923 research outputs found

    Elastic Strips: A Framework for Motion Generation in Human Environments

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    Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection

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    In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation

    ๊ธฐ๊ตฌํ•™์  ๋ฐ ๋™์  ์ œํ•œ์กฐ๊ฑด๋“ค์„ ๊ณ ๋ คํ•œ ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์˜ ์ž‘์—… ์ค‘์‹ฌ ์ „์‹  ๋™์ž‘ ์ƒ์„ฑ ์ „๋žต

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œ์ „๊ณต), 2023. 2. ๋ฐ•์žฌํฅ.๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋Š” ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์— ์žฅ์ฐฉ๋œ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์ž…๋‹ˆ๋‹ค. ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋Š” ๊ณ ์ •ํ˜• ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์— ๋น„ํ•ด ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ์ด๋™์„ฑ์„ ์ œ๊ณต๋ฐ›๊ธฐ ๋•Œ๋ฌธ์— ๋‹ค์–‘ํ•˜๊ณ  ๋ณต์žกํ•œ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋‘ ๊ฐœ์˜ ์„œ๋กœ ๋‹ค๋ฅธ ์‹œ์Šคํ…œ์„ ๊ฒฐํ•ฉํ•จ์œผ๋กœ์จ ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์˜ ์ „์‹ ์„ ๊ณ„ํšํ•˜๊ณ  ์ œ์–ดํ•  ๋•Œ ์—ฌ๋Ÿฌ ํŠน์ง•์„ ๊ณ ๋ คํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํŠน์ง•๋“ค์€ ์—ฌ์ž์œ ๋„, ๋‘ ์‹œ์Šคํ…œ์˜ ๊ด€์„ฑ ์ฐจ์ด ๋ฐ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ๋น„ํ™€๋กœ๋…ธ๋ฏน ์ œํ•œ ์กฐ๊ฑด ๋“ฑ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ๋ชฉ์ ์€ ๊ธฐ๊ตฌํ•™์  ๋ฐ ๋™์  ์ œํ•œ์กฐ๊ฑด๋“ค์„ ๊ณ ๋ คํ•˜์—ฌ ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์˜ ์ „์‹  ๋™์ž‘ ์ƒ์„ฑ ์ „๋žต์„ ์ œ์•ˆํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๋จผ์ €, ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๊ฐ€ ์ดˆ๊ธฐ ์œ„์น˜์—์„œ ๋ฌธ์„ ํ†ต๊ณผํ•˜์—ฌ ๋ชฉํ‘œ ์œ„์น˜์— ๋„๋‹ฌํ•˜๊ธฐ ์œ„ํ•œ ์ „์‹  ๊ฒฝ๋กœ๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ด ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ๋กœ๋ด‡๊ณผ ๋ฌธ์— ์˜ํ•ด ์ƒ๊ธฐ๋Š” ๊ธฐ๊ตฌํ•™์  ์ œํ•œ์กฐ๊ฑด์„ ๊ณ ๋ คํ•ฉ๋‹ˆ๋‹ค. ์ œ์•ˆํ•˜๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ๋‘ ๋‹จ๊ณ„๋ฅผ ๊ฑฐ์ณ ์ „์‹ ์˜ ๊ฒฝ๋กœ๋ฅผ ์–ป์Šต๋‹ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” ๊ทธ๋ž˜ํ”„ ํƒ์ƒ‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ์ž์„ธ ๊ฒฝ๋กœ์™€ ๋ฌธ์˜ ๊ฐ๋„ ๊ฒฝ๋กœ๋ฅผ ๊ณ„์‚ฐํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ๊ทธ๋ž˜ํ”„ ํƒ์ƒ‰์—์„œ area indicator๋ผ๋Š” ์ •์ˆ˜ ๋ณ€์ˆ˜๋ฅผ ์ƒํƒœ์˜ ๊ตฌ์„ฑ ์š”์†Œ๋กœ์„œ ์ •์˜ํ•˜๋Š”๋ฐ, ์ด๋Š” ๋ฌธ์— ๋Œ€ํ•œ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ์ƒ๋Œ€์  ์œ„์น˜๋ฅผ ๋‚˜ํƒ€๋ƒ…๋‹ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ๊ฒฝ๋กœ์™€ ๋ฌธ์˜ ๊ฐ๋„๋ฅผ ํ†ตํ•ด ๋ฌธ์˜ ์†์žก์ด ์œ„์น˜๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ์—ญ๊ธฐ๊ตฌํ•™์„ ํ™œ์šฉํ•˜์—ฌ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์˜ ๊ด€์ ˆ ์œ„์น˜๋ฅผ ๊ณ„์‚ฐํ•ฉ๋‹ˆ๋‹ค. ์ œ์•ˆ๋œ ํ”„๋ ˆ์ž„์›Œํฌ์˜ ํšจ์œจ์„ฑ์€ ๋น„ํ™€๋กœ๋…ธ๋ฏน ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์‹ค์ œ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๋‘˜ ์งธ, ์ตœ์ ํ™” ๋ฐฉ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœํ•œ ์ „์‹  ์ œ์–ด๊ธฐ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๋“ฑ์‹ ๋ฐ ๋ถ€๋“ฑ์‹ ์ œํ•œ์กฐ๊ฑด ๋ชจ๋‘์— ๋Œ€ํ•ด ๊ฐ€์ค‘ ํ–‰๋ ฌ์„ ๋ฐ˜์˜ํ•œ ๊ณ„์ธต์  ์ตœ์ ํ™” ๋ฌธ์ œ์˜ ํ•ด๋ฅผ ๊ณ„์‚ฐํ•ฉ๋‹ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ ๋˜๋Š” ํœด๋จธ๋…ธ์ด๋“œ์™€ ๊ฐ™์ด ์ž์œ ๋„๊ฐ€ ๋งŽ์€ ๋กœ๋ด‡์˜ ์—ฌ์ž์œ ๋„๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ฐœ๋ฐœ๋˜์–ด ์ž‘์—… ์šฐ์„  ์ˆœ์œ„์— ๋”ฐ๋ผ ๊ฐ€์ค‘์น˜๊ฐ€ ๋‹ค๋ฅธ ๊ด€์ ˆ ๋™์ž‘์œผ๋กœ ์—ฌ๋Ÿฌ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๊ฐ€์ค‘ ํ–‰๋ ฌ์„ ์ตœ์ ํ™” ๋ฌธ์ œ์˜ 1์ฐจ ์ตœ์  ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜๋„๋ก ํ•˜๋ฉฐ, Active-set ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ๋“ฑ์‹ ๋ฐ ๋ถ€๋“ฑ์‹ ์ž‘์—…์„ ์ฒ˜๋ฆฌํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ๋Œ€์นญ์ ์ธ ์˜๊ณต๊ฐ„ ์‚ฌ์˜ ํ–‰๋ ฌ์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ณ„์‚ฐ์ƒ ํšจ์œจ์ ์ž…๋‹ˆ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, ์ œ์•ˆ๋œ ์ œ์–ด๊ธฐ๋ฅผ ํ™œ์šฉํ•˜๋Š” ๋กœ๋ด‡์€ ์šฐ์„  ์ˆœ์œ„์— ๋”ฐ๋ผ ๊ฐœ๋ณ„์ ์ธ ๊ด€์ ˆ ๊ฐ€์ค‘์น˜๋ฅผ ๋ฐ˜์˜ํ•˜์—ฌ ์ „์‹  ์›€์ง์ž„์„ ํšจ๊ณผ์ ์œผ๋กœ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ์ œ์•ˆ๋œ ์ œ์–ด๊ธฐ์˜ ํšจ์šฉ์„ฑ์€ ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์™€ ํœด๋จธ๋…ธ์ด๋“œ๋ฅผ ์ด์šฉํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์˜€์Šต๋‹ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ชจ๋ฐ”์ผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์˜ ๋™์  ์ œํ•œ์กฐ๊ฑด๋“ค ์ค‘ ํ•˜๋‚˜๋กœ์„œ ์ž๊ฐ€ ์ถฉ๋Œ ํšŒํ”ผ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์™€ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡ ๊ฐ„์˜ ์ž๊ฐ€ ์ถฉ๋Œ์— ์ค‘์ ์„ ๋‘ก๋‹ˆ๋‹ค. ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ๋ฒ„ํผ ์˜์—ญ์„ ๋‘˜๋Ÿฌ์‹ธ๋Š” 3์ฐจ์› ๊ณก๋ฉด์ธ distance buffer border์˜ ๊ฐœ๋…์„ ์ •์˜ํ•ฉ๋‹ˆ๋‹ค. ๋ฒ„ํผ ์˜์—ญ์˜ ๋‘๊ป˜๋Š” ๋ฒ„ํผ ๊ฑฐ๋ฆฌ์ž…๋‹ˆ๋‹ค. ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์™€ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡ ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ๊ฐ€ ๋ฒ„ํผ ๊ฑฐ๋ฆฌ๋ณด๋‹ค ์ž‘์€ ๊ฒฝ์šฐ, ์ฆ‰ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๊ฐ€ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ๋ฒ„ํผ ์˜์—ญ ๋‚ด๋ถ€์— ์žˆ๋Š” ๊ฒฝ์šฐ ์ œ์•ˆ๋œ ์ „๋žต์€ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋ฅผ ๋ฒ„ํผ ์˜์—ญ ๋ฐ–์œผ๋กœ ๋‚ด๋ณด๋‚ด๊ธฐ ์œ„ํ•ด ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ์›€์ง์ž„์„ ์ƒ์„ฑํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋Š” ๋ฏธ๋ฆฌ ์ •์˜๋œ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์˜ ์›€์ง์ž„์„ ์ˆ˜์ •ํ•˜์ง€ ์•Š๊ณ ๋„ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡๊ณผ์˜ ์ž๊ฐ€ ์ถฉ๋Œ์„ ํ”ผํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์˜ ์›€์ง์ž„์€ ๊ฐ€์ƒ์˜ ํž˜์„ ๊ฐ€ํ•จ์œผ๋กœ์จ ์ƒ์„ฑ๋ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ํž˜์˜ ๋ฐฉํ–ฅ์€ ์ฐจ๋™ ๊ตฌ๋™ ์ด๋™ ๋กœ๋ด‡์˜ ๋น„ํ™€๋กœ๋…ธ๋ฏน ์ œ์•ฝ ๋ฐ ์กฐ์ž‘๊ธฐ์˜ ๋„๋‹ฌ ๊ฐ€๋Šฅ์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๊ฒฐ์ •๋ฉ๋‹ˆ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ 7์ž์œ ๋„ ๋กœ๋ด‡ํŒ”์„ ๊ฐ€์ง„ ์ฐจ๋™ ๊ตฌ๋™ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡์— ์ ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์‹คํ—˜ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ์ž…์ฆ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.A mobile manipulator is a manipulator mounted on a mobile robot. Compared to a fixed-base manipulator, the mobile manipulator can perform various and complex tasks because the mobility is offered by the mobile robot. However, combining two different systems causes several features to be considered when generating the whole-body motion of the mobile manipulator. The features include redundancy, inertia difference, and non-holonomic constraint. The purpose of this thesis is to propose the whole-body motion generation strategy of the mobile manipulator for considering kinematic and dynamic constraints. First, a planning framework is proposed that computes a path for the whole-body configuration of the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The framework handles the kinematic constraint imposed by the closed-chain between the robot and door. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called area indicator is introduced as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the inverse kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a nonholonomic mobile manipulator. Second, a whole-body controller is presented based on the optimization method that can consider both equality and inequality constraints. The method computes the optimal solution of the weighted hierarchical optimization problem. The method is developed to resolve the redundancy of robots with a large number of Degrees of Freedom (DOFs), such as a mobile manipulator or a humanoid, so that they can execute multiple tasks with differently weighted joint motion for each task priority. The proposed method incorporates the weighting matrix into the first-order optimality condition of the optimization problem and leverages an active-set method to handle equality and inequality constraints. In addition, it is computationally efficient because the solution is calculated in a weighted joint space with symmetric null-space projection matrices for propagating recursively to a low priority task. Consequently, robots that utilize the proposed controller effectively show whole-body motions handling prioritized tasks with differently weighted joint spaces. The effectiveness of the proposed controller was validated through experiments with a nonholonomic mobile manipulator as well as a humanoid. Lastly, as one of dynamic constraints for the mobile manipulator, a reactive self-collision avoidance algorithm is developed. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface enclosing a buffer region of the mobile robot. The region has the thickness equal to buffer distance. When the distance between the manipulator and mobile robot is less than the buffer distance, i.e. the manipulator lies inside the buffer region of the mobile robot, the proposed strategy is to move the mobile robot away from the manipulator in order for the manipulator to be placed outside the border of the region, the DBB. The strategy is achieved by exerting force on the mobile robot. Therefore, the manipulator can avoid self-collision with the mobile robot without modifying the predefined motion of the manipulator in a world Cartesian coordinate frame. In particular, the direction of the force is determined by considering the non-holonomic constraint of the differentially driven mobile robot. Additionally, the reachability of the manipulator is considered to arrive at a configuration in which the manipulator can be more maneuverable. To realize the desired force and resulting torque, an avoidance task is constructed by converting them into the accelerations of the mobile robot and smoothly inserted with a top priority into the controller. The proposed algorithm was implemented on a differentially driven mobile robot with a 7-DOFs robotic arm and its performance was demonstrated in various experimental scenarios.1 INTRODUCTION 1 1.1 Motivation 1 1.2 Contributions of thesis 2 1.3 Overview of thesis 3 2 WHOLE-BODY MOTION PLANNER : APPLICATION TO NAVIGATION INCLUDING DOOR TRAVERSAL 5 2.1 Background & related works 7 2.2 Proposed framework 9 2.2.1 Computing path for mobile robot and door angle - S1 10 2.2.1.1 State 10 2.2.1.2 Action 13 2.2.1.3 Cost 15 2.2.1.4 Search 18 2.2.2 Computing path for arm configuration - S2 20 2.3 Results 21 2.3.1 Application to pull and push-type door 21 2.3.2 Experiment in cluttered environment 22 2.3.3 Experiment with different robot platform 23 2.3.4 Comparison with separate planning by existing works 24 2.3.5 Experiment with real robot 29 2.4 Conclusion 29 3 WHOLE-BODY CONTROLLER : WEIGHTED HIERARCHICAL QUADRATIC PROGRAMMING 31 3.1 Related works 32 3.2 Problem statement 34 3.2.1 Pseudo-inverse with weighted least-squares norm for each task 35 3.2.2 Problem statement 37 3.3 WHQP with equality constraints 37 3.4 WHQP with inequality constraints 45 3.5 Experimental results 48 3.5.1 Simulation experiment with nonholonomic mobile manipulator 48 3.5.1.1 Scenario description 48 3.5.1.2 Task and weighting matrix description 49 3.5.1.3 Results 51 3.5.2 Real experiment with nonholonomic mobile manipulator 53 3.5.2.1 Scenario description 53 3.5.2.2 Task and weighting matrix description 53 3.5.2.3 Results 54 3.5.3 Real experiment with humanoid 55 3.5.3.1 Scenario description 55 3.5.3.2 Task and weighting matrix description 55 3.5.3.3 Results 57 3.6 Discussions and implementation details 57 3.6.1 Computation cost 57 3.6.2 Composite weighting matrix in same hierarchy 59 3.6.3 Nullity of WHQP 59 3.7 Conclusion 59 4 WHOLE-BODY CONSTRAINT : SELF-COLLISION AVOIDANCE 61 4.1 Background & related Works 64 4.2 Distance buffer border and its score computation 65 4.2.1 Identification of potentially colliding link pairs 66 4.2.2 Distance buffer border 67 4.2.3 Evaluation of distance buffer border 69 4.2.3.1 Singularity of the differentially driven mobile robot 69 4.2.3.2 Reachability of the manipulator 72 4.2.3.3 Score of the DBB 74 4.3 Self-collision avoidance algorithm 75 4.3.1 Generation of the acceleration for the mobile robot 76 4.3.2 Generation of the repulsive acceleration for the other link pair 82 4.3.3 Construction of an acceleration-based task for self-collision avoidance 83 4.3.4 Insertion of the task in HQP-based controller 83 4.4 Experimental results 86 4.4.1 System overview 87 4.4.2 Experimental results 87 4.4.2.1 Self-collision avoidance while tracking the predefined trajectory 87 4.4.2.2 Self-collision avoidance while manually guiding the end-effector 89 4.4.2.3 Extension to obstacle avoidance when opening the refrigerator 91 4.4.3 Discussion 94 4.5 Conclusion 95 5 CONCLUSIONS 97 Abstract (In Korean) 113 Acknowlegement 116๋ฐ•

    Enhanced online programming for industrial robots

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    The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal

    Human-friendly robotic manipulators: safety and performance issues in controller design

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    Recent advances in robotics have spurred its adoption into new application areas such as medical, rescue, transportation, logistics, personal care and entertainment. In the personal care domain, robots are expected to operate in human-present environments and provide non-critical assistance. Successful and flourishing deployment of such robots present different opportunities as well as challenges. Under a national research project, Bobbie, this dissertation analyzes challenges associated with these robots and proposes solutions for identified problems. The thesis begins by highlighting the important safety concern and presenting a comprehensive overview of safety issues in a typical domestic robot system. By using functional safety concept, the overall safety of the complex robotic system was analyzed through subsystem level safety issues. Safety regions in the world model of the perception subsystem, dependable understanding of the unstructured environment via fusion of sensory subsystems, lightweight and compliant design of mechanical components, passivity based control system and quantitative metrics used to assert safety are some important points discussed in the safety review. The main research focus of this work is on controller design of robotic manipulators against two conflicting requirements: motion performance and safety. Human-friendly manipulators used on domestic robots exhibit a lightweight design and demand a stable operation with a compliant behavior injected via a passivity based impedance controller. Effective motion based manipulation using such a controller requires a highly stiff behavior while important safety requirements are achieved with compliant behaviors. On the basis of this intuitive observation, this research identifies suitable metrics to identify the appropriate impedance for a given performance and safety requirement. This thesis also introduces a domestic robot design that adopts a modular design approach to minimize complexity, cost and development time. On the basis of functional modularity concept where each module has a unique functional contribution in the system, the robot โ€œBobbie-UTโ€ฟ is built as an interconnection of interchangeable mobile platform, torso, robotic arm and humanoid head components. Implementation of necessary functional and safety requirements, design of interfaces and development of suitable software architecture are also discussed with the design

    Magneto-Rheological Actuators for Human-Safe Robots: Modeling, Control, and Implementation

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    In recent years, research on physical human-robot interaction has received considerable attention. Research on this subject has led to the study of new control and actuation mechanisms for robots in order to achieve intrinsic safety. Naturally, intrinsic safety is only achievable in kinematic structures that exhibit low output impedance. Existing solutions for reducing impedance are commonly obtained at the expense of reduced performance, or significant increase in mechanical complexity. Achieving high performance while guaranteeing safety seems to be a challenging goal that necessitates new actuation technologies in future generations of human-safe robots. In this study, a novel two degrees-of-freedom safe manipulator is presented. The manipulator uses magneto-rheological fluid-based actuators. Magneto-rheological actuators offer low inertia-to-torque and mass-to-torque ratios which support their applications in human-friendly actuation. As a key element in the design of the manipulator, bi-directional actuation is attained by antagonistically coupling MR actuators at the joints. Antagonistically coupled MR actuators at the joints allow using a single motor to drive multiple joints. The motor is located at the base of the manipulator in order to further reduce the overall weight of the robot. Due to the unique characteristic of MR actuators, intrinsically safe actuation is achieved without compromising high quality actuation. Despite these advantages, modeling and control of MR actuators present some challenges. The antagonistic configuration of MR actuators may result in limit cycles in some cases when the actuator operates in the position control loop. To study the possibility of limit cycles, describing function method is employed to obtain the conditions under which limit cycles may occur in the operation of the system. Moreover, a connection between the amplitude and the frequency of the potential limit cycles and the system parameters is established to provide an insight into the design of the actuator as well as the controller. MR actuators require magnetic fields to control their output torques. The application of magnetic field however introduces hysteresis in the behaviors of MR actuators. To this effect, an adaptive model is developed to estimate the hysteretic behavior of the actuator. The effectiveness of the model is evaluated by comparing its results with those obtained using the Preisach model. These results are then extended to an adaptive control scheme in order to compensate for the effect of hysteresis. In both modeling and control, stability of proposed schemes are evaluated using Lyapunov method, and the effectiveness of the proposed methods are validated with experimental results

    Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

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    L'abstract รจ presente nell'allegato / the abstract is in the attachmen

    Mechatronics versus Robotics

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    In Bolton, mechatronics is defined as the integration of electronics, control engineering, and mechanical engineering, thus recognizing the fundamental role of control in joining electronics and mechanics. A robot is commonly considered as a typical mechatronic system, which integrates software, control, electronics, and mechanical designs in a synergistic manner. Robotics can be considered as a part of mechatronics; i.e., all robots are mechatronic systems, but not all mechatronic systems are robots. Advanced robots usually plan their actions by combining an assigned functional task with the knowledge about the environment in which they operate. By using a simplified approach, advanced robots could be defined as mechatronic devices governed by a smart brain, placed at a higher hierarchical level. Actuators are building blocks of any mechatronic system. Such systems, however, have a huge application span, ranging from low-cost consumer applications to high-end, high-precision industrial manufacturing equipment
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