An Investigation of Bug Algorithms for Mobile Robot Navigation and Obstacle Avoidance in Two-Dimensional Unknown Static Environments

A significant problem in mobile robot navigation is obstacle avoidance in an unknown environment. Though there are efficient global path planning algorithms for known environments, they are not feasible in layouts where the robot does not have a comprehensive knowledge about the obstacle locations. Bug Algorithms are navigation and obstacle avoidance algorithms that can be implemented in a 2D environment with previously unknown static obstacles. The Bug family of algorithms includes some of the most fundamental navigation and obstacle avoidance algorithms which are still used at a higher level in any mobile robot navigation problem. In this paper, three algorithms belonging to the Bug algorithm family have been implemented with Python3 software and the results have been compared against each other with cost of the path and the computational time as criteria. Finally, the probability density functions were plotted and the relative performances of three Bug algorithms which are Bug0, Bug1 and Bug2, were compared using simulations with varying parameters and layouts.