Implementasi Pengendalian Robot Mobil Pencari Target Dan Penghindar Rintangan

Robots are useful to help humans in performing jobs that require high precision, substantial labor, repetitive and dirty work, and high-risk or dangerous jobs. Those are the high-risk human jobs that a robot can do. Wheeled robots have the ability to go to the targeted position. Proportional control is used to control the movement of robots. In addition, the robot will also be equipped with PI control method to adjust the actual wheel speed of the robot. The block diagram of the obstacle-driven avoider robot consists of push button, rotary encoder, ultrasonic sensor, Atmega, IC L298D, DC Motor and Light. The results of the obstacle-driven avoider robot, wheeled robots have the ability to run in accordance with the desired black line. Proportional control is used to control the movement of robots. In addition, the robot will also be equipped with ultrasonic sensors to set the robot in avoiding obstacles. Based on the results of testing and analysis that have done, it is suggested that there is tool that can be provided to develop a more sophisticated technology like adding sensors or more features.

A Stereo Visual-Inertial Odometry Using Structural Lines for Localizing Indoor Wheeled Robots

This paper proposes an optimization-based stereo visual-inertial odometry (VIO) to locate indoor wheeled robots. Multiple Manhattan worlds assumption is adopted to model the interior environment. Instead of treating these worlds as isolated ones, we fuse the latest Manhattan world with the previous ones if they are in the same direction, reducing the calculated errors on the orientation of the latest Manhattan world. Then the structural lines which encode the orientation information of these worlds are taken as additional landmarks to improve positioning accuracy and reduce accumulated drift of the system, especially when the system is in a challenging environment (i.e., scenes with continuous turning and low textures). Besides, the structural lines are parameterized by only two variables, which improves the computational efficiency and simplifies the initialization of lines. Experiments on public benchmark datasets and in real-world environments demonstrate that the proposed VIO system can accurately position the wheeled robot in a complex indoor environment.

Continuous reinforcement learning based ramp jump control for single-track two-wheeled robots

Single-track two-wheeled robots have become an important research topic in recent years, owing to their simple structure, energy savings and ability to run on narrow roads. However, the ramp jump remains a challenging task. In this study, we propose to realize a single-track two-wheeled robot ramp jump. We present a control method that employs continuous action reinforcement learning techniques for single-track two-wheeled robot control. We design a novel reward function for reinforcement learning, optimize the dimensions of the action space, and enable training under the deep deterministic policy gradient algorithm. Finally, we validate the control method through simulation experiments and successfully realize the single-track two-wheeled robot ramp jump task. Simulation results validate that the control method is effective and has several advantages over high-dimension action space control, reinforcement learning control of sparse reward function and discrete action reinforcement learning control.