12 research outputs found

    Adaptive Evolving Strategy for Dextrous Robotic Manipulation

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    A robot task can be represented as a set of trajectories conformed by a sequence of poses. In this way it is possible to teach a mobile robot to accomplish a manipulation task, and also to reproduce it. Nevertheless robot navigation may normally introduce inaccuracies in localization due to natural events as wheel-slides, causing a mismatch between the end-effector and the objects or tools the robot is supposed to interact with. We propose an algorithm for adapting manipulation trajectories for different locations. The adaptation is achieved by optimizing in position, orientation and energy consumption. The approach is built over the basis of Evolution Strategies, and only uses forward kinematics permitting to avoid all the inconveniences that inverse kinematics imply, as well as convergence problems in singular kinematic configurations. Manipulation paths generated with this algorithm can achieve optimal performance, sometimes even improving original path smoothness. Experimental results are presented to verify the algorithm.The research leading to these results has received funding from the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.Publicad

    ๊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•

    Two-stage trajectory optimization for autonomous ground vehicles parking maneuver

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    This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multi-layer optimization strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework

    Simulating a Flexible Robotic System based on Musculoskeletal Modeling

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    Humanoid robotics offers a unique research tool for understanding the human brain and body. The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research, with the recent advent of complex humanoid systems. This work presents the design and development of a new-generation bipedal robot. Its modeling and simulation has been realized by using an open-source software to create and analyze dynamic simulation of movement: OpenSim. Starting from a study by Fuben He, our model aims to be used as an innovative approach to the study of a such type of robot in which there are series elastic actuators represented by active and passive spring components in series with motors. It has provided of monoarticular and biarticular joint in a very similar manner to human musculoskeletal model. This thesis is only the starting point of a wide range of other possible future works: from the control structure completion and whole-body control application, to imitation learning and reinforcement learning for human locomotion, from motion test on at ground to motion test on rough ground, and obviously the transition from simulation to practice with a real elastic bipedal robot biologically-inspired that can move like a human bein

    Predictive Whole-Body Control of Humanoid Robot Locomotion

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    Humanoid robots are machines built with an anthropomorphic shape. Despite decades of research into the subject, it is still challenging to tackle the robot locomotion problem from an algorithmic point of view. For example, these machines cannot achieve a constant forward body movement without exploiting contacts with the environment. The reactive forces resulting from the contacts are subject to strong limitations, complicating the design of control laws. As a consequence, the generation of humanoid motions requires to exploit fully the mathematical model of the robot in contact with the environment or to resort to approximations of it. This thesis investigates predictive and optimal control techniques for tackling humanoid robot motion tasks. They generate control input values from the system model and objectives, often transposed as cost function to minimize. In particular, this thesis tackles several aspects of the humanoid robot locomotion problem in a crescendo of complexity. First, we consider the single step push recovery problem. Namely, we aim at maintaining the upright posture with a single step after a strong external disturbance. Second, we generate and stabilize walking motions. In addition, we adopt predictive techniques to perform more dynamic motions, like large step-ups. The above-mentioned applications make use of different simplifications or assumptions to facilitate the tractability of the corresponding motion tasks. Moreover, they consider first the foot placements and only afterward how to maintain balance. We attempt to remove all these simplifications. We model the robot in contact with the environment explicitly, comparing different methods. In addition, we are able to obtain whole-body walking trajectories automatically by only specifying the desired motion velocity and a moving reference on the ground. We exploit the contacts with the walking surface to achieve these objectives while maintaining the robot balanced. Experiments are performed on real and simulated humanoid robots, like the Atlas and the iCub humanoid robots

    Humanoid Robot Soccer Locomotion and Kick Dynamics: Open Loop Walking, Kicking and Morphing into Special Motions on the Nao Robot

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    Striker speed and accuracy in the RoboCup (SPL) international robot soccer league is becoming increasingly important as the level of play rises. Competition around the ball is now decided in a matter of seconds. Therefore, eliminating any wasted actions or motions is crucial when attempting to kick the ball. It is common to see a discontinuity between walking and kicking where a robot will return to an initial pose in preparation for the kick action. In this thesis we explore the removal of this behaviour by developing a transition gait that morphs the walk directly into the kick back swing pose. The solution presented here is targeted towards the use of the Aldebaran walk for the Nao robot. The solution we develop involves the design of a central pattern generator to allow for controlled steps with realtime accuracy, and a phase locked loop method to synchronise with the Aldebaran walk so that precise step length control can be activated when required. An open loop trajectory mapping approach is taken to the walk that is stabilized statically through the use of a phase varying joint holding torque technique. We also examine the basic princples of open loop walking, focussing on the commonly overlooked frontal plane motion. The act of kicking itself is explored both analytically and empirically, and solutions are provided that are versatile and powerful. Included as an appendix, the broader matter of striker behaviour (process of goal scoring) is reviewed and we present a velocity control algorithm that is very accurate and efficient in terms of speed of execution

    Learning-based methods for planning and control of humanoid robots

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    Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans. No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience. This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity. First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks. Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness. The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3

    From locomotion to cognition: Bridging the gap between reactive and cognitive behavior in a quadruped robot

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    The cognitivistic paradigm, which states that cognition is a result of computation with symbols that represent the world, has been challenged by many. The opponents have primarily criticized the detachment from direct interaction with the world and pointed to some fundamental problems (for instance the symbol grounding problem). Instead, they emphasized the constitutive role of embodied interaction with the environment. This has motivated the advancement of synthetic methodologies: the phenomenon of interest (cognition) can be studied by building and investigating whole brain-body-environment systems. Our work is centered around a compliant quadruped robot equipped with a multimodal sensory set. In a series of case studies, we investigate the structure of the sensorimotor space that the application of different actions in different environments by the robot brings about. Then, we study how the agent can autonomously abstract the regularities that are induced by the different conditions and use them to improve its behavior. The agent is engaged in path integration, terrain discrimination and gait adaptation, and moving target following tasks. The nature of the tasks forces the robot to leave the ``here-and-now'' time scale of simple reactive stimulus-response behaviors and to learn from its experience, thus creating a ``minimally cognitive'' setting. Solutions to these problems are developed by the agent in a bottom-up fashion. The complete scenarios are then used to illuminate the concepts that are believed to lie at the basis of cognition: sensorimotor contingencies, body schema, and forward internal models. Finally, we discuss how the presented solutions are relevant for applications in robotics, in particular in the area of autonomous model acquisition and adaptation, and, in mobile robots, in dead reckoning and traversability detection

    Understanding the fundamentals of bipedal locomotion in humans and robots

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    Walking is a robust and efficient method of moving around the world, which would greatly enhance the capabilities of humanoid robots, although they cannot match the performance of their biological counterparts. The highly nonlinear dynamics of locomotion create a vast state-action space, which makes model-based control difficult, yet biological humans are highly proficient and robust in their motion while operating under similar constraints. This disparity in performance naturally leads to the question: what can we learn about locomotion control by observing humans, and how can this be used to develop bio-inspired locomotion control in mechatronic humanoids? This thesis investigates bio-inspired locomotion control, but also explores the limitations of this approach and how we can use robotic platforms to move towards a better understanding of locomotion. We first present a methodology for measuring and analysing human locomotion behaviour, specifically disturbance recovery, and fit models to this complex behaviour to represent it in as simple as possible such that it can be easily translated into a simple controller for reactive motion. A minimum-jerk Model Predictive Control algorithm at the Centre of Mass (CoM) best captured human motion during multiple recovery strategies instead of using one controller for each strategy, which is common in this area. Capturing this simple CoM model of complex human behaviour shows that bio-inspiration can be an important tool for controller development, but behaviour varies between and even within individuals given similar initial conditions, which manifests as stochastic behaviour. Coupled with the ability to only measure expressed behaviours instead of direct control policies, this stochasticity presents a fundamental limit to using bio-inspiration for control purposes, as only indirect inferences can be made about a complex, stochastic system. To overcome these barriers, we investigate the use of mechatronic humanoid robots as a means to explore invariant aspects of the vast dynamic state-space of locomotion which are described by physical laws, and are therefore not subject to the stochastic behaviour of individual humans, that apply to both biological and mechatronic humanoid forms. We present a pipeline to explore the invariant energetics of humanoid robots during stepping for push recovery, where the most efficient stepping parameters are identified for a given initial CoM velocity and desired step length. Using this to explore the stepping state-space, our analysis finds a region of attraction between disturbance magnitude and optimal step length surrounded by a region of similarly efficient alternatives which corresponds to the stochastic behavior observed in humans during push recovery, which we would be unable to identify without reproducibility, direct access to internal measurements and known full body dynamics, which is not available in humans. We expand this paradigm further to investigate the invariant energetics of continuous walking using a full-body humanoid by exploring the state-space of step-length and step-timing to identify the most efficient sub-spaces of these parameters which describes the most efficient way to walk. Through analysis of this state-space, we provide evidence that the humanoid morphology exhibits a passive tendency towards energy-optimal motion and its dynamics follow a region of attraction towards Cost of Transport-optimal motion. Overall, these findings demonstrate the utility of robotics as a tool with which to explore certain aspects of legged locomotion and the results gained from our methodology suggest that humans do not need to explore a vast state-action space to learn to walk, they need only internalise simple heuristics for the natural dynamics of stepping that are easy to learn and can produce rapid, reactive and efficient stepping without costly decision-making processes
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