824 research outputs found

    Towards Developing Gripper to obtain Dexterous Manipulation

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    Artificial hands or grippers are essential elements in many robotic systems, such as, humanoid, industry, social robot, space robot, mobile robot, surgery and so on. As humans, we use our hands in different ways and can perform various maneuvers such as writing, altering posture of an object in-hand without having difficulties. Most of our daily activities are dependent on the prehensile and non-prehensile capabilities of our hand. Therefore, the human hand is the central motivation of grasping and manipulation, and has been explicitly studied from many perspectives such as, from the design of complex actuation, synergy, use of soft material, sensors, etc; however to obtain the adaptability to a plurality of objects along with the capabilities of in-hand manipulation of our hand in a grasping device is not easy, and not fully evaluated by any developed gripper. Industrial researchers primarily use rigid materials and heavy actuators in the design for repeatability, reliability to meet dexterity, precision, time requirements where the required flexibility to manipulate object in-hand is typically absent. On the other hand, anthropomorphic hands are generally developed by soft materials. However they are not deployed for manipulation mainly due to the presence of numerous sensors and consequent control complexity of under-actuated mechanisms that significantly reduce speed and time requirements of industrial demand. Hence, developing artificial hands or grippers with prehensile capabilities and dexterity similar to human like hands is challenging, and it urges combined contributions from multiple disciplines such as, kinematics, dynamics, control, machine learning and so on. Therefore, capabilities of artificial hands in general have been constrained to some specific tasks according to their target applications, such as grasping (in biomimetic hands) or speed/precision in a pick and place (in industrial grippers). Robotic grippers developed during last decades are mostly aimed to solve grasping complexities of several objects as their primary objective. However, due to the increasing demands of industries, many issues are rising and remain unsolved such as in-hand manipulation and placing object with appropriate posture. Operations like twisting, altering orientation of object within-hand, require significant dexterity of the gripper that must be achieved from a compact mechanical design at the first place. Along with manipulation, speed is also required in many robotic applications. Therefore, for the available speed and design simplicity, nonprehensile or dynamic manipulation is widely exploited. The nonprehensile approach however, does not focus on stable grasping in general. Also, nonprehensile or dynamic manipulation often exceeds robot\u2019s kinematic workspace, which additionally urges installation of high speed feedback and robust control. Hence, these approaches are inapplicable especially when, the requirements are grasp oriented such as, precise posture change of a payload in-hand, placing payload afterward according to a strict final configuration. Also, addressing critical payload such as egg, contacts (between gripper and egg) cannot be broken completely during manipulation. Moreover, theoretical analysis, such as contact kinematics, grasp stability cannot predict the nonholonomic behaviors, and therefore, uncertainties are always present to restrict a maneuver, even though the gripper is capable of doing the task. From a technical point of view, in-hand manipulation or within-hand dexterity of a gripper significantly isolates grasping and manipulation skills from the dependencies on contact type, a priory knowledge of object model, configurations such as initial or final postures and also additional environmental constraints like disturbance, that may causes breaking of contacts between object and finger. Hence, the property (in-hand manipulation) is important for a gripper in order to obtain human hand skill. In this research, these problems (to obtain speed, flexibility to a plurality of grasps, within-hand dexterity in a single gripper) have been tackled in a novel way. A gripper platform named Dexclar (DEXterous reConfigurable moduLAR) has been developed in order to study in-hand manipulation, and a generic spherical payload has been considered at the first place. Dexclar is mechanism-centric and it exploits modularity and reconfigurability to the aim of achieving within-hand dexterity rather than utilizing soft materials. And hence, precision, speed are also achievable from the platform. The platform can perform several grasps (pinching, form closure, force closure) and address a very important issue of releasing payload with final posture/ configuration after manipulation. By exploiting 16 degrees of freedom (DoF), Dexclar is capable to provide 6 DoF motions to a generic spherical or ellipsoidal payload. And since a mechanism is reliable, repeatable once it has been properly synthesized, precision and speed are also obtainable from them. Hence Dexclar is an ideal starting point to study within-hand dexterity from kinematic point of view. As the final aim is to develop specific grippers (having the above capabilities) by exploiting Dexclar, a highly dexterous but simply constructed reconfigurable platform named VARO-fi (VARiable Orientable fingers with translation) is proposed, which can be used as an industrial end-effector, as well as an alternative of bio-inspired gripper in many robotic applications. The robust four fingered VARO-fi addresses grasp, in-hand manipulation and release (payload with desired configuration) of plurality of payloads, as demonstrated in this thesis. Last but not the least, several tools and end-effectors have been constructed to study prehensile and non-prehensile manipulation, thanks to Bayer Robotic challenge 2017, where the feasibility and their potentiality to use them in an industrial environment have been validated. The above mentioned research will enhance a new dimension for designing grippers with the properties of dexterity and flexibility at the same time, without explicit theoretical analysis, algorithms, as those are difficult to implement and sometime not feasible for real system

    The GR2 gripper: an underactuated hand for open-loop in-hand planar manipulation

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    Performing dexterous manipulation of unknown objects with robot grippers without using high-fidelity contact sensors, active/sliding surfaces, or a priori workspace exploration is still an open problem in robot manipulation and a necessity for many robotics applications. In this paper, we present a two-fingered gripper topology that enables an enhanced predefined in-hand manipulation primitive controlled without knowing the size, shape, or other particularities of the grasped object. The in-hand manipulation behavior, namely, the planar manipulation of the grasped body, is predefined thanks to a simple hybrid low-level control scheme and has an increased range of motion due to the introduction of an elastic pivot joint between the two fingers. Experimental results with a prototype clearly show the advantages and benefits of the proposed concept. Given the generality of the topology and in-hand manipulation principle, researchers and designers working on multiple areas of robotics can benefit from the findings

    The role of morphology of the thumb in anthropomorphic grasping : a review

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    The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This article presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands

    Sensors for Robotic Hands: A Survey of State of the Art

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    Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands

    ๋ถˆํ™•์‹ค์„ฑ์„ ํฌํ•จํ•˜๋Š” ์กฐ๋ฆฝ์ž‘์—…์„ ์œ„ํ•œ ์ปดํ”Œ๋ผ์ด์–ธ์Šค ๊ธฐ๋ฐ˜ ํŽ™์ธํ™€ ์ „๋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), 2020. 8. ๋ฐ•์žฌํฅ.The peg-in-hole assembly is a representative robotic task that involves physical contact with the external environment. The strategies generally involve performing the assembly task by estimating the contact state between the peg and the hole. The contact forces and moments, measured using force sensors, are primarily used to estimate the contact state. In this paper, in contrast to past research in the area, which has involved the utilization of such expensive devices as force/torque sensors or remote compliance mechanisms, an inexpensive method is proposed for peg-in-hole assembly without force feedback or passive compliance mechanisms. The method consists of an analysis of the state of contact between the peg and the hole as well as a strategy to overcome the inevitable positional uncertainty of the hole incurred in the recognition process. A control scheme was developed to yield compliant behavior from the robot with physical contact under the condition of hybrid position/force control. Proposed peg-in-hole strategy is based on compliance characteristics and generating the force and moment. The peg is inserted into the hole as it adapts to the external environment. The effectiveness of the proposed method was experimentally verified using a humanoid upper body robot with fifty degrees of freedom and a peg-in-hole apparatus with a small clearance (0.1 mm). Three cases of experiments were conducted; Assembling the peg attached to the arm in the hole fixed in the external environment, grasping a peg with an anthropomorphic hand and assembling it into a fixed hole, and grasping both peg and hole with both hands and assembling each other. In order to assemble the peg-in-hole through the proposed strategy by the humanoid upper body robot, I present a method of gripping an object, estimating the kinematics of the gripped object, and manipulating the gripped object. In addition to the cost aspect, which is the fundamental motivation for the proposed strategy, the experimental results show that the proposed strategy has advantages such as fast assembly time and high success rate, but has the disadvantage of unpredictable elapsed time. The reason for having a high variance value for the success time is that the spiral trajectory, which is most commonly used, is used. In this study, I analyze the efficiency of spiral force trajectory and propose an improved force trajectory. The proposed force trajectory reduces the distribution of elapsed time by eliminating the uncertainty in the time required to find a hole. The efficiency of the force trajectory is analyzed numerically, verified through repeated simulations, and verified by the actual experiment with humanoid upper body robot developed by Korea institute of industrial technology.ํŽ™์ธํ™€ ์กฐ๋ฆฝ์€ ๋กœ๋ด‡์˜ ์ ‘์ด‰ ์ž‘์—…์„ ๋Œ€ํ‘œํ•˜๋Š” ์ž‘์—…์œผ๋กœ, ํŽ™์ธํ™€ ์กฐ๋ฆฝ ์ „๋žต์„ ์—ฐ๊ตฌํ•จ์œผ๋กœ์จ ์‚ฐ์—… ์ƒ์‚ฐ ๋ถ„์•ผ์˜ ์กฐ๋ฆฝ์ž‘์—…์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ํŽ™์ธํ™€ ์กฐ๋ฆฝ์ž‘์—…์€ ์ผ๋ฐ˜์ ์œผ๋กœ ํŽ™๊ณผ ํ™€ ๊ฐ„์˜ ์ ‘์ด‰์ƒํƒœ๋ฅผ ์ถ”์ •ํ•จ์œผ๋กœ์จ ์ด๋ฃจ์–ด์ง„๋‹ค. ์ ‘์ด‰์ƒํƒœ๋ฅผ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ€์žฅ ๋„๋ฆฌ ์“ฐ์ด๋Š” ๋ฐฉ๋ฒ•์€ ํž˜ ์„ผ์„œ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ธ๋ฐ, ์ ‘์ด‰ ํž˜๊ณผ ๋ชจ๋ฉ˜ํŠธ๋ฅผ ์ธก์ •ํ•˜์—ฌ ์ ‘์ด‰์ƒํƒœ๋ฅผ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ์‹์ด๋‹ค. ๋งŒ์•ฝ ์ด๋Ÿฌํ•œ ์„ผ์„œ๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š์„ ์ˆ˜ ์žˆ๋‹ค๋ฉด, ํ•˜๋“œ์›จ์–ด ๋น„์šฉ๊ณผ ์†Œํ”„ํŠธ์›จ์–ด ์—ฐ์‚ฐ๋Ÿ‰ ๊ฐ์†Œ ๋“ฑ์˜ ์žฅ์ ์ด ์žˆ์Œ์€ ์ž๋ช…ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํž˜ ์„ผ์„œ ํ˜น์€ ์ˆ˜๋™ ์ปดํ”Œ๋ผ์ด์–ธ์Šค ์žฅ์น˜๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š” ํŽ™์ธํ™€ ์ „๋žต์„ ์ œ์•ˆํ•œ๋‹ค. ํ™€์— ๋Œ€ํ•œ ์ธ์‹ ์˜ค์ฐจ ํ˜น์€ ๋กœ๋ด‡์˜ ์ œ์–ด ์˜ค์ฐจ๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋จผ์ € ํŽ™๊ณผ ํ™€์˜ ์ ‘์ด‰ ๊ฐ€๋Šฅ ์ƒํƒœ๋ฅผ ๋ถ„์„ํ•˜๊ณ  ๋กœ๋ด‡์˜ ์ปดํ”Œ๋ผ์ด์–ธ์Šค ๋ชจ์…˜์„ ์œ„ํ•œ ์ œ์–ด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋””์ž์ธํ•œ๋‹ค. ์ „๋žต์€ ์ปดํ”Œ๋ผ์ด์–ธ์Šค ํŠน์ง•์— ๊ธฐ๋ฐ˜ํ•˜๋ฉฐ ํŽ™์— ํž˜๊ณผ ๋ชจ๋ฉ˜ํŠธ๋ฅผ ์ƒ์„ฑ์‹œํ‚ด์œผ๋กœ์จ ์กฐ๋ฆฝ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ํŽ™์€ ์™ธ๋ถ€ํ™˜๊ฒฝ์— ์ˆœ์‘ํ•จ์œผ๋กœ์จ ํ™€์— ์‚ฝ์ž…๋œ๋‹ค. ์ œ์•ˆํ•œ ์ „๋žต์€ ๋‚ฎ์€ ๊ณต์ฐจ๋ฅผ ๊ฐ–๋Š” ํŽ™์ธํ™€ ์‹คํ—˜์„ ํ†ตํ•ด์„œ ๊ทธ ์œ ํšจ์„ฑ์ด ๊ฒ€์ฆ๋œ๋‹ค. ํŽ™๊ณผ ํ™€์„ ๋กœ๋ด‡ํŒ”๊ณผ ์™ธ๋ถ€ํ™˜๊ฒฝ์— ๊ฐ๊ฐ ๊ณ ์ •๋œ ํ™˜๊ฒฝ์—์„œ์˜ ์‹คํ—˜, ์ธ๊ฐ„ํ˜• ๋กœ๋ด‡ํ•ธ๋“œ๋ฅผ ์ด์šฉํ•˜์—ฌ ํŽ™์„ ์žก์•„์„œ ๊ณ ์ •๋œ ํ™€์— ์‚ฝ์ž…ํ•˜๋Š” ์‹คํ—˜, ๊ทธ๋ฆฌ๊ณ  ํ…Œ์ด๋ธ”์— ๋†“์ธ ํŽ™๊ณผ ํ™€์„ ๊ฐ๊ฐ ๋กœ๋ด‡ํ•ธ๋“œ๋กœ ํŒŒ์ง€ํ•˜์—ฌ ์กฐ๋ฆฝํ•˜๋Š” ์ด ์„ธ ๊ฐ€์ง€์˜ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํ•ธ๋“œ๋กœ ํŽ™์„ ํŒŒ์ง€ํ•˜๊ณ  ์กฐ์ž‘ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ํŒŒ์ง€ ๋ฐฉ๋ฒ•๊ณผ ํ•ธ๋“œ๋ฅผ ์ด์šฉํ•œ ๋ฌผ์ฒด ์กฐ์ž‘ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐ„๋žตํžˆ ์†Œ๊ฐœํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ์ „๋žต์˜ ์„ฑ๋Šฅ์„ ์‹คํ—˜์ ์œผ๋กœ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๋†’์€ ์กฐ๋ฆฝ ์„ฑ๊ณต๋ฅ ์„ ๊ฐ–๋Š” ๋Œ€์‹  ์กฐ๋ฆฝ์‹œ๊ฐ„์ด ์˜ˆ์ธกํ•  ์ˆ˜ ์—†๋Š” ๋‹จ์ ์ด ๋‚˜ํƒ€๋‚˜ ์ด๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋ Œ์น˜ ๊ถค์  ๋˜ํ•œ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ € ๊ฐ€์žฅ ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ๋‚˜์„  ํž˜ ๊ถค์ ์„ ์ด์šฉํ–ˆ์„ ๋•Œ ์กฐ๋ฆฝ ์„ฑ๊ณต์‹œ๊ฐ„์˜ ๋ถ„์‚ฐ์ด ํฐ ์ด์œ ๋ฅผ ํ™•๋ฅ ๊ฐœ๋…์„ ์ด์šฉํ•ด ๋ถ„์„ํ•˜๊ณ , ์ด๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•œ ๋ถ€๋ถ„์  ๋‚˜์„  ํž˜ ๊ถค์ ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ ํž˜ ๊ถค์ ์ด ๋‚˜์„  ํž˜ ๊ถค์ ์— ๋น„ํ•ด ๊ฐ–๋Š” ์„ฑ๋Šฅ์˜ ์šฐ์ˆ˜์„ฑ์„ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ˆ˜์น˜์  ๋ถ„์„, ๋ฐ˜๋ณต์  ์‹œ๋ฎฌ๋ ˆ์ด์…˜, ๊ทธ๋ฆฌ๊ณ  ๋กœ๋ด‡์„ ์ด์šฉํ•œ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค.1 INTRODUCTION 1 1.1 Motivation: Peg-in-Hole Assembly 1 1.2 Contributions of Thesis 2 1.3 Overview of Thesis 3 2 COMPLIANCE BASED STRATEGY 5 2.1 Background & Related Works 5 2.2 Analysis of Peg-in-Hole Procedure 6 2.2.1 Contact Analysis 7 2.2.2 Basic Idea 9 2.3 Peg-in-Hole Strategy 12 2.3.1 Unit Motions 12 2.3.2 State of Strategy 13 2.3.3 Conditions for State Transition 15 2.4 Control Frameworks 18 2.4.1 Control for Compliant Behavior 18 2.4.2 Friction Compensate 20 2.4.3 Control Input for the Strategy 25 2.5 Experiment 29 2.5.1 Experiment Environment 29 2.5.2 Fixed Peg and Fixed Hole 31 2.5.2.1 Experiment Results 31 2.5.2.2 Analysis of Force and Control Gain 36 2.5.3 Peg-in-Hole with Multi Finger Hand 41 2.5.3.1 Object Grasping 42 2.5.3.2 Object In-Hand Manipulation 44 2.5.3.3 Experiment Results 49 2.5.4 With Upper Body Robot 50 2.5.4.1 Peg-in-Hole Procedure 52 2.5.4.2 Kinematics of Grasped Object 54 2.5.4.3 Control Frameworks 54 2.5.4.4 Experiment Results 56 2.6 Discussion 59 2.6.1 Peg-in-Hole Transition 59 2.6.2 Influential Issues 59 3 WRENCH TRAJECTORY 63 3.1 Problem Statement 64 3.1.1 Hole Search Process 64 3.1.2 Spiral Force Trajectory Analysis 66 3.2 Partial Spiral Force Trajectory 70 3.2.1 Force Trajectory with Tilted Posture 70 3.2.2 Probability to Three-point Contact 76 3.3 SIMULATION & EXPERIMENT 78 3.3.1 Simulation 78 3.3.2 Experiment 83 4 CONCLUSIONS 90 Abstract (In Korean) 102Docto

    Bio-Inspired Motion Strategies for a Bimanual Manipulation Task

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    Steffen JF, Elbrechter C, Haschke R, Ritter H. Bio-Inspired Motion Strategies for a Bimanual Manipulation Task. In: International Conference on Humanoid Robots (Humanoids). 2010
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