---

In the present article, an improved Learning Classifier Systems (LCS) is proposed to control the balance of a moving unmanned bicycle. Significant characteristics of learning classifier systems is that they can learn through a set of system actions in the real world (similar to intelligent creatures) while no dynamic model of the system is needed. Contrary to studies reported in the literature where action domain of the controller is discrete and accordingly such controller cannot be used in real world applications, in the present study efficacy of the classifier system is enhanced by definition of continuous domain for the outputs, and then is used to control the balance of unmanned bicycle. A scheme based upon fuzzy membership functions is proposed which makes it possible for the domain of actions to be continuous. The proposed LCS features a dynamic reward assignment mechanism which is invented to cope with the bicycle’s delayed response due to its mass inertias. This allows the rapid calculation of the reward and hence enables the controller to be used in such real time applications as the balance control of unmanned vehicles. A standard 2 degree of freedom (2-DOF) bicycle model is employed to demonstrate the efficiency of the enhanced LCS. Simulation results show that the proposed classifier system outperforms traditional classifier system as well as some of the more common balance-control strategies reported in the literature.

---

This paper investigated the imitation of human motions by a NAO humanoid robot which can be regarded as a human-robot interaction research. In this research, first, human motion is captured by a Kinect 3-dimentional camera through a Robot Operating System (ROS) package. Captured motion is then mapped into the robot’s dimension due to the differences between human and humanoid robot dimensions. After performing the mapping procedure, the solution of both forward and inverse kinematic problem of the robot are solved. To this end, a “Distal” form of forward kinematics solution of the NAO humanoid robot is computed and based on the latter form an analytical inverse kinematics solution for the whole-body imitation purpose is used. The foregoing issue, as one of the contributions of this paper, can be regarded as one of the main reason for obtaining a smooth imitation. In order to keep the robot’s stability during the imitation, an ankle strategy based on a Linear Inverted Pendulum Model (LIPM) and the Ground projection of the Center of Mass (GCoM) criteria is introduced. Moreover, the latter LIPM is controlled by a Proportional-Integral-Derivative (PID) controller for two cases, namely, double and single support phases. Considering the limitation on the motion capture device, from experimental and simulation results obtained by implementing the proposed method on a NAO-H25 Version4 it can be inferred that the robot exhibits an accurate, smooth and fast whole-body motion imitation.