2,126 research outputs found

    A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots

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    Re-configurable modular robotic (RMR) systems are advantageous for their reconfigurability and versatility. A new modular robot can be built for a specific task by using modules as building blocks. However, constructing a kinematic model for a newly conceived robot requires significant work. Due to the finite size of module-types, models of all module-types can be built individually and stored in a database beforehand. With this priori knowledge, the model construction process can be automated by detecting the modules and their corresponding interconnections. Previous literature proposed theoretical frameworks for constructing kinematic models of modular robots, assuming that such information was known a priori. While well-devised mechanisms and built-in sensors can be employed to detect these parameters automatically, they significantly complicate the module design and thus are expensive. In this paper, we propose a vision-based method to identify kinematic chains and automatically construct robot models for modular robots. Each module is affixed with augmented reality (AR) tags that are encoded with unique IDs. An image of a modular robot is taken and the detected modules are recognized by querying a database that maintains all module information. The poses of detected modules are used to compute: (i) the connection between modules and (ii) joint angles of joint-modules. Finally, the robot serial-link chain is identified and the kinematic model constructed and visualized. Our experimental results validate the effectiveness of our approach. While implementation with only our RMR is shown, our method can be applied to other RMRs where self-identification is not possible

    Generalized approach to the modelling of modular machines

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    This paper describes a method of graphically simulating modular machines within a computer aided design environment. This forms part of a much larger Science and Engineering Research Council (SERC) funded programme aimed at advancing modern practices when designing and building manufacturing machines. A generalized approach to the synthesis of the generic features of various kinematic motion pairs is presented and prismatic and revolute motion primitives generalized in their functional and geometric aspects. A hierarchical ring and tree data structure has been designed and implemented to comprehensively represent these motion pairs and to simulate their performance. More complex modular manufacturing machines can be represented using information from a library of up to three degree of freedom motion modules. Seven two degree of freedom motion primitives and twelve three degree of freedom motion primitives with articulation configurations have been analyzed and included in the motion primitive library. The configuration of modular machines comprised of physically separate but logically connected distributed motion primitives are described. Examples of a two-finger industrial robot gripper and a three-finger industrial robot hand are used to demonstrate the general principles

    Graphical modelling of modular machines

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    This research is aimed at advancing machine design through specifying and implementing (in "proof of concept" form) a set of tools which graphically model modular machines. The tools allow mechanical building elements (or machine modules) to be selected and configured together in a highly flexible manner so that operation of the chosen configuration can be simulated and performance properties evaluated. Implementation of the tools has involved an extension in capability of a proprietary robot simulation system. This research has resulted in a general approach to graphically modelling manufacturing machines built from modular elements. A focus of study has been on a decomposition of machine functionality leading to the establishment of a library of modular machine primitives. This provides a useful source of commonly required machine building elements for use by machine designers. Study has also focussed on the generation of machine configuration tools which facilitate the construction of a simulation model and ultimately the physical machine itself. Simulation aspects of machine control are also considered which depict methods of manipulating a machine model in the simulation phase. In addition methods of achieving machine programming have been considered which specify the machine and its operational tasks. Means of adopting common information data structures are also considered which can facilitate interfacing with other systems, including the physical machine system constructed as an issue of the simulation phase. Each of these study areas is addressed in its own context, but collectively they provide a means of creating a complete modular machine design environment which can provide significant assistance to machine designers. Part of the methodology employed in the study is based on the use of the discrete event simulation technique. To easily and effectively describe a modular machine and its activity in a simulation model, a hierarchical ring and tree data structure has been designed and implemented. The modularity and reconfigurability are accommodated by the data structure, and homogeneous transformations are adopted to determine the spatial location and orientation of each of the machine elements. A three-level machine task programming approach is used to describe the machine's activities. A common data format method is used to interface the machine design environment with the physical machine and other building blocks of manufacturing systems (such as CAD systems) where systems integration approaches can lead to enhanced product realisation. The study concludes that a modular machine design environment can be created by employing the graphical simulation approach together with a set of comprehensive configuration. tools. A generic framework has been derived which outlines the way in which machine design environments can be constructed and suggestions are made as to how the proof of concept design environment implemented in this study can be advanced

    ์•ˆ์ „ํ•œ ์žฌ๊ตฌ์„ฑ ๋กœ๋ด‡ ์‹œ์Šคํ…œ: ์„ค๊ณ„, ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๋ฐ ๋ฐ˜์‘ํ˜• ๊ฒฝ๋กœ๊ณ„ํš

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2020. 8. ๋ฐ•์ข…์šฐ.The next generation of robots are being asked to work in close proximity to humans. At the same time, the robot should have the ability to change its topology to flexibly cope with various tasks. To satisfy these two requirements, we propose a novel modular reconfi gurable robot and accompanying software architecture, together with real-time motion planning algorithms to allow for safe operation in unstructured dynamic environments with humans. Two of the key innovations behind our modular manipulator design are a genderless connector and multi-dof modules. By making the modules connectable regardless of the input/output directions, a genderless connector increases the number of possible connections. The developed genderless connector can transmit as much load as necessary to an industrial robot. In designing two-dof modules, an offset between two joints is imposed to improve the overall integration and the safety of the modules. To cope with the complexity in modeling due to the genderless connector and multi-dof modules, a programming architecture for modular robots is proposed. The key feature of the proposed architecture is that it efficiently represents connections of multi-dof modules only with connections between modules, while existing architectures should explicitly represent all connections between links and joints. The data structure of the proposed architecture contains properties of tree-structured multi-dof modules with intra-module relations. Using the data structure and connection relations between modules, kinematic/dynamic parameters of connected modules can be obtained through forward recursion. For safe operation of modular robots, real-time robust collision avoidance algorithms for kinematic singularities are proposed. The main idea behind the algorithms is generating control inputs that increase the directional manipulability of the robot to the object direction by reducing directional safety measures. While existing directional safety measures show undesirable behaviors in the vicinity of the kinematic singularities, the proposed geometric safety measure generates stable control inputs in the entire joint space. By adding the preparatory input from the geometric safety measure to the repulsive input, a hierarchical collision avoidance algorithm that is robust to kinematic singularity is implemented. To mathematically guarantee the safety of the robot, another collision avoidance algorithm using the invariance control framework with velocity-dependent safety constraints is proposed. When the object approached the robot from a singular direction, the safety constraints are not satis ed in the initial state of the robot and the safety cannot be guaranteed using the invariance control. By proposing a control algorithm that quickly decreases the preparatory constraints below thresholds, the robot re-enters the constraint set and avoids collisions using the invariance control framework. The modularity and safety of the developed reconfi gurable robot is validated using a set of simulations and hardware experiments. The kinematic/dynamic model of the assembled robot is obtained in real-time and used to accurately control the robot. Due to the safe design of modules with o sets and the high-level safety functions with collision avoidance algorithms, the developed recon figurable robot has a broader safe workspace and wider ranger of safe operation speed than those of cooperative robots.๋‹ค์Œ ์„ธ๋Œ€์˜ ๋กœ๋ด‡์€ ์‚ฌ๋žŒ๊ณผ ๊ฐ€๊นŒ์ด์—์„œ ํ˜‘์—…ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋Šฅ์„ ๊ฐ€์ ธ์•ผํ•œ๋‹ค. ๊ทธ์™€ ๋™์‹œ์—, ๋กœ๋ด‡์€ ๋‹ค์–‘ํ•˜๊ฒŒ ๋ณ€ํ•˜๋Š” ์ž‘์—…์— ๋Œ€ํ•ด ์œ ์—ฐํ•˜๊ฒŒ ๋Œ€์ฒ˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ž์‹ ์˜ ๊ตฌ์กฐ๋ฅผ ๋ฐ”๊พธ๋Š” ๊ธฐ๋Šฅ์„ ๊ฐ€์ ธ์•ผ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋‘ ๊ฐ€์ง€ ์š”๊ตฌ์กฐ๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์œ„ํ•ด, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ƒˆ๋กœ์šด ๋ชจ๋“ˆ๋ผ ๋กœ๋ด‡ ์‹œ์Šคํ…œ๊ณผ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์•„ํ‚คํ…์ณ๋ฅผ ์ œ์‹œํ•˜๊ณ , ์‚ฌ๋žŒ์ด ์กด์žฌํ•˜๋Š” ๋™์  ํ™˜๊ฒฝ์—์„œ ์•ˆ์ „ํ•œ ๋กœ๋ด‡์˜ ์šด์šฉ์„ ์œ„ํ•œ ์‹ค์‹œํ•œ ๊ฒฝ๋กœ ๊ณ„ํš ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•œ๋‹ค. ๊ฐœ๋ฐœ๋œ ๋ชจ๋“ˆ๋ผ ๋กœ๋ด‡์˜ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ์ ์ธ ํ˜์‹ ์„ฑ์€ ๋ฌด์„ฑ๋ณ„ ์ปค๋„ฅํ„ฐ์™€ ๋‹ค์ž์œ ๋„ ๋ชจ๋“ˆ์—์„œ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ์ž…๋ ฅ/์ถœ๋ ฅ ๋ฐฉํ–ฅ์— ์ƒ๊ด€ ์—†์ด ๋ชจ๋“ˆ์ด ์—ฐ๊ฒฐ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•จ์œผ๋กœ์จ, ๋ฌด์„ฑ๋ณ„ ์ปค๋„ฅํ„ฐ๋Š” ๊ฒฐํ•ฉ ๊ฐ€๋Šฅํ•œ ๊ฒฝ์šฐ์˜ ์ˆ˜๋ฅผ ๋Š˜๋ฆด ์ˆ˜ ์žˆ๋‹ค. ๊ฐœ๋ฐœ๋œ ๋ฌด์„ฑ๋ณ„ ์ปค๋„ฅํ„ฐ๋Š” ์‚ฐ์—…์šฉ ๋กœ๋ด‡์—์„œ ์š”๊ตฌ๋˜๋Š” ์ถฉ๋ถ„ํ•œ ๋ถ€ํ•˜๋ฅผ ๊ฒฌ๋”œ ์ˆ˜ ์žˆ๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. 2 ์ž์œ ๋„ ๋ชจ๋“ˆ์˜ ์„ค๊ณ„์—์„œ ๋‘ ์ถ• ์‚ฌ์ด์— ์˜คํ”„์…‹์„ ๊ฐ€์ง€๋„๋ก ํ•จ์œผ๋กœ์จ ์ „์ฒด์ ์ธ ์™„์„ฑ๋„ ๋ฐ ์•ˆ์ „๋„๋ฅผ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ๋ฌด์„ฑ๋ณ„ ์ปค๋„ฅํ„ฐ์™€ ๋‹ค์ž์œ ๋„ ๋ชจ๋“ˆ๋กœ ์ธํ•œ ๋ชจ๋ธ๋ง์˜ ๋ณต์žก์„ฑ์— ๋Œ€์‘ํ•˜๊ธฐ ์œ„ํ•ด, ์ผ๋ฐ˜์ ์ธ ๋ชจ๋“ˆ๋ผ ๋กœ๋ด‡์„ ์œ„ํ•œ ์†Œํ”„ํŠธ์›จ์–ด ์•„ํ‚คํ…์ณ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ธฐ์กด ๋ชจ๋“ˆ๋ผ ๋กœ๋ด‡์˜ ์—ฐ๊ฒฐ์„ ๋‚˜ํƒ€๋‚ด๋Š” ๋ฐฉ๋ฒ•์ด ๋ชจ๋“  ๋งํฌ์™€ ์กฐ์ธํŠธ ์‚ฌ์ด์˜ ์—ฐ๊ฒฐ ๊ด€๊ณ„๋ฅผ ๋ณ„๋„๋กœ ๋‚˜ํƒ€๋‚ด์•ผํ•˜๋Š” ๊ฒƒ๊ณผ ๋‹ค๋ฅด๊ฒŒ, ์ œ์•ˆ๋œ ์•„ํ‚คํ…์ณ๋Š” ๋ชจ๋“ˆ๋“ค ์‚ฌ์ด์˜ ์—ฐ๊ฒฐ๊ด€๊ณ„๋งŒ์„ ๋‚˜ํƒ€๋ƒ„์œผ๋กœ์จ ํšจ์œจ์ ์ธ ๋‹ค์ž์œ ๋„ ๋ชจ๋“ˆ์˜ ์—ฐ๊ฒฐ๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ํŠน์ง•์œผ๋กœ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํŠธ๋ฆฌ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๋Š” ์ผ๋ฐ˜์ ์ธ ๋‹ค์ž์œ ๋„ ๋ชจ๋“ˆ์˜ ์„ฑ์งˆ์„ ๋‚˜ํƒ€๋‚ด๋Š” ๋ฐ์ดํ„ฐ ๊ตฌ์กฐ๋ฅผ ์ •์˜ํ•˜์˜€๋‹ค. ๋ชจ๋“ˆ๋“ค ์‚ฌ์ด์˜ ์—ฐ๊ฒฐ๊ด€๊ณ„ ๋ฐ ๋ฐ์ดํ„ฐ ๊ตฌ์กฐ๋ฅผ ์ด์šฉํ•˜์—ฌ, ์ •ํ™•ํ•œ ๊ธฐ๊ตฌํ•™/๋™์—ญํ•™ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์–ป์–ด๋‚ด๋Š” ์ˆœ๋ฐฉํ–ฅ ์žฌ๊ท€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๋ชจ๋“ˆ๋ผ ๋กœ๋ด‡์˜ ์•ˆ์ „ํ•œ ์šด์šฉ์„ ์œ„ํ•ด, ๊ธฐ๊ตฌํ•™์  ํŠน์ด์ ์— ๊ฐ•๊ฑดํ•œ ์‹ค์‹œ๊ฐ„ ์ถฉ๋ŒํšŒํ”ผ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ฐฉํ–ฅ์„ฑ ์•ˆ์ „๋„๋ฅผ ์ค„์ด๋Š” ๋ฐฉํ–ฅ์˜ ์ œ์–ด ์ž…๋ ฅ์„ ์ƒ์„ฑํ•˜์—ฌ ๋ฌผ์ฒด ๋ฐฉํ–ฅ์œผ๋กœ์˜ ๋กœ๋ด‡ ๋ฐฉํ–ฅ์„ฑ ๋งค๋‹ˆํ“ฐ๋Ÿฌ๋นŒ๋ฆฌํ‹ฐ๋ฅผ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ๊ฒƒ์ด ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ•ต์‹ฌ์ด๋‹ค. ๊ธฐ์กด์˜ ๋ฐฉํ–ฅ์„ฑ ์•ˆ์ „๋„๊ฐ€ ๊ธฐ๊ตฌํ•™์  ํŠน์ด์  ๊ทผ์ฒ˜์—์„œ ์›ํ•˜์ง€ ์•Š๋Š” ์„ฑ์งˆ์„ ๊ฐ€์ง€๋Š” ๊ฒƒ๊ณผ๋Š” ๋ฐ˜๋Œ€๋กœ, ์ œ์•ˆํ•œ ๊ธฐํ•˜ํ•™์  ์•ˆ์ „๋„๋Š” ์ „์ฒด ์กฐ์ธํŠธ ๊ณต๊ฐ„์—์„œ ์•ˆ์ •์ ์ธ ์ œ์–ด ์ž…๋ ฅ์„ ์ƒ์„ฑํ•œ๋‹ค. ์ด ๊ธฐํ•˜ํ•™์  ์•ˆ์ „๋„๋ฅผ ์ด์šฉํ•˜์—ฌ, ๊ธฐ๊ตฌํ•™์  ํŠน์ด์ ์— ๊ฐ•๊ฑดํ•œ ๊ณ„์ธต์  ์ถฉ๋ŒํšŒํ”ผ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ์ˆ˜ํ•™์ ์œผ๋กœ ๋กœ๋ด‡์˜ ์•ˆ์ „๋„๋ฅผ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•ด, ์ƒ๋Œ€์†๋„์— ์ข…์†์ ์ธ ์•ˆ์ „ ์ œ์•ฝ์กฐ๊ฑด์„ ๊ฐ€์ง€๋Š” ๋ถˆ๋ณ€ ์ œ์–ด ํ”„๋ ˆ์ž„์›Œํฌ์„ ์ด์šฉํ•˜์—ฌ ๋˜ ํ•˜๋‚˜์˜ ์ถฉ๋Œ ํšŒํ”ผ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ฌผ์ฒด๊ฐ€ ํŠน์ด์  ๋ฐฉํ–ฅ์œผ๋กœ๋ถ€ํ„ฐ ๋กœ๋ด‡์— ์ ‘๊ทผํ•  ๋•Œ, ๋กœ๋ด‡์˜ ์ดˆ๊ธฐ ์ƒํƒœ์—์„œ ์•ˆ์ „ ์ œ์•ฝ์กฐ๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค์ง€ ๋ชปํ•˜๊ฒŒ ๋˜์–ด ๋ถˆ๋ณ€์ œ์–ด๋ฅผ ์ ์šฉํ•  ์ˆ˜ ์—†๊ฒŒ ๋œ๋‹ค. ์ค€๋น„ ์ œ์•ฝ์กฐ๊ฑด์„ ๋น ๋ฅด๊ฒŒ ์ž„๊ณ„์  ์•„๋ž˜๋กœ ๊ฐ์†Œ์‹œํ‚ค๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•จ์œผ๋กœ์จ, ๋กœ๋ด‡์€ ์ œ์•ฝ์กฐ๊ฑด ์ง‘ํ•ฉ์— ๋‹ค์‹œ ๋“ค์–ด๊ฐ€๊ณ  ๋ถˆ๋ณ€ ์ œ์–ด ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์ถฉ๋Œ์„ ํšŒํ”ผํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ๋‹ค. ๊ฐœ๋ฐœ๋œ ์žฌ๊ตฌ์„ฑ ๋กœ๋ด‡์˜ ๋ชจ๋“ˆ๋ผ๋ฆฌํ‹ฐ์™€ ์•ˆ์ „๋„๋Š” ์ผ๋ จ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ํ•˜๋“œ์›จ์–ด ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. ์‹ค์‹œ๊ฐ„์œผ๋กœ ์กฐ๋ฆฝ๋œ ๋กœ๋ด‡์˜ ๊ธฐ๊ตฌํ•™/๋™์—ญํ•™ ๋ชจ๋ธ์„ ์–ป์–ด๋‚ด ์ •๋ฐ€ ์ œ์–ด์— ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์•ˆ์ „ํ•œ ๋ชจ๋“ˆ ๋””์ž์ธ๊ณผ ์ถฉ๋Œ ํšŒํ”ผ ๋“ฑ์˜ ๊ณ ์ฐจ์› ์•ˆ์ „ ๊ธฐ๋Šฅ์„ ํ†ตํ•˜์—ฌ, ๊ฐœ๋ฐœ๋œ ์žฌ๊ตฌ์„ฑ ๋กœ๋ด‡์€ ๊ธฐ์กด ํ˜‘๋™๋กœ๋ด‡๋ณด๋‹ค ๋„“์€ ์•ˆ์ „ํ•œ ์ž‘์—…๊ณต๊ฐ„๊ณผ ์ž‘์—…์†๋„๋ฅผ ๊ฐ€์ง„๋‹ค.1 Introduction 1 1.1 Modularity and Recon gurability 1 1.2 Safe Interaction 4 1.3 Contributions of This Thesis 9 1.3.1 A Recon gurable Modular Robot System with Bidirectional Modules 9 1.3.2 A Modular Robot Software Programming Architecture 10 1.3.3 Anticipatory Collision Avoidance Planning 11 1.4 Organization of This Thesis 14 2 Design and Prototyping of the ModMan 17 2.1 Genderless Connector 18 2.2 Modules for ModMan 21 2.2.1 Joint Modules 21 2.2.2 Link and Gripper Modules 25 2.3 Experiments 26 2.3.1 System Setup 26 2.3.2 Repeatability Comparison with Non-recon gurable Robot Manipulators 28 2.3.3 E ect of the O set in Two-dof Modules 30 2.4 Conclusion 32 3 A Programming Architecture for Modular Recon gurable Robots 33 3.1 Data Structure for Multi-dof Joint Modules 34 3.2 Automatic Kinematic Modeling 37 3.3 Automatic Dynamic Modeling 40 3.4 Flexibility in Manipulator 42 3.5 Experiments 45 3.5.1 System Setup 46 3.5.2 Recon gurability 46 3.5.3 Pick-and-Place with Vision Sensors 48 3.6 Conclusion 49 4 A Preparatory Safety Measure for Robust Collision Avoidance 51 4.1 Preliminaries on Manipulability and Safety 52 4.2 Analysis on Reected Mass 56 4.3 Manipulability Control on S+(1;m) 60 4.3.1 Geometry of the Group of Positive Semi-de nite Matrices 60 4.3.2 Rank-One Manipulability Control 63 4.4 Collision Avoidance with Preparatory Action 65 4.4.1 Repulsive and Preparatory Potential Functions 65 4.4.2 Hierarchical Control and Task Relaxation 67 4.5 Experiments 70 4.5.1 Manipulability Control 71 4.5.2 Collision Avoidance 75 4.6 Conclusion 82 5 Collision Avoidance with Velocity-Dependent Constraints 85 5.1 Input-Output Linearization 87 5.2 Invariance Control 89 5.3 Velocity-Dependent Constraints for Robot Safety 90 5.3.1 Velocity-Dependent Repulsive Constraints 90 5.3.2 Preparatory Constraints 92 5.3.3 Corrective Control for Dangerous Initial State 93 5.4 Experiment 95 5.5 Conclusion 98 6 Conclusion 101 6.1 Overview of This Thesis 101 6.2 Future Work 104 Appendix A Appendix 107 A.1 Preliminaries on Graph Theory 107 A.2 Lie-Theoretic Formulations of Robot Kinematics and Dynamics 108 A.3 Derivatives of Eigenvectors and Eigenvalues 110 A.4 Proof of Proposition Proposition 4.1 111 A.5 Proof of Triangle Inequality When p = 1 114 A.6 Detailed Conditions for a Danger Field 115 Bibliography 117 Abstract 127Docto

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