Implementation of Rev1 and Rev2 Bug Family Algorithms in ROS Noetic

Abstract

Modern map-dependent algorithms for mobile robot navigation typically overload a CPU and memory with a gradually increasing amount of environmental data. In contrast, Bug family local path planning algorithms operate without mapping and have significantly lower hardware requirements. Bug algorithms use real-time measurements from visual and touch sensors to make immediate decisions on direction of the robot’s motion. This paper presents an implementation of Rev1 and Rev2 Bug algorithms for the Robot Operating System (ROS) Noetic framework with a virtual model of the TurtleBot3 Burger robot. Given algorithms are validated in Gazebo simulation using various convex and maze environments. The results demonstrate superiority of Rev1 method over Rev2 in trajectory length in convex maps. However, Rev2 finds shorter paths in maze type environments.