927 resultados para ROBOTS INDUSTRIALES


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This paper outlines some rehabilitation applications of manipulators and identifies that new approaches demand that the robot make an intimate contact with the user. Design of new generations of manipulators with programmable compliance along with higher level controllers that can set the compliance appropriately for the task, are both feasible propositions. We must thus gain a greater insight into the way in which a person interacts with a machine, particularly given that the interaction may be non-passive. We are primarily interested in the change in wrist and arm dynamics as the person co-contracts his/her muscles. It is observed that this leads to a change in stiffness that can push an actuated interface into a limit cycle. We use both experimental results gathered from a PHANToM haptic interface and a mathematical model to observe this effect. Results are relevant to the fields of rehabilitation and therapy robots, haptic interfaces, and telerobotics

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As healthcare costs rise and an aging population makes an increased demand on services, so new techniques must be introduced to promote an individuals independence and provide these services. Robots can now be designed so they can alter their dynamic properties changing from stiff to flaccid, or from giving no resistance to movement, to damping any large and sudden movements. This has some strong implications in health care in particular for rehabilitation where a robot must work in conjunction with an individual, and might guiding or assist a persons arm movements, or might be commanded to perform some set of autonomous actions. This paper presents the state-of-the-art of rehabilitation robots with examples from prosthetics, aids for daily living and physiotherapy. In all these situations there is the potential for the interaction to be non-passive with a resulting potential for the human/machine/environment combination to become unstable. To understand this instability we must develop better models of the human motor system and fit these models with realistic parameters. This paper concludes with a discussion of this problem and overviews some human models that can be used to facilitate the design of the human/machine interfaces.

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A number of Intelligent Mobile Robots have been developed at the University of Reading. They are completely autonomous in that no umbilical cord attaches to them to extra power supplies or computer station: further, they are not radio controlled. In this paper, the robots are discussed, in their various forms, and the individual behaviours and characteristics which appear are considered.

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The whole concept of just what is and what is not, intelligence is a vitally important one. As humans interact more with machines, so the similarities and differences between human and machine intelligence need to be looked at in a sensible, scientific way. This paper considers human and machine intelligence and links them closely to physical characteristics, as exhibited by robots. Potential interfaces between humans and machines are also considered, as is the state of the art in direct physical links between humans and machines.

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The problem of a manipulator operating in a noisy workspace and required to move from an initial fixed position P0 to a final position Pf is considered. However, Pf is corrupted by noise, giving rise to Pˆf, which may be obtained by sensors. The use of learning automata is proposed to tackle this problem. An automaton is placed at each joint of the manipulator which moves according to the action chosen by the automaton (forward, backward, stationary) at each instant. The simultaneous reward or penalty of the automata enables avoiding any inverse kinematics computations that would be necessary if the distance of each joint from the final position had to be calculated. Three variable-structure learning algorithms are used, i.e., the discretized linear reward-penalty (DLR-P, the linear reward-penalty (LR-P ) and a nonlinear scheme. Each algorithm is separately tested with two (forward, backward) and three forward, backward, stationary) actions.

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A three degrees of freedom industrial robot is controlled by applying PID self-tuning (PID/ST) controllers. This control is considered as a corrective term to a nominal value, centrally computed from an inaccurate and/ or simplified dynamic model. An identification scheme on an assumed linear plant describing the deviation from the desired trajectory is employed in order to tune the controller coefficients and thus accomplish a behaviour prescribed through a desired pole placement. A salient feature of our approach is the decentralized nature of the controllers producing the corrective term for each joint. This opens the way to practical implementation, as recent computing requirement calculations for similar set-ups have shown in the literature. Numerical results are presented.

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In this article, four different practical experiments in robotics and human/machine merger are firstly described and then considered with regard to their ethical implications. Results from the experiments are discussed in terms of their meaning and application possibilities. The article is written from the perspective of scientific experimentation, opening up realistic possibilities to be faced in the future rather than giving conclusive comments on the technologies employed. Human implantation and the merger of biology and technology are key elements.

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A low cost, compact embedded design approach for actuating soft robots is presented. The complete fabrication procedure and mode of operation was demonstrated, and the performance of the complete system was also demonstrated by building a microcontroller based hardware system which was used to actuate a soft robot for bending motion. The actuation system including the electronic circuit board and actuation components was embedded in a 3D-printed casing to ensure a compact approach for actuating soft robots. Results show the viability of the system in actuating and controlling siliconebased soft robots to achieve bending motions. Qualitative measurements of uniaxial tensile test, bending distance and pressure were obtained. This electronic design is easy to reproduce and integrate into any specified soft robotic device requiring pneumatic actuation.

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Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF algorithm that is capable of processing two-dimensional maps containing up to 1.8 k features at real time (14 Hz), a three-fold improvement over a Pentium M 1.6 GHz, and a 13-fold improvement over an ARM920T 200 MHz. The proposed architecture also consumes only 1.3% of the Pentium and 12.3% of the ARM energy per feature.

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Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

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We present a novel scheme for node localization in a Delay-Tolerant Sensor Network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a Robust Extended Kalman Filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1m in a large indoor setting.

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In this paper we present a technique based on precision guidance approach for the sensor delivery and reception problem between two mobile robots. A slave robot is employed to collect sensors and slack them on a tray carried by the mobile master robot. We define the terminal attitude of the slave robot with respect to the master and present a LQR control approach to solving the problem of achieving a desired terminal approach angle necessary for the appropriate sensor delivery. The approach criteria is defined in terms of both minimizing the miss distance and controlling the slave robot's body attitude with respect to the master robot at the terminal point.