During the last decade, students of mechanical engineering, electrical engineering, and mechatronics have been interested in studying robotics. It is a challenge for robotics course professors to get students to clearly understand the basic theoretical concepts for its correct application to solve problems, and to motivate them to achieve the best performance. Previous works proposed the use of 3D models to perform the simulation of industrial robots in class and consequently to reinforce the students’ theoretical and practical concepts. However, it has not yet been clearly demonstrated how the use of robots and simulations helps improve students’ academic performance. In this paper, the main contribution consists of a formal evaluation in which the comparison between the use of two teaching methods was carried out. In the first teaching method (traditional method), the professor used PowerPoint presentations and oral explanations to teach theoretical concepts and solve exercises (Group A). In the second teaching method, the professor added the use of 3D simulations together with the traditional method to teach theoretical concepts and solve exercises (Group B). Students of both groups performed the same written exam about forward kinematics in order to study and analyse the differences between the methodology used with Group A and the methodology used with Group B. This exam consisted of solving three exercises with different robot complexity levels. The results demonstrate that the performance of the students in group B is significantly better than that of the students in group A. In other words, the use of 3D simulations improves student’s academic performance, and provides a teaching tool to enhance the way the professors teach.

In this paper, we propose a stable neurovisual servoing algorithm for set-point control of planar robot manipulators in a fixed-camera configuration an show that all the closed-loop signals are uniformly ultimately bounded (UUB) and converge exponentially to a small compact set. We assume that the gravity term and Jacobian matrix are unknown. Radial basis function neural networks (RBFNNs) with online real-time learning are proposed for compensating both gravitational forces and errors in the robot Jacobian matrix. The learning rule for updating the neural network weights, similar to a back propagation algorithm, is obtained from a Lyapunov stability analysis. Experimental results on a two degrees of freedom manipulator are presented to evaluate the proposed controller.

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