International Journal of Robotics
Volume:4 Issue: 4, Winter-Spring 2016

In this paper, an innovative mobile platform is presented which is equipped by three new wheels. The core of the new idea is to establish a new design of rigid circular structure which can be implemented as a wheel by variable radius. The structure of wheel includes a circular pattern of a simple two-link mechanism assembled to obtain a wheel shape. Each wheel has two degrees of freedom. The first is to rotate wheel axis and the second is to change the wheel radius. As the first step, after definition of the new model, its spatial kinematics and constraints will be formulated. The well-known Newton-Raphson algorithm is implemented to find the current response of the kinematic model. A semi-dynamic formulation is further utilized to find the torque of motors for adapting the required wheel radius for maneuverability improvement on rough surfaces. The principles of virtual work will be used to extract the torque values numerically. The ability of the proposed robot for performing the required tasks will finally be checked by some simulations.

In this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. It is assumed that the robot performs navigation in unknown environment and builds the map through SLAM procedure. Since the moving objects' location and properties are unknown, they are identified and tracked by Kalman filter. Kalman observer provides important information about next paths of moving objects which are employed in finding collision point and time in future. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads the robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. Besides, comparison between Escaping Algorithm and Probabilistic Velocity Obstacle, based on computational complexity and required steps for finishing the mission is provided in this paper. The results show Escaping Algorithm outperforms PVO in term of dynamic obstacle avoidance and complexity as a practical method for autonomous navigation Abstract—In this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. It is assumed that the robot performs navigation in unknown environment and builds the map through SLAM procedure. Since the moving objects' location and properties are unknown, they are identified and tracked by Kalman filter. Kalman observer provides important information about next paths of moving objects which are employed in finding collision point and time in future. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads the robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. Besides, comparison between Escaping Algorithm and Probabilistic Velocity Obstacle, based on computational complexity and required steps for finishing the mission is provided in this paper. The results show Escaping Algorithm outperforms PVO in term of dynamic obstacle avoidance and complexity as a practical method for autonomous navigation.

This case study investigates the effects of Robot Assisted Language Learning (RALL) on English vocabulary learning and retention of Iranian children with high-functioning autism. Two groups of three male students (6-10 years old) with high-functioning autism participated in the current study. The humanoid robot NAO was used as a teacher assistant to teach English to the RALL group. Both RALL and non-RALL programs consisted of 12 sessions held within a 2-month period. Using a pre-test, mid-test, immediate post-test, delayed post-test design, this study measured the learning gains of the participants. The RALL group outperformed the non-RALL group in the designed tests which showed the effectiveness of RALL. This was further supported by comparing and contrasting the RALL and non-RALL groups’ parents’ feedbacks as well as the results obtained from the qualitative analysis of the video records. The findings of this study could be a starting point for a new line of research in second/foreign language education specific to children with autism.

In this paper an automatic examination robot was developed to improve the process of cancer detection, tumor localization and geometrical shape diagnosis. A uniformly distributed compressive load was applied to the top tissue surface and the resultant mechanical stress was measured that was employed for the tumor diagnosis task. The experimental examinations were performed on the soft tissue of the liver. A compression test was used to extract viscoelastic properties of tissue. Viscoelastic coefficients were used in the finite element modeling and the capability of the robotic-assisted tumor detection procedure was verified. Finally to localize the tumor embedded in the tissue, two sinusoidal and step paths were generated which was followed by the robot. The mean errors of path following by the automatic examination robot affirmed the accuracy and the reliability of the Cartesian mechanism in the soft tissue scanning.

In this research, first, kinematic and dynamic equations of a 4-DOF 3-link robotic finger are derived using Denavit-Hartenberg convention and Lagrange’s formulation. To model the muscles, several springs and dampers are placed between the finger links. Then, two advanced controllers, namely adaptive-robust and adaptive-neural, which can control the robotic finger in presence of parametric uncertainty, are applied to the dynamic model of the system in order to track the desired trajectory of tapping. The simulation of the dynamic system is performed in presence of  10% uncertainty in the parameters of the system and the results are obtained when applying the two controllers separately on the robotic finger dynamic model. By comparing the simulation results of the tracking errors, it is observed that both controllers perform decently; however, the adaptive-neural controller has a better performance.

Soft tissue modeling for the creation of a haptic simulator for training medical skills has been the focus of many attempts up to now. In soft tissue modeling the most important parameter considered is its being real-time, as well as its accuracy and sensitivity. In this paper, ANFIS approach is used to present a nonlinear model for soft tissue. The required data for training the neuro-fuzzy model of soft tissue is provided from breast tissue numerical modeling in ANSYS 12.0 software. To validate the ANSYS mode, numerical data have been compared with the experimental data with an average error of less than 3%. On the other hand, for the validation of ANFIS model, testing session indicates a root mean square error of less than 0.02 (N), which shows the high degree of accuracy for the presented model. To evaluate the efficiency of this model, it has been used in the breast cancerous tumors diagnosis training haptic simulator. The presented model’s real-time feature is about 100 times more than the maximum amount needed for force modeling simulations.