-effector of the robotic arm. It is a novel grasping device that aims at accommodating varying object shapes. This is achieved by the structure of the grasping device that is a soft structure with a compliant and a rigid phase. Its completely soft structure is able to mould to the object's shape in the compliant phase, while the rigid phase allows holding the object in a stable way.In this study, variable compliance is defined as a physical structure's change from a compliant to a rigid behaviour and vice versa. Due to its versatility and effectiveness, variable compliance has become the founding block of the design of the two devices in the robot arm physical structure. The novelty of the employment of variable compliance in this thesis resides in its use in both rigid and soft devices in order to help ensure both safety and adaptable grasping in one integrated physical structure, the robot arm.The safety device has been designed, modelled, produced, tested and physically embedded in the robot arm system. Compared to previous work in this field, the feature described in this thesis' work has a major advantage: its torque threshold can be actively regulated depending on the operational situation. The threshold torque is best described by an exponential curve in the mathematical model while it is best fit by a second order equation in the experimental data. The mismatch is more considerable for high values of threshold torque. However, both curves reflect that threshold torque magnitude increases by increasing the setting of the device. Testing of both the passive decoupling and active threshold torque regulation show that both are successfully obtained. The second novel feature of the robot arm is the soft grasping device inspired by hydrostatic skeletons. Its ability to passively adapts to complex shapes objects, reduces the complexity of the grasping action control. This gripper is low-cost, soft, cable-driven and it features no stiff sections. Its versatility, variable compliance and stable grasp are shown in several experiments. A model of the forward kinematics of the system is derived from observation of its bending behaviour.Variable compliance has shown to be a very relevant principle for the design and implementation of a robotic arm aimed at safely interacting with human users and that can reduce grasp control complexity by passively adapting to the object's shape

    A New Position Detection and Status Monitoring System for Joint of SCARA

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