In this paper, the application of the policy gradient Reinforcement Learning-based (RL) method for obstacle avoidance is proposed. This method was successfully used to control the movements of a robot using trial-and-error interactions with its environment. In this paper, an approach based on a Deep Deterministic Policy Gradient (DDPG) algorithm combined with a Hindsight Experience Replay (HER) algorithm for avoiding obstacles has been investigated. In order to ensure that the robot avoids obstacles and reaches the desired position as quickly and as accurately as possible, a special approach to the training and architecture of two RL agents working simultaneously was proposed. The implementation of this RL-based approach was first implemented in a simulation environment, which was used to control the 6-axis robot simulation model. Then, the same algorithm was used to control a real 6-DOF (degrees of freedom) robot. The results obtained in the simulation were compared with results obtained in laboratory conditions.
The article describes investigation of rolling ball stabilization problem on a biaxial platform. The aim of the control system proposed here is to stabilize ball moving on a plane in equilibrium point. The authors proposed a control algorithm based on cascade PID and they compared it with another control method. The article shows the results of the accuracy of ball stabilization and influence of applied filter on the signal waveform. The application used to detect the ball position measured by digital camera has been written using a cross platform .Net wrapper to the OpenCV image processing library - EmguCV. The authors used the bipolar stepper motor with dedicated electronic controller. The data between the computer and the designed controller are sent with use of the RS232 standard. The control stand is based on ATmega series microcontroller.
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