106 research outputs found
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ 곡과λν κΈ°κ³ν곡곡νλΆ, 2017. 8. κΉμ’
μ.νμΈν(Peg-In-Hole) μμ
μ λ‘λ΄μ νμ©ν 쑰립μμ
μ€ κ°μ₯ κΈ°μ΄μ μΈ μμ
μ΄λΌκ³ ν μ μλ€. μ‘°κ·Έλ§ν μμΉ μλ¬μλ λΌμ νμ(Jamming λλ Wedging)μ΄ λ°μνκ³ μ΄λ λΆν μ½μ
μ€μ νμμ μ λ°ν μ μκΈ° λλ¬Έμ, 쑰립 λμλ¬Όκ°μ μμΉ λ° λ°©ν₯μ λν μ λ ¬μ΄ μ±κ³΅μ μΈ νμΈν μμ
μ μν΄μλ 무μλ³΄λ€ μ€μνλ€. μ΄λ¬ν νμΈν μμ
μ μν΄μλ μ§κΈκΉμ§ λ§μ μ°κ΅¬κ° μ§νλμ΄ μμΌλ©°, λμλ¬Όκ°μ μ λ ¬ λ°©μμ λ°λΌμ μλμ λλ λ₯λμ λ°©λ²μΌλ‘ ꡬλΆλλ€. RCC(Remote Center Compliance)λ‘ λνλλ μλμ μΈ μ λ ¬λ°©λ²μ μ»΄νλΌμ΄μΈμ€μ λμ λΆνμ νΉμ λͺ¨μμ μ΄μ©νλ λ°λ©΄μ, λ₯λμ μΈ μ λ ¬λ°©λ²μ λΉμ μ΄λ 쑰립 μ λ°μνλ λ°λ ₯ μ 보λ₯Ό μ΄μ©νμ¬ λμλ¬Όκ°μ μ λ ¬μ μννλ€. μλμ μ λ ¬ λ°©λ²μ νΉλ³ν μΈ‘μ μ΄λ λ
Έλ ₯ μμ΄ μ¬μ©λ μ μλ€λ μ₯μ μ κ°μ§κ³ μμ§λ§, λΆνμ μ±λ²(Chamfer) μ¬μ΄μ¦λ νμ κΈΈμ΄ λ±μ λ°λΌμ μ¬μ© κ°λ₯ μ¬λΆκ° κ²°μ λμ΄ μ μ©μ΄ μ νμ μ΄λ€. λΉμ μ νμ©μ ν΅ν μ λ ¬λ λν μ μ©μ΄ μ νμ μΈλ°, κ·Έ μ΄μ λ μΉ΄λ©λΌμ μ€μΉ μμΉ λ° μ£Όλ³ νκ²½μ λ°λ₯Έ μΈ‘μ μ νλμ λ―Όκ°μ± λλ¬Έμ΄λ€.
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μ μννκΈ° μνμ¬ λ€μμ λμ 그리νΌ, κ°λ μλ¬ μΈ‘μ κΈ° λ° μΈ‘μ λ ν μ 보λ₯Ό κ΅°μ§ννμ¬ λμλ¬Όκ°μ μμΉ μλ¬λ₯Ό μΈ‘μ ν μ μλ μκ³ λ¦¬μ¦μ΄ μ μλμλ€. μ΄λ₯Ό μνμ¬ νλ¨μ μ£Όμ μΈκ°μ§ ν΅μ¬ κΈ°λ₯μ΄ μμ€ν
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μμΉ μλ¬ λ³΄μ μμ
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μ μνμ¬, 4 μμ λλ₯Ό μ§λ λ κ°μ μκ°λ½μΌλ‘ ꡬμ±λ 그리νΌκ° μ€κ³λμμΌλ©°, μκ°λ½ λ λ¨μλ 6μΆ ν μΌμκ° λ΄μ¬λμ΄ λ°λ ₯ μΈ‘μ μ κ°λ₯νκ² νμλ€. λ‘λ΄μ μλͺ©μ μ€μΉλ ν μΌμμ λ‘λ΄ νμ μμ λλ₯Ό μ¬μ©νμ¬ μμ
μ μννλ μΌλ°μ μΈ λ°©λ²κ³Όλ λ¬λ¦¬, μ€κ³λ λ€μμ λ 그리νΌλ₯Ό νμ©νμ¬ νμ μ‘°μ κ°λ₯νκ² νμλ€. λν, νμ μ μΈ‘λ©΄μμ λ°μλ λ°λ ₯ μ 보λ€μ νμ μμΉ μ 보μ ν¨κ» μ μ₯νμ¬ μμΉμλ¬ λμΆμ νμ© κ°λ₯νλλ‘ νμλ€.
2 μμ λμ μ§κ΅ λ‘λ΄κ³Ό λ μ΄μ 거리 μΌμλ‘ κ΅¬μ±λ 견μ€ν κ°λ μΈ‘μ κΈ°(Scanner)κ° νκ³Ό ν μ¬μ΄κ°μ κ°λ μλ¬ λ³΄μ μ μνμ¬ μ€κ³ λ° κ΅¬νλμλ€. νκ³Ό ν μ¬μ΄κ°μ μ μ΄ μ‘°κ±΄μ λ°λΌμ λͺ¨λ©νΈ λ°λ ₯μ λ°μ μ λ¬΄κ° κ²°μ λλλ°, ν μ 보λ₯Ό λ°νμΌλ‘ ν λΉ λ₯΄κ³ μ λ’°μ± μλ μλ¬ μΆμ μ μν΄μλ κ°λ μλ¬ μΈ‘μ μ ν΅ν 보μ μ νμλ‘ νλ€. μ¬κ°νμμ ν μΈ ν μμ
μ κ²½μ°μλ, νκ³Ό ν μ¬μ΄κ°μ μ£μ§ λ° μ§μ§ λ©΄μ μμ λ°λΌμ μ΄ 5κ°μ§μ κ²½μ°λ‘ μ μ΄ μ‘°κ±΄μ΄ λΆλ₯κ° λλλ°, λͺ¨λ©νΈλ κ·Έ μ€μμ νκ°μ§μ κ²½μ°μλ§ λ°μνκ² λλ€. κ°λ μλ¬ λ³΄μ μ ν΅νμ¬, μ μ΄ μ‘°κ±΄μ 2κ°μ§λ‘ μ€μ΄λ€κ² λλ©°, μ΄λ₯Ό ν΅νμ¬ μλ¬ λ³΄μ μκ°μ μ€μ΄λ κ²μ΄ κ°λ₯νλ€.
νκ³Ό ν μ¬μ΄κ°μ μμΉ μλ¬λ₯Ό μΆμΆνκΈ° μνμ¬, λͺ¨λ©νΈ λ°λ ₯ μ 보μ νμ μμΉ μ λ³΄λ‘ κ΅¬μ±λ λ°μ΄ν° μΈνΈμ κ΅°μ§ν μκ³ λ¦¬μ¦μ μ μ©νμλ€. κ°λ μλ¬ λ³΄μ νμλ, λͺ¨λ©νΈκ° λ°μνμ§ μλ κ²½μ°κ° λ¨κ² λλ©° μ΄λ¬ν νΌν©λ λ°μ΄ν° μΈνΈμμλ μμΉ μλ¬λ₯Ό μΆμΆν μ μλ μΈκ³΅μ§λ₯μ νμλ‘ νλ€. μ΄λ₯Ό μνμ¬, κΈ°κ³ νμ΅μμ μ¬μ©λλ λ κ°μ§μ λνμ μΈ μκ³ λ¦¬μ¦, K νκ· μκ³ λ¦¬μ¦κ³Ό κ°μ°μμ νΌν© λͺ¨λΈ μκ³ λ¦¬μ¦μ λ€μν μΈ‘μ λ°μ΄ν° μΈνΈλ€μ μ μ©νμλ€. μλ¬ μΆμΆ μ μκ³ λ¦¬μ¦μ μ νλμ 견μ€ν¨μ νμΈ νκΈ° μνμ¬ κ°μ 쑰건μμ μΈ‘μ λκ±°λ λ€λ₯Έ μλμμ μΈ‘μ λ μΈ κ°μ λ°μ΄ν° μΈνΈκ° μμΉ μλ¬ μΆμΆμ μνμ¬ μ¬μ©λμλ€. K νκ· μκ³ λ¦¬μ¦μ κ²½μ°, μΆμΆλ μμΉ μλ¬μ μ νλμ κ°κ°μ λ°μ΄ν° μΈνΈμμ μΆμΆλ μμΉ μλ¬ κ°λ€μ νΈμ°¨λ κ°κ° 0.29mm, 0.14mm μ΄λ΄μ΄μ§λ§, κ°μ°μμ νΌν© λͺ¨λΈ μκ³ λ¦¬μ¦μ κ²½μ°μλ κ°κ° 0.44mm, 0.43mmλ₯Ό 보μ΄κ³ μλ€. K νκ· μκ³ λ¦¬μ¦μ μμΉ μλ¬ μΆμΆμμ μμ μ μΈ μ νλμ 견μ€ν¨μ κ°μ§λ©°, κ°μ°μμ νΌν© λͺ¨λΈ μκ³ λ¦¬μ¦μ μνμ¬ μ ν쑰건μ μ§λ νλΌλ―Έν° μ¬μ©μ νμλ‘ νλ κ²μ νμΈν μ μλ€.
μΌμλ‘λΆν°μ μ 보μ μμ§νμ§ μκ³ , κΈ΄ λμ ν κΆ€μ λ§μ μ΄μ©νμ¬ μλ¬ λ³΄μ μ μννλ λΈλΌμΈλ μμΉ(Blind Search)μ λΉκ΅ν λ, μ μλ μΈ‘μ κΈ°μ μμΉ μΆμΆ μκ³ λ¦¬μ¦μ μ§§κ³ νΈμ°¨κ° μλ μλ¬ λ³΄μ μκ°μ μ₯μ μ κ°μ§κ³ μλ€. μ£Όμ΄μ§ κ²μ μμμ μμ§ μνμΌλ‘ μμ§μ΄λ 짧μ XY κΆ€μ μ μ¬μ©νμ¬ μλ¬ λ³΄μ μκ°μ λ¨μΆ κ°λ₯νκ² νκ³ , κ°λ μλ¬ λ³΄μ μ ν΅νμ¬ μ μ΄ μ‘°κ±΄ κ²½μ°μ μλ₯Ό μ€μ΄λ©΄μ μλ¬ λ³΄μ μ μν μκ°μ νΈμ°¨κ° μλλ‘ νμλ€.Peg-In-Hole is the one of basic tasks for robotic assembly. For successful Peg-In-Hole, the position and orientation alignment between mating parts is very important because small error can induce jamming and wedging which generates excessive force leading to damages on mating parts during insertion. A lot of researches for Peg-In-Hole task have been underway and it can be categorized into passive and active approaches. The passive approach represented by Remote Center Compliance uses the compliance and shape of mating parts for alignment, whereas the active approach uses measurement from vision, force or both of them. Passive approach has strength in which alignment can be done passively without any other measurements but applications are limited because it depends on the shape of mating parts like chamfer size and length of peg. Utilization of vision is also limited because of sensitivity in accuracy which is affected significantly by camera location and surrounding environment.
In this dissertation, a dexterous gripper with an angular error measuring instrument and reliable position error estimation algorithm by clustering the force dataset is proposed for Peg-In-Hole task. Three main key features stated below are implemented in the system design and tested with square Peg-In-Hole experiments.
The dexterous gripper which consists of 4 DOF(Degree Of Freedom) two fingers embedded with 6 axis force sensors at the fingertip is designed for micro manipulation during error recovery. Unlike the usual method in which force sensor is mounted on the robot wrist and peg is manipulated by robot arm, the designed dexterous gripper is used for both of grasping and manipulating peg. Reaction force generated on both side of peg is also measured at fingertip and recorded with peg position for error estimation.
Robust angle measuring instrument, Scanner, consisted of 2DOF manipulator and laser distance sensor is also designed and implemented for detecting the angular error between peg and hole. Depending on the contact condition, its decided whether moment is generated or not, thus angular error compensation is necessary for fast and reliable error estimation based on the force data. In case of square Peg-In-Hole, the contact condition can be classified into 5 cases depending on the number of edge and supporting area between peg and hole and moment is generated in only one case. With the angular error compensation, the number of contact condition can be diminished to 2 cases thus shortened recovery time can be accomplished.
To extract the position error between peg and hole, error estimation with clustering algorithm is applied to the measured dataset of moment and peg position. Even after angular error compensation, there still exists the condition which generates no reaction moment, thus artificial intelligence which can extract the position error among mixed dataset is required. Two representative algorithms, K means algorithms and Gaussian Mixture Model algorithm, commonly used in machine learning for clustering dataset are applied to various datasets constructed with position and moment for estimating position error. Two datasets, one constructed with the three datasets measured at same condition and the other constructed with three datasets measured with different velocity are used to check accuracy and robustness in error estimation from both of algorithm. The accuracy of estimated position error and deviation among estimated error in each dataset from K means algorithm is within 0.29mm and 0.14mm whereas both of that from Gaussian Mixture Model algorithm is within 0.44mm and 0.43mm. K means algorithm shows stable accuracy and robustness on position error estimation whereas the Gaussian Mixture Model algorithm needs to use constrained parameter for both of them.
Comparing with blind search which uses no information from sensors and long spiral trajectory for error recovery, the proposed measurement system and algorithms have advantages in terms of recovery time and no variation of it. Short XY trajectory which moves horizontally and vertically in given search area can be used and error recovery time have no variation regardless of position error by diminishing the number of contact conditions through angular error compensation.Chapter 1. Introduction 1
1.1. Robotic Assembly and Peg-In-Hole Task 1
1.2. Previous Research Works 2
1.2.1. Passive approaches 3
1.2.2. Active approaches 5
1.3. Purpose and Contribution of Research 9
Chapter 2. Contact Condition Analysis 12
2.1. Classification of Contact Condition 12
2.1.1. Connected Component Labeling 12
2.1.2. Binary image generation procedure 13
2.1.3. Analysis results for contact condition 14
2.2. Force and Moment depending on Contact Condition 17
Chapter 3. Design Synthesis of Gripper and Scanner 21
3.1. Overall Design Overview 21
3.2. Design and Mechanism of Finger 23
3.2.1. Advantages of parallel mechanism 23
3.2.2. Mechanism description of finger 28
3.2.3. Kinematics of finger 31
3.3 Design and Mechanism of Scanner 33
3.3.1. Mechanism description 33
3.3.2. FEM analysis for deflection compensation 34
Chapter 4. Error Recovery Algorithms 40
4.1. Clustering for Error Estimation 40
4.1.1. K means algorithm 41
4.1.2. Gaussian Mixture Model algorithm 42
4.2. Procedure for Error Recovery 44
4.3. Comparison of Error Recovery Algorithms 45
4.3.1. Comparison of trajectory in blind and XY search 45
4.3.2. Comparison of trajectory for position error recovery 46
4.3.3. Comparison of trajectory for angular error recovery 49
4.3.4. Comparison of variation in recovery time 50
Chapter 5. Experimental Results 52
5.1. Angular Error Measurement of Scanner 52
5.1.1. Verification of scanner accuracy and repeatability 52
5.1.2. Measurement and alignment of angular error 56
5.2. Reaction Moment Measurement at Fingertip 58
5.2.1. Measurement of moment data 58
5.2.2. Description of measurement condition 59
5.2.3. Clustering results from K means algorithm 61
5.2.4. Clustering results from Gaussian Mixture Model Algorithm 64
5.2.5 Comparison of clustering result 69
Chapter 6. Conclusion 71
Bibliography 74
Abstract in Korean 78Docto
Workshop on "Robotic assembly of 3D MEMS".
Proceedings of a workshop proposed in IEEE IROS'2007.The increase of MEMS' functionalities often requires the integration of various technologies used for mechanical, optical and electronic subsystems in order to achieve a unique system. These different technologies have usually process incompatibilities and the whole microsystem can not be obtained monolithically and then requires microassembly steps. Microassembly of MEMS based on micrometric components is one of the most promising approaches to achieve high-performance MEMS. Moreover, microassembly also permits to develop suitable MEMS packaging as well as 3D components although microfabrication technologies are usually able to create 2D and "2.5D" components. The study of microassembly methods is consequently a high stake for MEMS technologies growth. Two approaches are currently developped for microassembly: self-assembly and robotic microassembly. In the first one, the assembly is highly parallel but the efficiency and the flexibility still stay low. The robotic approach has the potential to reach precise and reliable assembly with high flexibility. The proposed workshop focuses on this second approach and will take a bearing of the corresponding microrobotic issues. Beyond the microfabrication technologies, performing MEMS microassembly requires, micromanipulation strategies, microworld dynamics and attachment technologies. The design and the fabrication of the microrobot end-effectors as well as the assembled micro-parts require the use of microfabrication technologies. Moreover new micromanipulation strategies are necessary to handle and position micro-parts with sufficiently high accuracy during assembly. The dynamic behaviour of micrometric objects has also to be studied and controlled. Finally, after positioning the micro-part, attachment technologies are necessary
KNOWLEDGE-BASED APPROACH FOR THE FORMATION OF RE-CONFIGURABLE ASSEMBLY CELLS-A USE CASE STUDY
The current market turbulence has forced the companies to increase their productivity
in order to remain in business, not only to remain competitive. Companies that make
high volume products involving labour-intensive assembly operation normally use
automated assembly since it may reduce the company cost and increase productivity.
Improving productivity is focused in the assembly area since it contributes a bigger
portion of manufacturing cos
NASA space station automation: AI-based technology review
Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures
NASA Tech Briefs, June 1992
Topics covered include: New Product Ideas; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences
A Hybrid and Extendable Self-Reconfigurable Modular Robotic System
Modular robotics has the potential to transform the perception of robotic systems from machines built for specific tasks to multi-purpose tools capable of performing virtually any task. This thesis presents the design, implementation and study of a new self-reconfigurable modular robotic system for use as a research and education platform. The system features a high-speed genderless connector (HiGen), a hybrid module (HyMod), an extensions framework, and a control architecture. The HiGen connector features inter-module communication and is able to join with other HiGen connectors in a manner that allows either side to disconnect in the event of failure. The rapid actuation of HiGen allows connections to be made and broken at a speed that is, to our knowledge, an order of magnitude faster than existing mechanical genderless approaches that feature single-sided disconnect, benefiting the self-reconfiguration time of modular robots. HyMod is a chain, lattice, and mobile hybrid modular robot, consisting of a spherical joint unit that is capable of moving independently and grouping with other units to form arbitrary cubic lattice structures. HyMod is the first module, to our knowledge, that combines efficient single-module locomotion, enabling self-assembly, with the ability for modules to freely rotate within their lattice positions, aiding the self-reconfigurability of large structures. The extension framework is used to augment the capabilities of HyMod units. Extensions are modules that feature specialized functionality, and interface with HyMod units via passive HiGen connectors, allowing them to be un-powered until required for a task. Control of the system is achieved using a software architecture. Based on message routing, the architecture allows for the concurrent use of both centralized and distributed module control strategies. An analysis of the system is presented, and experiments conducted to demonstrate its capabilities. Future versions of the system created by this thesis could see uses in reconfigurable manufacturing, search and rescue, and space exploration
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MODULAR MANIPULATOR FOR ROBOTICS APPLICATIONS
ARM Automation, Inc. is developing a framework of modular actuators that can address the DOE's wide range of robotics needs. The objective of this effort is to demonstrate the effectiveness of this technology by constructing a manipulator from these actuators within a glovebox for Automated Plutonium Processing (APP). At the end of the project, the system of actuators was used to construct several different manipulator configurations, which accommodate common glovebox tasks such as repackaging. The modular nature and quickconnects of this system simplify installation into ''hot'' boxes and any potential modifications or repair therein. This work focused on the development of self-contained robotic actuator modules including the embedded electronic controls for the purpose of building a manipulator system. Both of the actuators developed under this project contain the control electronics, sensors, motor, gear train, wiring, system communications and mechanical interfaces of a complete robotics servo device. Test actuators and accompanying DISC{trademark}s underwent validation testing at The University of Texas at Austin and ARM Automation, Inc. following final design and fabrication. The system also included custom links, an umbilical cord, an open architecture PC-based system controller, and operational software that permitted integration into a completely functional robotic manipulator system. The open architecture on which this system is based avoids proprietary interfaces and communication protocols which only serve to limit the capabilities and flexibility of automation equipment. The system was integrated and tested in the contractor's facility for intended performance and operations. The manipulator was tested using the full-scale equipment and process mock-ups. The project produced a practical and operational system including a quantitative evaluation of its performance and cost
Heterogeneous Robot Swarm β Hardware Design and Implementation
Swarm robotics is one the most fascinating, new research areas in the field of robotics, and one of it's grand challenge is the design of swarm robots that are both heterogeneous and self-sufficient. This can be crucial for robots exposed to environments that are unstructured or not easily accessible for a human operator, such as a collapsed building, the deep sea, or the surface of another planet. In Swarm robotics; self-assembly, self-reconfigurability and self-replication are among the most important characteristics as they can add extra capabilities and functionality to the robots besides the robustness,
flexibility and scalability. Developing a swarm robot system with heterogeneity and larger
behavioral repertoire is addressed in this work. This project is a comprehensive study of the hardware architecture of the homogeneous robot swarm and several problems related to the important aspects of robot's hardware, such as: sensory units, communication among the modules, and hardware components. Most of the hardware platforms used in the swarm robot system are homogeneous and use centralized control architecture for task completion. The hardware architecture is designed and implemented for UB heterogeneous robot swarm with both decentralized and centralized control, depending on the task requirement. Each robot in the UB heterogeneous swarm is equipped with different sensors, actuators, microcontroller and communication modules, which makes them distinct from each other from a hardware point of view. The methodology provides detailed guidelines in designing and implementing the hardware architecture of the heterogeneous UB robot swarm with plug and play approach. We divided the design module into three main categories - sensory modules, locomotion and manipulation, communication and control. We conjecture that the hardware architecture of heterogeneous swarm robots implemented in this work is the most sophisticated and modular design to date
Intelligent gripper design and application for automated part recognition and gripping
Intelligent gripping may be achieved through gripper design, automated part recognition, intelligent algorithm for control of the gripper, and on-line decision-making based on sensory data. A generic framework which integrates sensory data, part recognition, decision-making and gripper control to achieve intelligent gripping based on ABB industrial robot is constructed. The three-fingered gripper actuated by a linear servo actuator designed and developed in this project for precise speed and position control is capable of handling a large variety of objects. Generic algorithms for intelligent part recognition are developed. Edge vector representation is discussed. Object geometric features are extracted. Fuzzy logic is successfully utilized to enhance the intelligence of the system. The generic fuzzy logic algorithm, which may also find application in other fields, is presented. Model-based gripping planning algorithm which is capable of extracting object grasp features from its geometric features and reasoning out grasp model for objects with different geometry is proposed. Manipulator trajectory planning solves the problem of generating robot programs automatically. Object-oriented programming technique based on Visual C++ MFC is used to constitute the system software so as to ensure the compatibility, expandability and modular programming design. Hierarchical architecture for intelligent gripping is discussed, which partitions the robotβs functionalities into high-level (modeling, recognizing, planning and perception) layers, and low-level (sensing, interfacing and execute) layers. Individual system modules are integrated seamlessly to constitute the intelligent gripping system
Basil Leaf Automation
Recent population and wage increases have forced farmers to grow more food without a proportionate increase in work force. Automation is a key factor in reducing cost and increasing efficiency. In this paper, we explore our automation solution that utilizes position manipulation and vision processing to identify, pick up, and drop a leaf into a can. Two stepper motors and a linear actuator drove the three-dimensional actuation. Leaf and can recognition were accomplished through edge detection and machine learning algorithms. Testing proved subsystem-level functionality and proof of concept of a delicate autonomous pick-and-place robot
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