2,703 research outputs found

    Whole-Body MPC for a Dynamically Stable Mobile Manipulator

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    Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening

    Overcoming barriers and increasing independence: service robots for elderly and disabled people

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    This paper discusses the potential for service robots to overcome barriers and increase independence of elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly people and advances in technology which will make new uses possible and provides suggestions for some of these new applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses the complementarity of assistive service robots and personal assistance and considers the types of applications and users for which service robots are and are not suitable

    Reuleaux: Robot Base Placement by Reachability Analysis

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    Before beginning any robot task, users must position the robot's base, a task that now depends entirely on user intuition. While slight perturbation is tolerable for robots with moveable bases, correcting the problem is imperative for fixed-base robots if some essential task sections are out of reach. For mobile manipulation robots, it is necessary to decide on a specific base position before beginning manipulation tasks. This paper presents Reuleaux, an open source library for robot reachability analyses and base placement. It reduces the amount of extra repositioning and removes the manual work of identifying potential base locations. Based on the reachability map, base placement locations of a whole robot or only the arm can be efficiently determined. This can be applied to both statically mounted robots, where position of the robot and work piece ensure the maximum amount of work performed, and to mobile robots, where the maximum amount of workable area can be reached. Solutions are not limited only to vertically constrained placement, since complicated robotics tasks require the base to be placed at unique poses based on task demand. All Reuleaux library methods were tested on different robots of different specifications and evaluated for tasks in simulation and real world environment. Evaluation results indicate that Reuleaux had significantly improved performance than prior existing methods in terms of time-efficiency and range of applicability.Comment: Submitted to International Conference of Robotic Computing 201

    Motion primitive based random planning for loco-manipulation tasks

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    Several advanced control laws are available for complex robotic systems such as humanoid robots and mobile manipulators. Controls are usually developed for locomotion or for manipulation purposes. Resulting motions are usually executed sequentially and the potentiality of the robotic platform is not fully exploited. In this work we consider the problem of loco-manipulation planning for a robot with given parametrized control laws known as primitives. Such primitives, may have not been designed to be executed simultaneously and by composing them instability may easily arise. With the proposed approach, primitives combination that guarantee stability of the system are obtained resulting in complex whole-body behavior. A formal definition of motion primitives is provided and a random sampling approach on a manifold with limited dimension is investigated. Probabilistic completeness and asymptotic optimality are also proved. The proposed approach is tested both on a mobile manipulator and on the humanoid robot Walk-Man, performing loco-manipulation tasks

    ๊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•
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