Obstacle-dependent Mixed Gaussian Potential Field (ODMG-PF)
The main idea behind this method is that, after receiving distance data from the LiDAR sensor, we consider only the objects that are within the threshold range, enlarge the obstacles with regard to the robot’s width, and construct a Gaussian (repulsive) potential field from them. Next, we calculate the mixed gaussian attractive field from the yaw angle information from an inertial measurement unit (IMU). The total field is made of these two fields, and, from it, we choose the angle with the minimum total field value.
The experiment was carried out in simulation (V-REP) environment first and then tested in the real-world environment. In order to communicate with the robot, I used Robot Operating System (ROS) melodic in Ubuntu 18.04 LTS OS. For mobile robot, I customized a turtlebot from Robotiz by replacing the normal wheels to Mecanum-wheels. To obtain the ground truth data as possible, I placed the robot and the obstacles at a fixed initial positions and calculated the change of it's positions by analyzing the videos recorded.
I evaluated the obstacle-avoidance capability with mobile robots and found my algorithm, ODMG-PF, successfully decreased the collision rate by 17.2% and 14.3% in the simulation and real-world, respectively, compared to the baseline.
 Cho, J. H., Pae, D. S., Lim, M. T., & Kang, T. K. (2018). A real-time obstacle avoidance method for autonomous vehicles using an obstacle-dependent Gaussian potential field. Journal of Advanced Transportation, 2018.