138 resultados para Robotic Grasping


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The propeller is the primary propulsion method for underwater vehicles. It is relatively simple to implement and generally uses rotational motion from the drive through to the propeller. However, it is difficult to seal a high speed propeller shaft from water ingress. As an alternative we can look at nature's own underwater inhabitants and study their locomotive methods.

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A randomised trial of robotic and open prostatectomy commenced in October 2010 and is progressing well. Clinical and quality of life outcomes together with economic costs to individuals and the health service are being examined critically to compare outcomes.

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Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm) into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding intermember collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

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Robotic assisted minimally invasive surgery systems not only have the advantages of traditional laparoscopic procedures but also restore the surgeon's hand-eye coordination and improve the surgeon's precision by filtering hand tremors. Unfortunately, these benefits have come at the expense of the surgeon's ability to feel. Several research efforts have already attempted to restore this feature and study the effects of force feedback in robotic systems. The proposed methods and studies have some shortcomings. The main focus of this research is to overcome some of these limitations and to study the effects of force feedback in palpation in a more realistic fashion.

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Vision based tracking of an object using the ideas of perspective projection inherently consists of nonlinearly modelled measurements although the underlying dynamic system that encompasses the object and the vision sensors can be linear. Based on a necessary stereo vision setting, we introduce an appropriate measurement conversion techniques which subsequently facilitate using a linear filter. Linear filter together with the aforementioned measurement conversion approach conforms a robust linear filter that is based on the set values state estimation ideas; a particularly rich area in the robust control literature. We provide a rigorously theoretical analysis to ensure bounded state estimation errors formulated in terms of an ellipsoidal set in which the actual state is guaranteed to be included to an arbitrary high probability. Using computer simulations as well as a practical implementation consisting of a robotic manipulator, we demonstrate our linear robust filter significantly outperforms the traditionally used extended Kalman filter under this stereo vision scenario. © 2008 IEEE.

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OBJECTIVE: To investigate the efficacy and effects of transcranial direct current stimulation (tDCS) on motor imagery brain-computer interface (MI-BCI) with robotic feedback for stroke rehabilitation. DESIGN: A sham-controlled, randomized controlled trial. SETTING: Patients recruited through a hospital stroke rehabilitation program. PARTICIPANTS: Subjects (N=19) who incurred a stroke 0.8 to 4.3 years prior, with moderate to severe upper extremity functional impairment, and passed BCI screening. INTERVENTIONS: Ten sessions of 20 minutes of tDCS or sham before 1 hour of MI-BCI with robotic feedback upper limb stroke rehabilitation for 2 weeks. Each rehabilitation session comprised 8 minutes of evaluation and 1 hour of therapy. MAIN OUTCOME MEASURES: Upper extremity Fugl-Meyer Motor Assessment (FMMA) scores measured end-intervention at week 2 and follow-up at week 4, online BCI accuracies from the evaluation part, and laterality coefficients of the electroencephalogram (EEG) from the therapy part of the 10 rehabilitation sessions. RESULTS: FMMA score improved in both groups at week 4, but no intergroup differences were found at any time points. Online accuracies of the evaluation part from the tDCS group were significantly higher than those from the sham group. The EEG laterality coefficients from the therapy part of the tDCS group were significantly higher than those of the sham group. CONCLUSIONS: The results suggest a role for tDCS in facilitating motor imagery in stroke.

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Micro-robotic cell injection is typically performed manually by a trainedbio-operator, and success rates are often low. To enhance bio-operator performance during real-time cell injection, our earlier work introduced a haptically-enabled micro-robotic cell injection system. The system employed haptic virtual fixtures to provide haptic guidance according to articular performance metrics. This paper extends the work by replicating the system within a virtual reality (VR) environment for bio-operator training. Using the virtual environment, the bio-operator is able to control the virtual injection process in the same way they would with the physical haptic micro-robotic cell injection system, while benefiting from the enhanced visualisation capabilities offered by the 3D VR environment. The system is achieved using cost-effective components offering training at much lower cost than using the physical system.

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The rapid development of virtual reality offers significant potential for skills training applications. Our ongoing work proposes virtual reality operator training for the micro-robotic cell injection procedure. The interface between the operator and the system can be achieved in many different ways. The computer keyboard is ubiquitous in its use for everyday computing applications and also commonly utilized in virtual reality systems. Based on the premise that most people have experience in using a computer keyboard, as opposed to more sophisticated input devices, this paper considers the feasibility of using a keyboard to control the micro-robot for cell injection. In this study, thirteen participants underwent the experimental evaluation. The participants were asked to perform three simulated trial sessions in a virtual micro-robotic cell injection environment. Each session consisted of ten cell injection trials and relevant data for each trial were recorded and analyzed. Results showed participants' performance improvement after the three sessions. It was also observed that participants intuitively controlled multiple axes of the micro-robot simultaneously despite the absence of instruction on how to do so. This continued throughout the experiments and suggests skills transfer from other keyboard based interactions. Based on the results provided, it is suggested that keyboard control is a feasible, simple and low-cost control method for the virtual micro-robot.

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Currently, the micro-robotic cell injection procedure is performed manually by expert human bio-operators. In order to be proficient at the task, lengthy and expensive dedicated training is required. As such, effective specialized training systems for this procedure can prove highly beneficial. This paper presents a comprehensive review of haptic technology relevant to cell injection training and discusses the feasibility of developing such training systems, providing researchers with an inclusive resource enabling the application of the presented approaches, or extension and advancement of the work. A brief explanation of cell injection and the challenges associated with the procedure are first presented. Important skills, such as accuracy, trajectory, speed and applied force, which need to be mastered by the bio-operator in order to achieve successful injection, are then discussed. Then an overview of various types of haptic feedback, devices and approaches is presented. This is followed by discussion on the approaches to cell modeling. Discussion of the application of haptics to skills training across various fields and haptically-enabled virtual training systems evaluation are then presented. Finally, given the findings of the review, this paper concludes that a haptically-enabled virtual cell injection training system is feasible and recommendations are made to developers of such systems.