Relevant bibliographies by topics / Manipulation robotics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Manipulation robotics'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 April 2022

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Journal articles on the topic "Manipulation robotics"

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Mason, Matthew T. "Toward Robotic Manipulation." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 1–28. http://dx.doi.org/10.1146/annurev-control-060117-104848.

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This article surveys manipulation, including both biological and robotic manipulation. Biology inspires robotics and demonstrates aspects of manipulation that are far in the future of robotics. Robotics develops concepts and principles that become evident only in the creative process. Robotics also provides a test of our understanding. As Richard Feynman put it: “What I cannot create, I do not understand.”
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Guo, Wanjin, Ruifeng Li, Yaguang Zhu, Tong Yang, Rui Qin, and Zhixin Hu. "A Robotic Deburring Methodology for Tool Path Planning and Process Parameter Control of a Five-Degree-of-Freedom Robot Manipulator." Applied Sciences 9, no. 10 (May 17, 2019): 2033. http://dx.doi.org/10.3390/app9102033.

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Industrial robotics is a continuously developing domain, as industrial robots have demonstrated to possess benefits with regard to robotic automation solutions in the industrial automation field. In this article, a new robotic deburring methodology for tool path planning and process parameter control is presented for a newly developed five-degree-of-freedom hybrid robot manipulator. A hybrid robot manipulator with dexterous manipulation and two experimental platforms of robot manipulators are presented. A robotic deburring tool path planning method is proposed for the robotic deburring tool position and orientation planning and the robotic layered deburring planning. Also, a robotic deburring process parameter control method is proposed based on fuzzy control. Furthermore, a dexterous manipulation verification experiment is conducted to demonstrate the dexterous manipulation and the orientation reachability of the robot manipulator. Additionally, two robotic deburring experiments are conducted to verify the effectiveness of the two proposed methods and demonstrate the highly efficient and dexterous manipulation and deburring capacity of the robot manipulator.
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Ladig, Robert, Hannibal Paul, Ryo Miyazaki, and Kazuhiro Shimonomura. "Aerial Manipulation Using Multirotor UAV: A Review from the Aspect of Operating Space and Force." Journal of Robotics and Mechatronics 33, no. 2 (April 20, 2021): 196–204. http://dx.doi.org/10.20965/jrm.2021.p0196.

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Aerial manipulation: physical interaction with the environment by using a robotic manipulator attached to the airframe of an aerial robot. In the future one can expect that aerial manipulation will greatly extend the range of possible applications for mobile robotics, especially multirotor UAVs. This can range from inspection and maintenance of previously hard to reach pieces of infrastructure, to search and rescue applications. What kind of manipulator is attached to what position of the airframe is a key point in accomplishing the aerial robot’s function and in the past, various aerial manipulation solutions have been proposed. This review paper gives an overview of the literature on aerial manipulation that have been proposed so far and classifies them by configuration of the workspace and function.
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Prado da Fonseca, Vinicius. "Tactile Sensor Analysis during Early Stages of Manipulation for Single Grasp Identification of Daily Objects." Engineering Proceedings 6, no. 1 (May 17, 2021): 56. http://dx.doi.org/10.3390/i3s2021dresden-10091.

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Dexterous robotic manipulation in unstructured environments is still challenging, despite the increasing number of robots entering human settings each day. Even though robotic manipulation provides complete solutions in factories and industries, it still lacks essential techniques, displaying clumsy or limited operation in unstructured environments. Daily objects typically aim at the human hand, and the human somatosensory system is responsible for solving all the complex calculations required for dexterous manipulations in unstructured settings. Borrowing concepts of the human visuotactile system can improve dexterous manipulation and increase robotics usage in unstructured environments. In humans, required finger and wrist joint adjustments occur after fast identification of the object in the initial stages of manipulation. Fast object identification during those phases may increase robotic dexterous manipulation performance. The present paper explores human-inspired concepts such as haptic glance to develop robotic single-grasp object identification. This concept can assist early phases of robotic manipulation, helping automated decision-making, such as type of grasp and joint position, during manipulation tasks. The main stages developed here are detecting sensor activation and sample collection using signal-to-noise and z-score filtering on tactile data. This procedure automates touch detection and reduces the sensor space for classification. Experiments on a daily objects dataset presented compelling results that will assist in the later stages of the early phases of robotic grasping.
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Gerasimenko, M. Yu, M. A. Eremushkin, M. V. Arkhipov, Yu I. Kolyagin, and I. V. Antonovich. "THE PROSPECTS FOR THE FURTHER DEVELOPMENT OF THE ROBOTIC MANIPULATION MECHANOTHERAPEUTIC COMPLEXES." Russian Journal of Physiotherapy, Balneology and Rehabilitation 16, no. 2 (April 15, 2017): 65–69. http://dx.doi.org/10.18821/1681-3456-2017-16-2-65-69.

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The ongoing active development of robotics in the framework of the new economic paradigm known as «the 4th technological revolution» opens up new possibilities for the progress in various disciplines including medicine. The further improvement and clinical application of robotic manipulation mechanotherapeutic complexes are among the major priorities for the Russian public healthcare system. The article represents the characteristics of the Russian robotic manipulator prototype designed to enhance the effectiveness of medical rehabilitation, develop various options of the robotic situational behavior, and specify the topical issues as regards the improvement of the manipulation robotic systems that require the effective solutions and actions. The prospects for the formation of the basic principles of new specialty «massage programmer» are outlined based on the above considerations.
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Postelnicu, Cristian, Florin Barbuceanu, Tudor Topoleanu, and Doru Talaba. "EOG-Based Interface for Manipulation Tasks." Applied Mechanics and Materials 162 (March 2012): 537–42. http://dx.doi.org/10.4028/www.scientific.net/amm.162.537.

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Recently human-robot interaction (HRI) has become a highly researched topic in robotics, especially in assistive robotics systems. A category of persons that require special attention is represented by the disabled people. For persons suffering of amyotrophic lateral sclerosis or brainstem stroke there are a few available interfaces among which is electrooculography (EOG) that offers an alternative communication channel. In this paper is proposed a paradigm for sending manipulation commands to a virtual robotic arm. A manipulation task is defined by three coordinates in space, the rest of the process being automatically executed. The users are asked to complete a simple task such as pour water into a glass from a bottle by activating the manipulation command.
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Klatzky, Roberta L., Susan J. Lederman, and J. D. Balakrishnan. "Task–Driven Extraction of Object Contour by Human Haptics: Part 1." Robotica 9, no. 1 (January 1991): 43–51. http://dx.doi.org/10.1017/s0263574700015551.

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SUMMARYThe extraction of contour information from objects is essential for purposes of grasping and manipulation. We proposed that human haptic exploration of contours, in the absence of vision, would reveal specialized patterns. Task goals and intrinsic system capacities were assumed to constrain the breadth of processing and the precision with which contour is encoded, thus determining parameters of exploration and ultimately producing movement synergies or “contour exploration procedures.” A methodology for testing these assumptions is described, and the most frequently observed procedures are documented in Part 1. Part 2 will further analyze the procedures, test predictions, and develop implications of the research. The paper (2 parts) is novel in its study of human manipulative behavior from a robotic standpoint; it is thus of interest to robotics research workers interested in the long-term goals of robot manipulation and those interested in an anthropomorphic approach to robotics studies.
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Ibarguren, Aitor, Iveta Eimontaite, José Luis Outón, and Sarah Fletcher. "Dual Arm Co-Manipulation Architecture with Enhanced Human–Robot Communication for Large Part Manipulation." Sensors 20, no. 21 (October 29, 2020): 6151. http://dx.doi.org/10.3390/s20216151.

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The emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.
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Lang, Haoxiang, Muhammad Tahir Khan, Kok-Kiong Tan, and Clarence W. de Silva. "Developments in Visual Servoing for Mobile Manipulation." Unmanned Systems 01, no. 01 (June 20, 2013): 143–62. http://dx.doi.org/10.1142/s2301385013300011.

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A new trend in mobile robotics is to integrate visual information in feedback control for facilitating autonomous grasping and manipulation. The result is a visual servo system, which is quite beneficial in autonomous mobile manipulation. In view of mobility, it has wider application than the traditional visual servoing in manipulators with fixed base. In this paper, the state of art of vision-guided robotic applications is presented along with the associated hardware. Next, two classical approaches of visual servoing: image-based visual servoing (IBVS) and position-based visual servoing (PBVS) are reviewed; and their advantages and drawbacks in applying to a mobile manipulation system are discussed. A general concept of modeling a visual servo system is demonstrated. Some challenges in developing visual servo systems are discussed. Finally, a practical application of mobile manipulation system which is developed for applications of search and rescue and homecare robotics is introduced.
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Veliev, E. I., R. F. Ganiev, V. A. Glazunov, and G. S. Filippov. "Parallel and sequential structures of manipulators in robotic surgery." Доклады Академии наук 485, no. 2 (May 20, 2019): 166–70. http://dx.doi.org/10.31857/s0869-56524852166-170.

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The problems of modern robotics associated with the requirements for devices designed for various purposes are considered. The daVinci robotic surgical manipulation system is analyzed. The developed robotic system with a parallel structure designed for various kinds of surgical operations is proposed.
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Dissertations / Theses on the topic "Manipulation robotics"

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Huckaby, Jacob O. "Knowledge transfer in robot manipulation tasks." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51902.

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Technology today has progressed to the point that the true potential of robotics is beginning to be realized. However, programming robots to be robust across varied environments and objectives, in a way that is accessible and intuitive to most users, is still a difficult task. There remain a number of unmet needs. For example, many existing solutions today are proprietary, which makes widespread adoption of a single solution difficult to achieve. Also, most approaches are highly targeted to a specific implementation. But it is not clear that these approaches will generalize to a wider range of problems and applications. To address these issues, we define the Interaction Space, or the space created by the interaction between robots and humans. This space is used to classify relevant existing work, and to conceptualize these unmet needs. GTax, a knowledge transfer framework, is presented as a solution that is able to span the Interaction Space. The framework is based on SysML, a standard used in many different systems, which provides a formalized representation and verification. Through this work, we demonstrate that by generalizing across the Interaction Space, we can simplify robot programming and enable knowledge transfer between processes, systems and application domains.
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Berenson, Dmitry. "Constrained Manipulation Planning." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/172.

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Every planning problem in robotics involves constraints. Whether the robot must avoid collision or joint limits, there are always states that are not permissible. Some constraints are straightforward to satisfy while others can be so stringent that feasible states are very difficult to find. What makes planning with constraints challenging is that, for many constraints, it is impossible or impractical to provide the planning algorithm with the allowed states explicitly; it must discover these states as it plans. The goal of this thesis is to develop a framework for representing and exploring feasible states in the context of manipulation planning. Planning for manipulation gives rise to a rich variety of tasks that include constraints on collision- avoidance, torque, balance, closed-chain kinematics, and end-effector pose. While many researchers have developed representations and strategies to plan with a specific constraint, the goal of this the- sis is to develop a broad representation of constraints on a robot’s configuration and identify general strategies to manage these constraints during the planning process. Some of the most important con- straints in manipulation planning are functions of the pose of the manipulator’s end-effector, so we devote a large part of this thesis to end-effector placement for grasping and transport tasks. We present an efficient approach to generating paths that uses Task Space Regions (TSRs) to specify manipulation tasks which involve end-effector pose goals and/or path constraints. We show how to use TSRs for path planning using the Constrained BiDirectional RRT (CBiRRT2) algorithm and describe several extensions of the TSR representation. Among them are methods to plan with object pose uncertainty, find optimal base placements, and handle more complex pose constraints by chaining TSRs together. We also explore the problem of automatically generating end-effector pose constraints for grasping tasks and present two grasp synthesis algorithms that can generate lists of grasps in extremely clut- tered environments. We then describe how to convert these lists of grasps to TSRs so they can be used with CBiRRT2. We have applied our framework to a wide range of problems for several robots, both in simulation and in the real world. These problems include grasping in cluttered environments, lifting heavy objects, two-armed manipulation, and opening doors, to name a few. These example problems demonstrate our framework’s practicality, and our proof of probabilistic completeness gives our approach a theoretical foundation. In addition to the above framework, we have also developed the Constellation algorithm for finding configurations that satisfy multiple stringent constraints where other constraint-satisfaction strategies fail. We also present the GradienT-RRT algorithm for planning with soft constraints, which outper- forms the state-of-the-art approach to high-dimensional path planning with costs.
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Ziesmer, Jacob Ames. "Reconfigurable End Effector Allowing For In-Hand Manipulation Without Finger Gaiting Or Regrasping." [Milwaukee, Wis.] : e-Publications@Marquette, 2009. http://epublications.marquette.edu/theses_open/2.

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Güler, Püren. "Learning Object Properties From Manipulation for Manipulation." Doctoral thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207154.

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The world contains objects with various properties - rigid, granular, liquid, elastic or plastic. As humans, while interacting with the objects, we plan our manipulation by considering their properties. For instance, while holding a rigid object such as a brick, we adapt our grasp based on its centre of mass not to drop it. On the other hand while manipulating a deformable object, we may consider additional properties to the centre of mass such elasticity, brittleness etc. for grasp stability. Therefore, knowing object properties is an integral part of skilled manipulation of objects.  For manipulating objects skillfully, robots should be able to predict the object properties as humans do. To predict the properties, interactions with objects are essential. These interactions give rise distinct sensory signals that contains information about the object properties. The signals coming from a single sensory modality may give ambiguous information or noisy measurements. Hence, by integrating multi-sensory modalities (vision, touch, audio or proprioceptive), a manipulated object can be observed from different aspects and this can decrease the uncertainty in the observed properties. By analyzing the perceived sensory signals, a robot reasons about the object properties and adjusts its manipulation based on this information. During this adjustment, the robot can make use of a simulation model to predict the object behavior to plan the next action. For instance, if an object is assumed to be rigid before interaction and exhibit deformable behavior after interaction, an internal simulation model can be used to predict the load force exerted on the object, so that appropriate manipulation can be planned in the next action. Thus, learning about object properties can be defined as an active procedure. The robot explores the object properties actively and purposefully by interacting with the object, and adjusting its manipulation based on the sensory information and predicted object behavior through an internal simulation model. This thesis investigates the necessary mechanisms that we mentioned above to learn object properties: (i) multi-sensory information, (ii) simulation and (iii) active exploration. In particular, we investigate these three mechanisms that represent different and complementary ways of extracting a certain object property, the deformability of objects. Firstly, we investigate the feasibility of using visual and/or tactile data to classify the content of a container based on the deformation observed when a robotic hand squeezes and deforms the container. According to our result, both visual and tactile sensory data individually give high accuracy rates while classifying the content type based on the deformation. Next, we investigate the usage of a simulation model to estimate the object deformability that is revealed through a manipulation. The proposed method identify accurately the deformability of the test objects in synthetic and real-world data. Finally, we investigate the integration of the deformation simulation in a robotic active perception framework to extract the heterogenous deformability properties of an environment through physical interactions. In the experiments that we apply on real-world objects, we illustrate that the active perception framework can map the heterogeneous deformability properties of a surface.

QC 20170517

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McEachern, Wendy A. "Manipulation strategies for applications in rehabilitation robotics." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389955.

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Arnekvist, Isac. "Reinforcement learning for robotic manipulation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216386.

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Reinforcement learning was recently successfully used for real-world robotic manipulation tasks, without the need for human demonstration, usinga normalized advantage function-algorithm (NAF). Limitations on the shape of the advantage function however poses doubts to what kind of policies can be learned using this method. For similar tasks, convolutional neural networks have been used for pose estimation from images taken with fixed position cameras. For some applications however, this might not be a valid assumption. It was also shown that the quality of policies for robotic tasks severely deteriorates from small camera offsets. This thesis investigates the use of NAF for a pushing task with clear multimodal properties. The results are compared with using a deterministic policy with minimal constraints on the Q-function surface. Methods for pose estimation using convolutional neural networks are further investigated, especially with regards to randomly placed cameras with unknown offsets. By defining the coordinate frame of objects with respect to some visible feature, it is hypothesized that relative pose estimation can be accomplished even when the camera is not fixed and the offset is unknown. NAF is successfully implemented to solve a simple reaching task on a real robotic system where data collection is distributed over several robots, and learning is done on a separate server. Using NAF to learn a pushing task fails to converge to a good policy, both on the real robots and in simulation. Deep deterministic policy gradient (DDPG) is instead used in simulation and successfully learns to solve the task. The learned policy is then applied on the real robots and accomplishes to solve the task in the real setting as well. Pose estimation from fixed position camera images is learned and the policy is still able to solve the task using these estimates. By defining a coordinate frame from an object visible to the camera, in this case the robot arm, a neural network learns to regress the pushable objects pose in this frame without the assumption of a fixed camera. However, the precision of the predictions were too inaccurate to be used for solving the pushing task. Further modifications to this approach could however show to be a feasible solution to randomly placed cameras with unknown poses.
Reinforcement learning har nyligen använts framgångsrikt för att lära icke-simulerade robotar uppgifter med hjälp av en normalized advantage function-algoritm (NAF), detta utan att använda mänskliga demonstrationer. Restriktioner på funktionsytorna som använts kan dock visa sig vara problematiska för generalisering till andra uppgifter. För poseestimering har i liknande sammanhang convolutional neural networks använts med bilder från kamera med konstant position. I vissa applikationer kan dock inte kameran garanteras hålla en konstant position och studier har visat att kvaliteten på policys kraftigt förvärras när kameran förflyttas.   Denna uppsats undersöker användandet av NAF för att lära in en ”pushing”-uppgift med tydliga multimodala egenskaper. Resultaten jämförs med användandet av en deterministisk policy med minimala restriktioner på Q-funktionsytan. Vidare undersöks användandet av convolutional neural networks för pose-estimering, särskilt med hänsyn till slumpmässigt placerade kameror med okänd placering. Genom att definiera koordinatramen för objekt i förhållande till ett synligt referensobjekt så tros relativ pose-estimering kunna utföras även när kameran är rörlig och förflyttningen är okänd. NAF appliceras i denna uppsats framgångsrikt på enklare problem där datainsamling är distribuerad över flera robotar och inlärning sker på en central server. Vid applicering på ”pushing”- uppgiften misslyckas dock NAF, både vid träning på riktiga robotar och i simulering. Deep deterministic policy gradient (DDPG) appliceras istället på problemet och lär sig framgångsrikt att lösa problemet i simulering. Den inlärda policyn appliceras sedan framgångsrikt på riktiga robotar. Pose-estimering genom att använda en fast kamera implementeras också framgångsrikt. Genom att definiera ett koordinatsystem från ett föremål i bilden med känd position, i detta fall robotarmen, kan andra föremåls positioner beskrivas i denna koordinatram med hjälp av neurala nätverk. Dock så visar sig precisionen vara för låg för att appliceras på robotar. Resultaten visar ändå att denna metod, med ytterligare utökningar och modifikationer, skulle kunna lösa problemet.
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Jentoft, Leif Patrick. "Sensing and Control for Robust Grasping with Simple Hardware." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11657.

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Robots can move, see, and navigate in the real world outside carefully structured factories, but they cannot yet grasp and manipulate objects without human intervention. Two key barriers are the complexity of current approaches, which require complicated hardware or precise perception to function effectively, and the challenge of understanding system performance in a tractable manner given the wide range of factors that impact successful grasping. This thesis presents sensors and simple control algorithms that relax the requirements on robot hardware, and a framework to understand the capabilities and limitations of grasping systems.
Engineering and Applied Sciences
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Dogar, Mehmet R. "Physics-Based Manipulation Planning in Cluttered Human Environments." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/310.

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This thesis presents a series of planners and algorithms for manipulation in cluttered human environments. The focus is on using physics-based predictions, particularly for pushing operations, as an effective way to address the manipulation challenges posed by these environments. We introduce push-grasping, a physics-based action to grasp an object first by pushing it and then closing the fingers. We analyze the mechanics of push-grasping and demonstrate its effectiveness under clutter and object pose uncertainty. We integrate a planning system based on push-grasping to the geometric planners traditionally used in grasping. We then show that a similar approach can be used to perform manipulation with environmental contact in cluttered environments. We present a planner where the robot can simultaneously push multiple obstacles out of the way while grasping an object through clutter. In the second part of this thesis we focus on planning a sequence of actions to manipulate clutter. We present a planning framework to rearrange clutter using prehensile and nonprehensile primitives. We show that our planner succeeds in environments where planners which only use prehensile primitives fail. We then explore the problem of manipulating clutter to search for a hidden object. We formulate the problem as minimizing the expected time to find the target, present two algorithms, and analyze their complexity and optimality.
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Dong, Shen. "Virtual manipulation." School of Electrical, Computer and Telecommunications Engineering - Faculty of Informatics, 2008. http://ro.uow.edu.au/theses/141.

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An empirical research on developing a new paradigm for programming a robotics manipulator to perform complex constrained motion tasks is carried out in this thesis. The teaching of the manipulation skills to the machine commences by demonstrating those skills in a haptic-rendered virtual environment. This is in contrast to conventional approach in which a robotics manipulator is programmed to perform a particular task. A manipulation skill consists of a number of basic skills that, when sequenced and integrated, can perform a desired manipulation task. By manipulation means the ability to transfer, physically transform or mate a part with another part. Haptic-rendering augments the effectiveness of computer simulation by providing force feedback for the user. This increases the quality of human － computer interaction and provides an attractive augmentation to visual display and significantly enhances the level of immersion in a virtual environment. The study is conducted based on the peg-in-hole application as it concisely represents a constrained motion-force-sensitive manufacturing task with all the attendant issues of jamming, tight clearances, and the need for quick assembly times, reliability, etc. The state recognition approach is used to identify and classify the skills acquired from the virtual environment. A human operator demonstrates both good and bad examples of the desired behaviour in the haptic virtual environment. Position and contact force and torque ii data, as well as orientation generated in the virtual environment, combined with a priori knowledge about the task, are used to identify and learn the skills in the newly demonstrated tasks and then to reproduce them in the robotics system. The robot evaluates the controller’s performance and thus learns the best way to produce that behaviour. The data obtained from the virtual environment is classified into different cluster sets using the Hidden Markov Model (HMM), Fuzzy Gustafson–Kessel (FGK) and Competitive Agglomeration (CA) respectively. Each cluster represents a contact state between the peg and the hole. The clusters in the optimum cluster set are tuned using a Locally Weighted Regression (LWR) algorithm to produce prediction models for robot trajectory performing the physical assembly based on the force/position information received from the rig. The significance of the work is highlighted. The approach developed and the outcomes achieved are reported. The development of the haptic-rendered virtual peg-in-hole model and structure of the physical experimental rig are described. The approach is validated though experimental work results are critically evaluated. Keywords: Haptic, PHANToM, ReachIn, Virtual Reality, Peg-in-hole, Skill acquisition.
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Lu, Su. "Subtask Automation in Robotic Surgery: Needle Manipulation for Surgical Suturing." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1607429591883517.

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Books on the topic "Manipulation robotics"

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1961-, Li Zexiang, and Sastry Shankar, eds. A mathematical introduction to robotic manipulation. Boca Raton: CRC Press, 1994.

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Robotic grasping and fine manipulation. Boston: Kluwer Academic Publishers, 1985.

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Schilling, Robert J. Robotic manipulation: Programming and simulation studies. Englewood Cliffs, N.J: Prentice-Hall, 1990.

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N, Bolotnik N., and Gradet͡s︡kiǐ V. G, eds. Manipulation robots: Dynamics, control, and optimization. Boca Raton: CRC Press, 1994.

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Vukobratović, Miomir. Applied control of manipulation robots: Analysis, synthesis, and exercises. Berlin: Springer-Verlag, 1989.

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Vukobratović, Miomir. Applied control of manipulation robots: Analysis, synthesis and exercises. Berlin: Springer-Verlag, 1989.

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author, Li Guangyong joint, ed. Introduction to nanorobotic manipulation and assembly. Boston: Artech House, 2012.

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Fukuda, Toshio. Micro-Nanorobotic Manipulation Systems and Their Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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D, Stokić, and Kirćanski N. 1953-, eds. Non-adaptive and adaptive control of manipulation robots. Berlin: Springer-Verlag, 1985.

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1954-, Kirćanski M., ed. Kinematics and trajectory synthesis of manipulation robots. Berlin: Springer-Verlag, 1986.

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Book chapters on the topic "Manipulation robotics"

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Karouta, Jeremy, Ángela Ribeiro, and Dionisio Andújar. "Robotics: Manipulation." In Manuali – Scienze Tecnologiche, 25. Florence: Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-044-3.25.

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This topic will talk about the different ways in which robotics can interact with its environment. It will dive into different actions necessary such as seeding, weeding, picking, spraying. It will not discuss the mechanical aspects in high details, but will be more in depth about the use of sensors and the decision making and reasoning of the way of work. It will also discuss the choices that need to be considered while designing for specific applications. Then this topic will also briefly mention data and monitoring applications, where the focus is mainly on the software capabilities, and not the hardware.
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Bayle, Bernard, and Laurent Barbé. "Tele-manipulation." In Medical Robotics, 269–302. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118562147.ch8.

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Prattichizzo, Domenico, Maria Pozzi, and Monica Malvezzi. "Dexterous Manipulation." In Encyclopedia of Robotics, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-41610-1_180-1.

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Kappassov, Zhanat. "Active Manipulation." In Encyclopedia of Robotics, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-642-41610-1_178-1.

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Pierri, Francesco, and Antonio Franchi. "Cooperative Manipulation." In Encyclopedia of Robotics, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-642-41610-1_177-1.

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Bruyninckx, Herman. "Session 12: Manipulation." In Experimental Robotics, 535–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00196-3_61.

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Caccavale, Fabrizio, and Masaru Uchiyama. "Cooperative Manipulation." In Springer Handbook of Robotics, 989–1006. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32552-1_39.

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Siciliano, Bruno, and Luigi Villani. "Control and Manipulation." In Mechatronics and Robotics, 81–104. Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429347474-5.

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de Gea Fernández, José, Elie Allouis, Karol Seweryn, Frank Kirchner, and Yang Gao. "Manipulation and Control." In Contemporary Planetary Robotics, 255–320. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2016. http://dx.doi.org/10.1002/9783527684977.ch5.

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Bolopion, Aude, and Michaël Gauthier. "Micro/Nano-Manipulation." In Encyclopedia of Robotics, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-41610-1_181-1.

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Conference papers on the topic "Manipulation robotics"

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Lee, Hoyul, Yonghwan Oh, Woong Hee Shon, and Youngjin Choi. "Stackable manipulator for mobile manipulation robot." In 2012 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2012. http://dx.doi.org/10.1109/icra.2012.6224793.

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Bishop, Bradley E. "Swarm-based object manipulation using redundant manipulator analogs." In 2008 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2008. http://dx.doi.org/10.1109/robot.2008.4543413.

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Gandolfo, F., G. Sandini, and M. Tistarelli. "Towards vision guided manipulation." In Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments. IEEE, 1991. http://dx.doi.org/10.1109/icar.1991.240674.

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Tan, ChingSeong, ChiaLoon Cheng, and KokSiang Chia. "In-Vitro Optically Aided Robotics Manipulation." In 2011 IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2011. http://dx.doi.org/10.1109/ramech.2011.6070505.

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Buican, Tudor N., Dan L. Neagley, William C. Morrison, and Bryan D. Upham. "Optical Trapping, Cell Manipulation, And Robotics." In OE/LASE '89, edited by Gary C. Salzman. SPIE, 1989. http://dx.doi.org/10.1117/12.951915.

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Ananthanarayanan, S. P., A. A. Goldberg, and J. Mylopoulos. "Dextrous manipulation using qualitative reasoning. II. Modelling and synthesis of finger manipulative synergies." In Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments. IEEE, 1991. http://dx.doi.org/10.1109/icar.1991.240669.

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Hou, Yifan, Zhenzhong Jia, and Matthew Mason. "Manipulation with Shared Grasping." In Robotics: Science and Systems 2020. Robotics: Science and Systems Foundation, 2020. http://dx.doi.org/10.15607/rss.2020.xvi.086.

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Mianowski, Krzysztof, and Teresa Zielinska. "Parallel manipulator POLMAN with isotropic properties dedicated for fast manipulation." In 2006 IEEE Conference on Robotics, Automation and Mechatronics. IEEE, 2006. http://dx.doi.org/10.1109/ramech.2006.252640.

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Ishimi, K., Y. Ohtsuki, T. Manabe, and K. Nakashima. "Manipulation system for subsea operation." In Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments. IEEE, 1991. http://dx.doi.org/10.1109/icar.1991.240455.

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Lu, Zhe, Xuping Zhang, Clement Leung, Navid Esfandiari, Robert F. Casper, and Yu Sun. "Automated cell manipulation: Robotic ICSI." In 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2011. http://dx.doi.org/10.1109/icra.2011.5980241.

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Reports on the topic "Manipulation robotics"

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Joseph W. Geisinger, Ph D. MODULAR MANIPULATOR FOR ROBOTICS APPLICATIONS. Office of Scientific and Technical Information (OSTI), July 2001. http://dx.doi.org/10.2172/810614.

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Allen, Peter. Intelligent Sensor-Based Manipulation with Robotic Hands. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada357655.

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Ramsower, D. C. Test results for robotic manipulator EMMA. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/670061.

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Pekala, Miles C., and Derek Scherer. Construction of a Hyper Redundant Robotic Tentacle Manipulator. Fort Belvoir, VA: Defense Technical Information Center, June 2012. http://dx.doi.org/10.21236/ada568730.

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BENNETT, PHIL C., and ROBERT J. ANDERSON. Robotic Mobile Manipulation Experiments at the U.S. Army Maneuver Support Center. Office of Scientific and Technical Information (OSTI), June 2002. http://dx.doi.org/10.2172/810622.

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Pomranky, Regina A., Keryl Cosenzo, Andrew Bodenhamer, and Brad Pettijohn. Experimental Evaluation of Computer-Aided Tele-operation (CATO) and Computer-Aided Robotic Manipulation (CARMAN) Technology. Fort Belvoir, VA: Defense Technical Information Center, August 2010. http://dx.doi.org/10.21236/ada526373.

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Ronzhin, Andrey, and Quyen Vu. Control Algorithm of a Robotic Gripper with a Vacuum Bellows for Manipulating Tomatoes. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, May 2021. http://dx.doi.org/10.7546/crabs.2021.05.12.

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Siskind, Jeffrey M. Emergent Intelligent Behavior through Integrated Investigation of Embodied Natural Language, Reasoning, Learning, Computer Vision, and Robotic Manipulation. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada551162.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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Abstract:
The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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