DYNAMICS BASED CONTROL OF A SKID STEERING MOBILE ROBOT

In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller

Reinforcement Learning for Robotic Applications with Vision Feedback

The presence of robots in our daily life is becoming more common, where robots start carrying out more complex tasks. This increase in the complexity of tasks makes conventional control system insufficient. Therefore, a plausible approach is required for robots to learn how to perform these tasks. Reinforcement learning enables robots to perform complex tasks without highly engineered control systems. However, using reinforcement learning in robotic applications is challenged by several problems such as high dimensionality. Thus, in this paper, we study the performance of the Hindsight Experience Replay (HER) algorithm which addresses the high dimensionality problem. In this paper, we analyze the algorithm performance using a simulated robotic arm to pick and place different objects. Then, we propose the use of vision feedback which is used to control the gripper of the robotic arm. The results and analysis highlights some of HER limitations when dealing with objects that have limited grasping points. Our proposed method allows the robotic arm to pick objects using the same trained policy without the need to retrain the agent for new objects. Finally, we prove that using our method the robotic arm can pick the objects with higher success rate compared to the one without vision feedback

GPR Signal Processing with Geography Adaptive Scanning using Vector Radar for Antipersonal Landmine Detection

Ground Penetrating Radar (GPR) is a promising sensor for landmine detection, however there are two major problems to overcome. One is the rough ground surface. The other problem is the distance between the antennas of GPR. It remains irremovable clutters on a sub-surface image output from GPR by first problem. Geography adaptive scanning is useful to image objects beneath rough ground surface. Second problem makes larger the nonlinearity of the relationship between the time for propagation and the depth of a buried object, imaging the small objects such as an antipersonnel landmine closer to the antennas. In this paper, we modify Kirchhoff migration so as to account for not only the variation of position of the sensor head, but also the antennas alignment of the vector radar. The validity of this method is discussed through application to the signals acquired in experiments