Relevant bibliographies by topics / Robot control

Academic literature on the topic 'Robot control'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Robot control"

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Karpas, Erez, and Daniele Magazzeni. "Automated Planning for Robotics." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 417–39. http://dx.doi.org/10.1146/annurev-control-082619-100135.

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Modern robots are increasingly capable of performing “basic” activities such as localization, navigation, and motion planning. However, for a robot to be considered intelligent, we would like it to be able to automatically combine these capabilities in order to achieve a high-level goal. The field of automated planning (sometimes called AI planning) deals with automatically synthesizing plans that combine basic actions to achieve a high-level goal. In this article, we focus on the intersection of automated planning and robotics and discuss some of the challenges and tools available to employ automated planning in controlling robots. We review different types of planning formalisms and discuss their advantages and limitations, especially in the context of planning robot actions. We conclude with a brief guide aimed at helping roboticists choose the right planning model to endow a robot with planning capabilities.
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Han, Jong-Ho. "Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control." Electronics 9, no. 1 (December 21, 2019): 14. http://dx.doi.org/10.3390/electronics9010014.

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This paper proposes fuzzy gain scheduling of proportional differential control (FGS-PD) system for tracking mobile robot to moving sound sources. Given that the target positions of the real-time moving sound sources are dynamic, the mobile robots should be able to estimate the target points continuously. In such a case, the robots tend to slip owing to abnormal velocities and abrupt changes in the tracking path. The selection of an appropriate curvature along which the robot follows a sound source makes it possible to ensure that the robot reaches the target sound source precisely. For enabling the robot to recognize the sound sources in real time, three microphones are arranged in a straight formation. In addition, by applying the cross correlation algorithm to the time delay of arrival base, the received signals can be analyzed for estimating the relative positions and velocities of the mobile robot and the sound source. Even if the mobile robot is navigating along a curved path for tracking the sound source, there could be errors due to the inertial and centrifugal forces resulting from the motion of the mobile robot. Velocities of both robot wheels are controlled using FGS-PD control to compensate for slippage and to minimize tracking errors. For experimentally verifying the efficacy of the proposed the control system with sound source estimation, two mobile robots were fabricated. It was demonstrated that the proposed control method effectively reduces the tracking error of a mobile robot following a sound source.
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Kazerooni, H. "Compliance Control and Stability Analysis of Cooperating Robot manipulators." Robotica 7, no. 3 (July 1989): 191–98. http://dx.doi.org/10.1017/s0263574700006044.

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SUMMARYThe work presented here is the description of the control strategy of two cooperating robots. A two–finger hand is an example of such a System. The control method allows for position control of the contact point by one of the robots while the other robot controls the contact force. The stability analysis of two robot manipulators has been investigated using unstructured models for dynamic behavior of robot manipulators. For the stability of two robots, there must be some initial compliance in either robot. The initial compliance in the robots can be obtained by a non-zero sensitivity function for the tracking controller or a passive compliant element such as an RCC.
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Rios, Jorge D., Daniel Ríos-Rivera, Jesus Hernandez-Barragan, Marco Pérez-Cisneros, and Alma Y. Alanis. "Formation Control of Mobile Robots Based on Pin Control of Complex Networks." Machines 10, no. 10 (October 6, 2022): 898. http://dx.doi.org/10.3390/machines10100898.

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Robot formation control has several advantages that make it interesting for research. Multiple works have been published in the literature using different control approaches. This work presents the control of different groups of robots to achieve a desired formation based on pinning control of complex networks and coordinate translation. The implemented control law comprises complex network bounding, proportional, and collision avoidance terms. The tests for this proposal were performed via simulation and experimental tests, considering different networks of differential robots. The selected robots are Turtlebot3® Waffle Pi robots. The Turtlebot3® Waffle Pi is a differential mobile robot with the Robot Operating System (ROS). It has a light detection and ranging (LiDAR) sensor used to compute the collision avoidance control law term. Tests show favorable results on different formations testing on various groups of robots, each composed of a different number of robots. From this work, implementation on other devices can be derived, as well as trajectory tracking once in formation, among other applications.
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Ravichandar, Harish, Athanasios S. Polydoros, Sonia Chernova, and Aude Billard. "Recent Advances in Robot Learning from Demonstration." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 297–330. http://dx.doi.org/10.1146/annurev-control-100819-063206.

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In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neither easily scripted (as is done in traditional robot programming) nor easily defined as an optimization problem, but can be demonstrated. While there have been multiple surveys of this field in the past, there is a need for a new one given the considerable growth in the number of publications in recent years. This review aims to provide an overview of the collection of machine-learning methods used to enable a robot to learn from and imitate a teacher. We focus on recent advancements in the field and present an updated taxonomy and characterization of existing methods. We also discuss mature and emerging application areas for LfD and highlight the significant challenges that remain to be overcome both in theory and in practice.
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Butler, John Travis, and Arvin Agah. "Control of a Mobile Service Robot Using Human Evaluations of Task-related Movement Patterns." Journal of Robotics and Mechatronics 12, no. 6 (December 20, 2000): 689–701. http://dx.doi.org/10.20965/jrm.2000.p0689.

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An important future application of robotics will be the utilization of mobile service robots in homes and offices, assisting people with their daily chores. Above all, these robots must be safe to use. In addition, service robots must be designed to be effective, productive, and user-friendly. In order for people to accept and use these robots, the robots must behave in a manner acceptable to humans. The intelligent control of service robots must take into. account the effects of robot behaviors on people. This paper focuses on the interactions between humans and mobile service robots, studying how people respond to a variety of robot behaviors as the robot performs certain tasks. Since different people could react differently to service robots, this paper reports on the effects of users' gender, age, technical background, and robot body preference on the responses to robot behaviors. The robot behaviors include the robot approaching a human, the robot avoiding a human while passing, and the robot performing non-interactive behaviors. The level of comfort the robot caused human subjects was analyzed according to the effects of robot speed, robot distance, and robot body design. It is hoped that information gained from human factor studies can be used to obtain a better understanding of acceptability of service robots by different people, resulting in the design and development of more effective intelligent controllers for service robots in the coming new generation.
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Kumar, P. Vishun, Golla Chatrapati, Shivaji Shivaji, Mangali Jagadeesh, Saritala Abishaik, and Pocha Devendra Reddy. "Speech control Robot using NodeMCU." Journal of Communication Engineering and its Innovations 8, no. 2 (June 27, 2022): 1–6. http://dx.doi.org/10.46610/jocei.2022..2022.v08i02.001.

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The movement of robot controlling via IoT technology. The field of robotic technology is implemented in many domains. Specific tasks are performed by robots which humans cannot and also humans are takes more time to complete. Robots are followed human instructions and perform the tasks such as security operations, act as spy robots etc. In this paper, we discuss about a smart robotic vehicle that operates on human voice commands, given remotely by using an Android platform based smart IoT device. The robotic assistant is developed on a NodeMCU ESP8266 micro-controller based platform. The voice commands are carried out and this signal is converted to text format and then communicated through Wi-Fi network. This robot is able to move in different directions like left, right, stop, backward, forward.
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Peng, Huan Xin, Bin Liu, and De Hong Xu. "Robot Flocking Control with Part Information of the Virtual Leader." Applied Mechanics and Materials 364 (August 2013): 352–56. http://dx.doi.org/10.4028/www.scientific.net/amm.364.352.

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Under virtual leader-follower model, when all robots can receive the information of the virtual leader, the robot flocking algorithm can avoid diverging. For time-delay, noise and network congestion in the communication, in the fact, only part robots can receive the information of the virtual leader. In the paper, we analyze the performance of robot flocking control algorithm with part information of the virtual leader. We analyze the impact brought by the parameters on the robot flocking control when only part robots can receive the information of the virtual leader, and simulations are done. Results show that the performance of distributed robot flocking algorithm depends on the probability of robot receiving the information of the virtual leader, the communication radius among robots, and density of robot and so on.
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Kress-Gazit, Hadas, Morteza Lahijanian, and Vasumathi Raman. "Synthesis for Robots: Guarantees and Feedback for Robot Behavior." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 211–36. http://dx.doi.org/10.1146/annurev-control-060117-104838.

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Robot control for tasks such as moving around obstacles or grasping objects has advanced significantly in the last few decades. However, controlling robots to perform complex tasks is still accomplished largely by highly trained programmers in a manual, time-consuming, and error-prone process that is typically validated only through extensive testing. Formal methods are mathematical techniques for reasoning about systems, their requirements, and their guarantees. Formal synthesis for robotics refers to frameworks for specifying tasks in a mathematically precise language and automatically transforming these specifications into correct-by-construction robot controllers or into a proof that the task cannot be done. Synthesis allows users to reason about the task specification rather than its implementation, reduces implementation error, and provides behavioral guarantees for the resulting controller. This article reviews the current state of formal synthesis for robotics and surveys the landscape of abstractions, specifications, and synthesis algorithms that enable it.
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Xing, Guansheng, and Weichuan Meng. "Design of Robot Vision Servo Control System Based on Image." Journal of Physics: Conference Series 2136, no. 1 (December 1, 2021): 012049. http://dx.doi.org/10.1088/1742-6596/2136/1/012049.

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Abstract Visual servo is a closed-loop control system of robot, which takes the image information obtained by visual sensor as feedback. Generally speaking, visual servo plays an important role in robot control, which is one of the main research directions in the field of robot control and plays a decisive role in the development of intelligent robots. In order to make the robot competent for more complex tasks and work more intelligently, autonomously and reliably, it is necessary not only to improve the control system of the robot, but also to obtain more and better information about the working environment of the robot. This paper introduces the principle and basic realization method of robot visual servo based on image, and expounds the problems and solutions in image feature extraction and visual servo controller design. In order to further expand the application field of robots and improve the operation performance of robots, robots must have higher intelligence level and stronger adaptability to the environment, so as to manufacture intelligent robots that can replace human labor.
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Dissertations / Theses on the topic "Robot control"

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Majors, Michael David. "Iterative robot control." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625008.

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Bishop, Russell C. "A Method for Generating Robot Control Systems." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1222394834.

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Celikkanat, Hande. "Control Of A Mobile Robot Swarm Via Informed Robots." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609966/index.pdf.

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In this thesis, we study how and to what extent a self-organized mobile robot flock can be guided by informing some of the robots within the flock about a preferred direction of motion. Specifically, we extend a flocking behavior that was shown to maneuver a swarm of mobile robots as a cohesive group in free space, avoiding obstacles. In its original form, this behavior does not have a preferred direction and the flock would wander aimlessly. In this study, we incorporate a preference for a goal direction in some of the robots. These informed robots do not signal that they are informed (a.k.a. unacknowledged leadership) and instead guide the swarm by their tendency to move in the desired direction. Through experimental results with physical and simulated robots we show that the self-organized flocking of a robot swarm can be effectively guided by an informed minority of the flock. We evaluate the system using a number of quantitative metrics: First, we propose to use the mutual information metric from Information Theory as a dynamical measure of the information exchange. Then, we discuss the accuracy metric from directional statistics and size of the largest cluster as the measures of system performance. Using these metrics, we perform analyses from two points of views: In the transient analyses, we demonstrate the information exchange between the robots as the time advances, and the increase in the accuracy of the flock when the conditions are suitable for an adequate amount of information exchange. In the steady state analyses, we investigate the interdependent effects of the size of the flock in terms of the robots in it, the ratio of informed robots in the flock over the total flock size, the weight of the direction preference behavior, and the noise in the system.
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Dentler, Donald Richard II. "Design, Control, and Implementation of a Three Link Articulated Robot Arm." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1217208877.

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Smith, Brian Stephen. "Automatic coordination and deployment of multi-robot systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28248.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Dr. Magnus Egerstedt; Committee Co-Chair: Dr. Ayanna Howard; Committee Member: Dr. David Taylor; Committee Member: Dr. Frank Dellaert; Committee Member: Dr. Ian Akyildiz; Committee Member: Dr. Jeff Shamma.
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Wang, Zongyao. "Distributed robot flocking control." Thesis, University of Essex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499765.

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Jou, Yung-Tsan. "Human-Robot Interactive Control." Ohio University / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1082060744.

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Macdonald, Edward A. "Multi-robot assignment and formation control." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41200.

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Our research focuses on one of the more fundamental issues in multi-agent, mobile robotics: the formation control problem. The idea is to create controllers that cause robots to move into a predefined formation shape. This is a well studied problem for the scenario in which the robots know in advance to which point in the formation they are assigned. In our case, we assume this information is not given in advance, but must be determined dynamically. This thesis presents an algorithm that can be used by a network of mobile robots to simultaneously determine efficient robot assignments and formation pose for rotationally and translationally invariant formations. This allows simultaneous role assignment and formation sysnthesis without the need for additional control laws. The thesis begins by introducing some general concepts regarding multi-agent robotics. Next, previous work and background information specific to the formation control and assignment problems are reviewed. Then the proposed assignment al- gorithm for role assignment and formation control is introduced and its theoretical properties are examined. This is followed by a discussion of simulation results. Lastly, experimental results are presented based on the implementation of the assignment al- gorithm on actual robots.
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Sequeira, Gerard. "Vision based leader-follower formation control for mobile robots." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.mst.edu/thesis/pdf/Sequeira_09007dcc804429d4.pdf.

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Thesis (M.S.)--University of Missouri--Rolla, 2007.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed February 13, 2008) Includes bibliographical references (p. 39-41).
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Gaskett, Chris, and cgaskett@it jcu edu au. "Q-Learning for Robot Control." The Australian National University. Research School of Information Sciences and Engineering, 2002. http://thesis.anu.edu.au./public/adt-ANU20041108.192425.

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Q-Learning is a method for solving reinforcement learning problems. Reinforcement learning problems require improvement of behaviour based on received rewards. Q-Learning has the potential to reduce robot programming effort and increase the range of robot abilities. However, most currentQ-learning systems are not suitable for robotics problems: they treat continuous variables, for example speeds or positions, as discretised values. Discretisation does not allow smooth control and does not fully exploit sensed information. A practical algorithm must also cope with real-time constraints, sensing and actuation delays, and incorrect sensor data. This research describes an algorithm that deals with continuous state and action variables without discretising. The algorithm is evaluated with vision-based mobile robot and active head gaze control tasks. As well as learning the basic control tasks, the algorithm learns to compensate for delays in sensing and actuation by predicting the behaviour of its environment. Although the learned dynamic model is implicit in the controller, it is possible to extract some aspects of the model. The extracted models are compared to theoretically derived models of environment behaviour. The difficulty of working with robots motivates development of methods that reduce experimentation time. This research exploits Q-learning’s ability to learn by passively observing the robot’s actions—rather than necessarily controlling the robot. This is a valuable tool for shortening the duration of learning experiments.
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Books on the topic "Robot control"

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Canudas de Wit, Carlos, ed. Advanced Robot Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/bfb0039262.

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Siciliano, Bruno, and Luigi Villani. Robot Force Control. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4431-9.

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Shevlin, F. Robot trajectory control. Dublin: Trinity College, Department of Computer Science, 1992.

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1966-, Villani Luigi, ed. Robot force control. Boston: Kluwer Academic, 1999.

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Siciliano, Bruno. Robot Force Control. Boston, MA: Springer US, 1999.

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János, Somló. Advanced robot control. Budapest: Akadémiai Kiadó, 1997.

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Lewis, Frank L. Control of robot manipulators. New York: Macmillan Pub. Co., 1993.

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Martins, Nardênio Almeida, and Douglas Wildgrube Bertol. Wheeled Mobile Robot Control. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-77912-2.

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de Wit, Carlos Canudas, Bruno Siciliano, and Georges Bastin, eds. Theory of Robot Control. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1501-4.

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Kozłowski, Krzysztof, ed. Robot Motion and Control. London: Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-405-2.

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Book chapters on the topic "Robot control"

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Bajd, Tadej, Matjaž Mihelj, Jadran Lenarčič, Aleš Stanovnik, and Marko Munih. "Robot control." In Robotics, 77–95. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3776-3_7.

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Mihelj, Matjaž, Tadej Bajd, Aleš Ude, Jadran Lenarčič, Aleš Stanovnik, Marko Munih, Jure Rejc, and Sebastjan Šlajpah. "Robot Control." In Robotics, 133–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72911-4_10.

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Siciliano, Bruno, and Luigi Villani. "Motion Control." In Robot Force Control, 7–29. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4431-9_2.

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Siciliano, Bruno, and Luigi Villani. "Indirect Force Control." In Robot Force Control, 31–64. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4431-9_3.

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Siciliano, Bruno, and Luigi Villani. "Direct Force Control." In Robot Force Control, 65–87. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4431-9_4.

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Song, Bongsob, and J. Karl Hedrick. "Biped Robot Control." In Communications and Control Engineering, 217–32. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-632-0_9.

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Castel, C., J. P. Chretien, G. Favier, M. Fliess, A. J. Fossard, M. Gauvrit, B. Gimonet, et al. "Robot Control Techniques." In The Digital Control of Systems, 383–429. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4615-6853-7_17.

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Colomé, Adrià, and Carme Torras. "Robot Compliant Control." In Springer Tracts in Advanced Robotics, 53–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26326-3_4.

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Spong, Mark W. "Robot Motion Control." In Encyclopedia of Systems and Control, 1168–77. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_168.

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Chaumette, François. "Robot Visual Control." In Encyclopedia of Systems and Control, 1188–94. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_170.

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Conference papers on the topic "Robot control"

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Ryu, Ji-Chul, Kaustubh Pathak, and Sunil K. Agarwal. "Control of a Passive Mobility Assistive Robot." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14701.

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In this paper, a control methodology for a mobility assistive robot is presented. There are various types of robots that can help the disabled. Among these, mobile robots can help to guide a subject from one place to the other. Broadly, the mobile guidance robots can be classified into active and passive type. From a user's safety point of view, passive mobility assistive robots are more desirable than the active robots. In this paper, a two-wheeled differentially driven mobile robot with a castor wheel is considered as the assistive robot. The robot is made to have passive mobility characteristics by a specific choice of control law which creates damper-like resistive forces on the wheels. The paper describes the dynamic model, the suggested control laws to achieve a passive behavior, and experiments on a mobile robot facility at the University of Delaware. From a starting position, the assistive device guides the user to the goal in two phases. In the first phase, the user is guided to reach a goal position while pushing the robot through a handle attached to it. At the end of this first phase, the robot may not have the desired orientation. In the second phase, it is assumed that the user does not apply any further pushing force while the robot corrects the heading angle. A control algorithm is suggested for each phase. In the second phase, the desired heading angle is achieved at the cost of deviation from the final position. This excursion from the goal position is minimized by the controller. This control scheme is first verified in computer simulation. Then, it is implemented on a laboratory system and the experimental results are presented.
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Zheng, Huanfei, Zhanrui Liao, and Yue Wang. "Human-Robot Trust Integrated Task Allocation and Symbolic Motion Planning for Heterogeneous Multi-Robot Systems." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9161.

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This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of parallel subtasks. Parallel subtask specifications are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. A dynamic Bayesian network (DBN) based human-robot trust model is constructed considering individual robot performance, safety coefficient, human cognitive workload and overall evaluation of task allocation. Hence, a task allocation path with maximum encoded human-robot trust can be searched based on the current trust value of each robot in the task allocation automaton. Symbolic motion planning (SMP) is implemented for each robot after they obtain the sequence of actions. The task allocation path can be intermittently updated with this DBN based trust model. The overall strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask automata.
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Warren, Stephen, and Panagiotis Artemiadis. "Bio-Inspired Robot Control for Human-Robot Bi-Manual Manipulation." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3834.

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As robots are increasingly used in human-cluttered environments, the requirement of human-likeness in their movements becomes essential. Although robots perform a wide variety of demanding tasks around the world in factories, remote sites and dangerous environments, they are still lacking the ability to coordinate with humans in simple, every-day life bi-manual tasks, e.g. removing a jar lid. This paper focuses on the introduction of bio-inspired control schemes for robot arms that coordinate with human arms in bi-manual manipulation tasks. Using data captured from human subjects performing a variety of every-day bi-manual life tasks, we propose a bio-inspired controller for a robot arm, that is able to learn human inter- and intra-arm coordination during those tasks. We embed human arm coordination in low-dimension manifolds, and build potential fields that attract the robot to human-like configurations using the probability distributions of the recorded human data. The method is tested using a simulated robot arm that is identical in structure to the human arm. A preliminary evaluation of the approach is also carried out using an anthropomorphic robot arm in bi-manual manipulation task with a human subject.
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Pollard, Beau, and Phanindra Tallapragada. "Fish Like Aquatic Robot Demonstrates Characteristics of a Linear System." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9764.

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In the recent past the design of many aquatic robots has been inspired by the motion of fish. In some recent work the authors described an underactuated planar swimming robot, that is propelled via the motion of an internal rotor. This robot is inspired by a simplified model of the fluid-body interaction mediated by singular distributions of vorticity. Such a model is a significant simplification of the fluid-structure interaction that can be understood using resource intensive numerical computations of the Navier Stokes equation that are unwieldy from a controls perspective. At the same time the simplified model incorporates the creation of vorticity and interaction of the body with the vorticity which many control theoretical models ignore. In this paper we show that despite the complexity of the interaction between the aquatic robot and the ambient vorticity in a fluid, the response of the robot is a nearly linear function of the control input. This surprisingly simple feature emerges in our theoretical model and is validated by our experimental data of the motion of the robot. This simplifying observation is an important step towards developing control algorithms for aquatic robots.
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Mascaro, Stephen. "A Modular 2-DOF Serial Robot Manipulator for Education in Robot Control." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9878.

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This paper describes a modular 2-DOF serial robot manipulator and accompanying experiments that have been developed to introduce students to the fundamentals of robot control. The robot is designed to be safe and simple to use, and to have just enough complexity (in terms of nonlinear dynamics) that it can be used to showcase and compare the performance of a variety of textbook robot control techniques including computed torque feedforward control, inverse dynamics control, robust sliding-mode control, and adaptive control. These various motion control schemes can be easily implemented in joint space or operational space using a MATLAB/Simulink real-time interface. By adding a simple 2-DOF force sensor to the end-effector, the robot can also be used to showcase a variety of force control techniques including impedance control, admittance control, and hybrid force/position control. The 2-DOF robots can also be used in pairs to demonstrate control architectures for multi-arm coordination and master/slave teleoperation. This paper will describe the 2-DOF robot and control hardware/software, illustrate the spectrum of robot control methods that can be implemented, and show sample results from these experiments.
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Shakouri, Payman, Gordana Collier, and Andrzej Ordys. "Teaching control using NI Starter Kit Robot." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334775.

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Elshazly, Osama, Ahmed Abo-Ismail, Hossam S. Abbas, and Zakarya Zyada. "Skid steering mobile robot modeling and control." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915116.

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Angatkina, Oyuna, Kimberly Gustafson, Aimy Wissa, and Andrew Alleyne. "Path Following for the Soft Origami Crawling Robot." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9175.

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Abstract Extensive growth of the soft robotics field has made possible the application of soft mobile robots for real world tasks such as search and rescue missions. Soft robots provide safer interactions with humans when compared to traditional rigid robots. Additionally, soft robots often contain more degrees of freedom than rigid ones, which can be beneficial for applications where increased mobility is needed. However, the limited number of studies for the autonomous navigation of soft robots currently restricts their application for missions such as search and rescue. This paper presents a path following technique for a compliant origami crawling robot. The path following control adapts the well-known pure pursuit method to account for the geometric and mobility constraints of the robot. The robot motion is described by a kinematic model that transforms the outputs of the pure pursuit into the servo input rotations for the robot. This model consists of two integrated sub-models: a lumped kinematic model and a segmented kinematic model. The performance of the path following approach is demonstrated for a straight-line following simulation with initial offset. Finally, a feedback controller is designed to account for terrain or mission uncertainties.
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Marzban, Mostapha, and Aria Alasty. "Stability Control of an Amphibious Single Wheel Robot." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-44020.

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Single wheel robots are typically those kinds of robots which contain all the necessary mechanizations, namely the stabilization and driving mechanizations, within a shell-liked housing appearing analogous to a wheel. These robots have proved to be useful in various fields of industry due to their advantages of giving high instant acceleration and maintaining high cruise speeds for considerable amount of time in addition to being compact and small. It is a sharp-edged wheel actuated by a spinning flywheel for steering and a drive motor for propulsion. The spinning flywheel acts as a gyroscope to stabilize the robot and it can be tilted to achieve steering. In this paper first the kinematics of a single wheel robot, like Gyrover, in water is considered and then a simple mechanism for its movement in water is proposed. After hydrodynamic analysis of the robot a complete dynamics model is designed with Lagrange energy method. Then a stabilizer controller is designed to balance the robot with nonlinear control approach. For simplicity the added mass effect in hydrodynamic analysis, has been neglected. This complete model can be used for examining the behavior of the robot in designing a controller.
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Haghshenas-Jaryani, Mahdi, Hakki Erhan Sevil, and Liang Sun. "Navigation and Obstacle Avoidance of Snake-Robot Guided by a Co-Robot UAV Visual Servoing." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3156.

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Abstract This paper presents the concept of teaming up snake-robots, as unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs) for autonomous navigation and obstacle avoidance. Snake robots navigate in cluttered environments based on visual servoing of a co-robot UAV. It is assumed that snake-robots do not have any means to map the surrounding environment, detect obstacles, or self-localize, and these tasks are allocated to the UAV, which uses visual sensors to track the UGVs. The obtained images were used for the geo-localization and mapping the environment. Computer vision methods were utilized for the detection of obstacles, finding obstacle clusters, and then, mapping based on Probabilistic Threat Exposure Map (PTEM) construction. A path planner module determines the heading direction and velocity of the snake robot. A combined heading-velocity controller was used for the snake robot to follow the desired trajectories using the lateral undulatory gait. A series of simulations were carried out for analyzing the snake-robot’s maneuverability and proof-of-concept by navigating the snake robot in an environment with two obstacles based on the UAV visual servoing. The results showed the feasibility of the concept and effectiveness of the integrated system for navigation.
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Reports on the topic "Robot control"

1

Nasr, Chaiban. Neural Networks For Robot Control. Fort Belvoir, VA: Defense Technical Information Center, April 2001. http://dx.doi.org/10.21236/ada387882.

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Williamson, Matthew M. Exploiting Natural Dynamics in Robot Control. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada457056.

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Gage, Douglas W. Command Control for Many-Robot Systems. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada422540.

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George Danko. Integrated Robot-Human Control in Mining Operations. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/988569.

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George Danko. INTEGRATED ROBOT-HUMAN CONTROL IN MINING OPERATIONS. Office of Scientific and Technical Information (OSTI), April 2005. http://dx.doi.org/10.2172/882518.

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George Danko. INTEGRATED ROBOT-HUMAN CONTROL IN MINING OPERATIONS. Office of Scientific and Technical Information (OSTI), April 2006. http://dx.doi.org/10.2172/882519.

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Falco, Joe, Jeremy Marvel, Rick Norcross, and Karl Van Wyk. Benchmarking Robot Force Control Capabilities: Experimental Results. National Institute of Standards and Technology, January 2016. http://dx.doi.org/10.6028/nist.ir.8097.

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Blackburn, Michael R., and Hoa G. Nguyen. Autonomous Visual Control of a Mobile Robot. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada422533.

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Starr, G. Sensor-driven robot control and mobility: Final report. Office of Scientific and Technical Information (OSTI), May 1989. http://dx.doi.org/10.2172/5912296.

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Arkin, Ronald C., Frank Dellaert, and Joan Devassy. Envisioning: Mental Rotation-based Semi-reactive Robot Control. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada563085.

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