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In this study, considering slippage between a robot end-effector and an object, adaptive control of a one-finger hand manipulating an object is explored. This system is a good sample to develop different techniques such as grasp analysis, grasp synthesis, stability analysis and designing different types of controller for cooperative manipulator systems. Due to the presence of inequality equations in frictional point contact modeling, a novel formulation is developed to replace the equality and inequality equations with a single second order differential equation with switching coefficients. Introducing this new friction contact model, an input-output conventional form is derived using the equality and inequality equations of motion of the system. Using this new form of motion equations, two adaptive controllers with simple update laws are proposed that both of them ensure the asymptotic convergence of the object position tracking as well as slippage control while compensating the system uncertainties. The first controller compensates the uncertain masses of the manipulator links and the object while the second one compensates the uncertain coefficients of friction. Numerical simulation is utilized to evaluate performance of the proposed controllers.

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Computer modeling of human behavior is an interesting branch in motor control science and it has attracted many researchers in the neuroscience and bioengineering field. Having a good perception of the role of Central Nervous System (CNS) and its strategies in planning and controlling of human movements will improve the bioengineering topics such as rehabilitation protocols and sport techniques. In present research a computer simulation of CNS's performance in designing the Sit-to-Stand transfer is developed. The mention simulation is based on decomposition hypothesis. Decomposition hypothesis sates that the CNS decomposes a complicated movements to several simpler phases. According to this hypothesis a modular and hierarchical movement planner (MHMP), which has been recently presented, is modified to describe the function of CNS in planning the Sit-to-Stand different phases under combination of different environmental conditions. The performance of the modified MHMP is evaluated with experimental captured motion. The results show that the original MHMP has a good performance in planning the motion phases under single environmental condition but it fails under a combination of different conditions, while the modified MHMP shows good performance in such cases.

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Simultaneous localization and mapping (SLAM) is a fundamental problem in autonomous robotic. Many algorithms have been exploited to solve this problem, among these algorithms, FastSLAM is one of the most widely used and Unscented FastSLAM is one of the newest. Although in several scientific researches it is stated that Unscented FastSLAM outperforms FastSLAM, there are still unexamined potentials regarding Unscented FastSLAM. Therefore, this paper seeks to improve the overall performance of Unscented FastSLAM. Map accuracy and quality directly depend on the accuracy of localization and observations. In SLAM algorithms, robot pose is predicted using motion model, and then corrected using the difference between map features and recently observed features. Accuracy of pose estimation may improve by comparing two sequential observations and modifying robot pose to result in best match between them. This method is called scan matching and has been successfully combined with FastSLAM algorithm and some other SLAM algorithms not including Unscented FastSLAM. Therefore, this paper seeks to investigate the performance of Unscented FastSLAM combined with scan matching. Simulation results show that combining Unscented FastSLAM with scan match significantly improves accuracy of localization and mapping.

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This paper presents a new Neuro-fuzzy control system to control rigid-flexible manipulators. Enhancing the performance of fuzzy controller and intelligence in fuzzy and non-fuzzy units are the goals of this research. Proposed control system includes a fuzzy controller in the feedback and a neural network is the feed-forward. The network has the responsibility of estimating the inverse dynamic of device and then, the production of control command. Updating weighting coefficients of network is done on line using the fuzzy controller output. On the other hand, two dynamic recurrent neural networks are used for making fuzzy unit intelligent. Networks are responsible for regulating the main factors of membership functions in the fuzzy controller. The input of these networks is error and error change rate and their weights are done by using an error back-propagation algorithm. To verify the effectiveness of the proposed method, simulation is conducted for skilled manipulators with three interfaces which the end interface is flexible. System responses to step input and sinusoidal input are separately obtained for fuzzy controllers and proposed controller and compared. Comparison and studies indicate the effectiveness of the provided method.