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In this paper, the forward kinematics of a parallel manipulator with three revolute-prismatic-spherical (3RPS), is analyzed using a combination of a numerical method with semi-analytical Homotopy Continuation Method (HCM) that due to its fast convergence, permits to solve forward kinematics of robots in real-time applications. The revolute joints of the proposed robot are actuated and direct kinematics equations of the manipulator leads to a system of three nonlinear equations with three unknowns that need to be solved. In this paper a fast and efficient Method, called the Ostrowski-HCM has been used to solve the direct kinematics equations of this parallel manipulator. This method has some advantages over conventional numerical iteration methods. Firstly, it is the independency in choosing the initial values and secondly, it can find all solutions of equations without divergence just by changing auxiliary Homotopy functions. Numerical example and simulation that has been done to solve the direct kinematic equations of the 3-RPS parallel manipulator leads to 7 real solutions. Results indicate that this method is more effective than other conventional Homotopy Continuation Methods such as Newton-HCM and reduces computation time by 77-97 % with more accuracy in solution in comparison with the Newton-HCM. Thus, it is appropriate for real-time applications.

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This paper proposes a two phase strategy for proportional myoelectric control of Surena 3 humanoid robot which benefits from strength of two common myoelectric control methods, Pattern recognition base and simultaneous proportional control, for improving joint angle estimation. The aim of this research is to present a human-robot interface to create a mapping between electrical activities of muscles known as electromyogram (EMG) signals and kinematics of corresponding motion. First phase concerns with motion classification using Quadratic Discriminant Analysis (QDA) and Majority Voting (MV). Several common motion classification algorithms and feature vectors including time domain and frequency domain futures were investigated which lead to QDA and a superior feature vector with more than 97% classification accuracy. The second phase concerns with continuous angle estimation of shoulder joint motion classes using Time Delayed Artificial Neural Network (TDANN) with overall accuracy of 90% R2. QDA serves as a high level controller which decides between four TDANN correspond to each shoulder motion classes. QDA and TDANN models trained with several sets of offline data and were tested with online dataset. Online and offline data estimation accuracy and model robustness against disturbances show a significant improvement compared to similar methods in this field.