261 research outputs found

    Cartesian control of redundant robots

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    A Cartesian-space position/force controller is presented for redundant robots. The proposed control structure partitions the control problem into a nonredundant position/force trajectory tracking problem and a redundant mapping problem between Cartesian control input F is a set member of the set R(sup m) and robot actuator torque T is a set member of the set R(sup n) (for redundant robots, m is less than n). The underdetermined nature of the F yields T map is exploited so that the robot redundancy is utilized to improve the dynamic response of the robot. This dynamically optimal F yields T map is implemented locally (in time) so that it is computationally efficient for on-line control; however, it is shown that the map possesses globally optimal characteristics. Additionally, it is demonstrated that the dynamically optimal F yields T map can be modified so that the robot redundancy is used to simultaneously improve the dynamic response and realize any specified kinematic performance objective (e.g., manipulability maximization or obstacle avoidance). Computer simulation results are given for a four degree of freedom planar redundant robot under Cartesian control, and demonstrate that position/force trajectory tracking and effective redundancy utilization can be achieved simultaneously with the proposed controller

    Singularity avoidance of a six degree of freedom three dimensional redundant planar manipulator

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    AbstractThis paper focuses on the improvement of singularity avoidance of three dimensional planar redundant manipulators by increasing its degrees of freedom without increasing the number of motors controlling the manipulator. Consequently, the method to build a three dimensional planar manipulator with six-degrees of freedom using three motors instead of six is discussed in detail. A comparison of the manipulability index values for the proposed manipulator is made with the manipulability index values of PUMA arm to demonstrate the effectiveness of using the proposed manipulator for singularity avoidance

    Dexterous Grasping by Manipulability Selection for Mobile Manipulator with Visual Guidance

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    Industry 4.0 demands the heavy usage of robotic mobile manipulators with high autonomy and intelligence. The goal is to accomplish dexterous manipulation tasks without prior knowledge of the object status in unstructured environments. It is important for the mobile manipulator to recognize and detect the objects, determine manipulation pose, and adjust its pose in the workspace fast and accurately. In this research, we developed a stereo vision algorithm for the object pose estimation using point cloud data from multiple stereo vision systems. An improved iterative closest point algorithm method is developed for the pose estimation. With the pose input, algorithms and several criteria are studied for the robot to select and adjust its pose by maximizing its manipulability on a given manipulation task. The performance of each technical module and the complete robotic system is finally shown by the virtual robot in the simulator and real robot in experiments. This study demonstrates a setup of autonomous mobile manipulator for various flexible manufacturing and logistical scenarios

    ๊ณ ์ธต๋นŒ๋”ฉ ๊ณค๋Œ๋ผ ํƒ‘์žฌ์šฉ ์™ธ๋ถ€ ์œ ๋ฆฌ์ฐฝ ์ฒญ์†Œ๋กœ๋ด‡ ์œ ๋‹› ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๊ณผ, 2019. 2. ๊น€์ข…์›.Walls of high-rise buildings are cleaned manually several times in a year by workers in a gondola. The cleaning work is difficult and extremely dangerous for human workers and there are several ongoing studies to automate this work by means of robotic solutions. To achieve a successful cleaning performance, a cleaning operation has to adapt to the environmental conditions. In this study, we design and assemble a manipulator to be used in wall-cleaning applications. From the design requirements identified by investigating a high-rise building in Korea, we determined the two important degrees-of-freedom (DOF), and a parallel mechanism is designed to achieve the motion. With the parallel configuration, the design parameters are optimized based on a dynamic index to achieve high cleaning performance in a gondola. A prototype is assembled, and the cleaning performance is verified on a test bench. A field test with the developed manipulator will be performed in the near future.๋นŒ๋”ฉ์˜ ๋นŒ๋”ฉ์˜ ๋ฒฝ์€ ์ฒญ์†Œ ๊ทผ๋กœ์ž๊ฐ€ ๊ทผ๋กœ์ž๊ฐ€ ์ผ ๋…„์— ์ˆ˜ ์ฐจ๋ก€ ์ง์ ‘ ๊ณค๋Œ๋ผ์— ๊ณค๋Œ๋ผ์— ํƒ‘์Šนํ•˜์—ฌ ์ฒญ์†Œํ•ฉ๋‹ˆ๋‹ค ์ฒญ์†Œํ•ฉ๋‹ˆ๋‹ค . ์ฒญ์†Œ ์ž‘์—…์€ ์ž‘์—…์€ ๋‹จ์ˆœ ๋…ธ๋™์ด์ง€๋งŒ ๋…ธ๋™์ด์ง€๋งŒ ๊ณ ์ธต์—์„œ์˜ ๊ณ ์ธต์—์„œ์˜ ๊ณ ์ธต์—์„œ์˜ ์ž‘์—…์ด๋ฏ€๋กœ ์ž‘์—…์ด๋ฏ€๋กœ ๋งค์šฐ ์œ„ํ—˜ํ•ฉ๋‹ˆ๋‹ค ์œ„ํ—˜ํ•ฉ๋‹ˆ๋‹ค . ๊ทธ๋ฆฌํ•˜์—ฌ ๊ทธ๋ฆฌํ•˜์—ฌ ๋กœ๋ด‡์„ ๋กœ๋ด‡์„ ์‚ฌ์šฉํ•˜์—ฌ ์‚ฌ์šฉํ•˜์—ฌ ์ด ์ž‘์—…์„ ์ž‘์—…์„ ์ž๋™ํ™”ํ•˜๋Š” ์ž๋™ํ™”ํ•˜๋Š” ์ง€์†์ ์ธ ์ง€์†์ ์ธ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์—ฐ๊ตฌ๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค . ์ฒญ์†Œ ์ž‘์—…์— ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์ฒญ์†Œ ์„ฑ๋Šฅ์„ ์„ฑ๋Šฅ์„ ๋†’์ด๊ธฐ ๋†’์ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ์œ„ํ•ด์„œ๋Š” ์ฒญ์†Œ ์กฐ๊ฑด์ด ์กฐ๊ฑด์ด ์ฒญ์†Œ ํ™˜๊ฒฝ์— ํ™˜๊ฒฝ์— ์ ์‘ํ•ด์•ผ ์ ์‘ํ•ด์•ผ ์ ์‘ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค . ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์—ฐ๊ตฌ์—์„œ๋Š” ์—ฐ๊ตฌ์—์„œ๋Š” ์™ธ๋ฒฝ ์ฒญ์†Œ์ž‘์—…์— ์ฒญ์†Œ์ž‘์—…์— ์‚ฌ์šฉํ•  ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋ฅผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋ฅผ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ๋ฅผ ์„ค๊ณ„ํ•˜๊ณ  ์ œ์ž‘ ํ•ฉ๋‹ˆ๋‹ค ํ•ฉ๋‹ˆ๋‹ค . ๊ณ ์ธต๋นŒ๋”ฉ์— ์œ ์ง€๋ณด์ˆ˜์™€ ์ฒญ์†Œ์ž‘์—…์„ ์œ„ํ•ด ์˜๋ฌด ์ ์œผ๋กœ ์„ค์น˜๋˜์–ด์žˆ๋Š” ๊ณค๋Œ๋ผ์— ํƒ‘์žฌํ•˜๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์˜ ์ฒญ์†Œ ๋กœ๋ด‡์ž…๋‹ˆ๋‹ค . ํ•œ๊ตญ์˜ ๊ณ ์ธต ๊ฑด๋ฌผ์„ ๊ฑด๋ฌผ์„ ์กฐ์‚ฌํ•ด์„œ ์กฐ์‚ฌํ•ด์„œ ๋‚˜์˜จ ์„ค๊ณ„ ์š”๊ตฌ ์‚ฌํ•ญ์—์„œ ์‚ฌํ•ญ์—์„œ ๋‘ ๊ฐ€์ง€ ์ค‘์š”ํ•œ ์ค‘์š”ํ•œ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์˜ ์ž์œ ๋„ (DOF)๋ฅผ ๊ฒฐ์ •ํ–ˆ์Šต๋‹ˆ๋‹ค ๊ฒฐ์ •ํ–ˆ์Šต๋‹ˆ๋‹ค . ๊ทธ ํ›„, ๋ชจ์…˜์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ๋ณ‘๋ ฌ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์„ค๊ณ„ํ–ˆ์Šต๋‹ˆ๋‹ค ์„ค๊ณ„ํ–ˆ์Šต๋‹ˆ๋‹ค . ๊ณค๋Œ๋ผ์—์„œ ๊ณค๋Œ๋ผ์—์„œ ๋†’์€ ์ฒญ์†Œ ์„ฑ๋Šฅ์„ ์„ฑ๋Šฅ์„ ์–ป๊ธฐ ์œ„ํ•ด ๋ณ‘๋ ฌ ๋งค์ปค๋‹ˆ์ฆ˜์˜ ๋™์  ์ธ๋ฑ์Šค๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ค๊ณ„ ๋ณ€์ˆ˜๋ฅผ ์ตœ์ ํ™” ์ตœ์ ํ™” ํ•˜์˜€์Šต๋‹ˆ๋‹ค. ๊ทธ ํ›„, ํ”„๋กœํ†  ํƒ€์ž…์„ ํƒ€์ž…์„ ์กฐ๋ฆฝํ•˜๊ณ  ์กฐ๋ฆฝํ•˜๊ณ  ์„ธ์ฒ™ ์„ฑ๋Šฅ์„ ์„ฑ๋Šฅ์„ ํ…Œ์ŠคํŠธ ๋ฒค์น˜์—์„œ ๋ฒค์น˜์—์„œ ํ™•์ธํ•ฉ๋‹ˆ๋‹ค.1. Introduction . 1 2. Condition for wall-cleaning operation . 4 2.1 63-story building and gondola specification . 4 2.2 Cleaning operation and cleaning performace 4 2.3 Motion and constraints of the gondola motion . 5 3. 2-DOF manipulator for cleaning operation . 7 3.1 Kinematic configuration and modeling 7 3.2 Jacobian matrix . 10 3.3 Dynamic analysis 11 3.4 Mass matrix of the manipulator 12 4. Optimal design . 23 4.1 Dynamic manipulator isotropy index 23 4.2 Workspace constraints 25 4.3 Optimization problem definition . 25 4.4 Optimal design result 25 5. Prototype and experiment . 31 6. Conclusion 31 Reference 32 ์ดˆ๋ก . 34Maste

    Contact aware robust semi-autonomous teleoperation of mobile manipulators

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    In the context of human-robot collaboration, cooperation and teaming, the use of mobile manipulators is widespread on applications involving unpredictable or hazardous environments for humans operators, like space operations, waste management and search and rescue on disaster scenarios. Applications where the manipulator's motion is controlled remotely by specialized operators. Teleoperation of manipulators is not a straightforward task, and in many practical cases represent a common source of failures. Common issues during the remote control of manipulators are: increasing control complexity with respect the mechanical degrees of freedom; inadequate or incomplete feedback to the user (i.e. limited visualization or knowledge of the environment); predefined motion directives may be incompatible with constraints or obstacles imposed by the environment. In the latter case, part of the manipulator may get trapped or blocked by some obstacle in the environment, failure that cannot be easily detected, isolated nor counteracted remotely. While control complexity can be reduced by the introduction of motion directives or by abstraction of the robot motion, the real-time constraint of the teleoperation task requires the transfer of the least possible amount of data over the system's network, thus limiting the number of physical sensors that can be used to model the environment. Therefore, it is of fundamental to define alternative perceptive strategies to accurately characterize different interaction with the environment without relying on specific sensory technologies. In this work, we present a novel approach for safe teleoperation, that takes advantage of model based proprioceptive measurement of the robot dynamics to robustly identify unexpected collisions or contact events with the environment. Each identified collision is translated on-the-fly into a set of local motion constraints, allowing the exploitation of the system redundancies for the computation of intelligent control laws for automatic reaction, without requiring human intervention and minimizing the disturbance of the task execution (or, equivalently, the operator efforts). More precisely, the described system consist in two different building blocks. The first, for detecting unexpected interactions with the environment (perceptive block). The second, for intelligent and autonomous reaction after the stimulus (control block). The perceptive block is responsible of the contact event identification. In short, the approach is based on the claim that a sensorless collision detection method for robot manipulators can be extended to the field of mobile manipulators, by embedding it within a statistical learning framework. The control deals with the intelligent and autonomous reaction after the contact or impact with the environment occurs, and consist on an motion abstraction controller with a prioritized set of constrains, where the highest priority correspond to the robot reconfiguration after a collision is detected; when all related dynamical effects have been compensated, the controller switch again to the basic control mode

    Method and apparatus for configuration control of redundant robots

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    A method and apparatus to control a robot or manipulator configuration over the entire motion based on augmentation of the manipulator forward kinematics is disclosed. A set of kinematic functions is defined in Cartesian or joint space to reflect the desirable configuration that will be achieved in addition to the specified end-effector motion. The user-defined kinematic functions and the end-effector Cartesian coordinates are combined to form a set of task-related configuration variables as generalized coordinates for the manipulator. A task-based adaptive scheme is then utilized to directly control the configuration variables so as to achieve tracking of some desired reference trajectories throughout the robot motion. This accomplishes the basic task of desired end-effector motion, while utilizing the redundancy to achieve any additional task through the desired time variation of the kinematic functions. The present invention can also be used for optimization of any kinematic objective function, or for satisfaction of a set of kinematic inequality constraints, as in an obstacle avoidance problem. In contrast to pseudoinverse-based methods, the configuration control scheme ensures cyclic motion of the manipulator, which is an essential requirement for repetitive operations. The control law is simple and computationally very fast, and does not require either the complex manipulator dynamic model or the complicated inverse kinematic transformation. The configuration control scheme can alternatively be implemented in joint space

    Parallel Manipulators

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    In recent years, parallel kinematics mechanisms have attracted a lot of attention from the academic and industrial communities due to potential applications not only as robot manipulators but also as machine tools. Generally, the criteria used to compare the performance of traditional serial robots and parallel robots are the workspace, the ratio between the payload and the robot mass, accuracy, and dynamic behaviour. In addition to the reduced coupling effect between joints, parallel robots bring the benefits of much higher payload-robot mass ratios, superior accuracy and greater stiffness; qualities which lead to better dynamic performance. The main drawback with parallel robots is the relatively small workspace. A great deal of research on parallel robots has been carried out worldwide, and a large number of parallel mechanism systems have been built for various applications, such as remote handling, machine tools, medical robots, simulators, micro-robots, and humanoid robots. This book opens a window to exceptional research and development work on parallel mechanisms contributed by authors from around the world. Through this window the reader can get a good view of current parallel robot research and applications

    ์•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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