A Comparative Study of Bug Algorithms for Robot Navigation

TL;DR

This paper reviews and compares Bug Algorithms for robot navigation, highlighting their sensitivity to sensor noise and the importance of multi-sensor approaches for real-world robustness.

Contribution

It provides a comprehensive comparison of Bug Algorithms, revealing their limitations under sensor noise and proposing multi-sensor strategies for improved real-world applicability.

Findings

Sensor noise significantly affects Bug Algorithm performance.

Multi-sensor approaches improve robustness against sensor failures.

Bug Algorithms may require adaptation for real-world deployment.

Abstract

This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these methods seem to provide an efficient navigation paradigm, ideal for implementations on tiny robots with limited resources. Closer inspection, however, shows that many of these Bug Algorithms assume perfect global position estimate of the robot which in GPS-denied environments implies considerable expenses of computation and memory -- relying on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry (VO) methods. We compare a selection of Bug Algorithms in a simulated robot and environment where they endure different types noise and failure-cases of their on-board sensors. From the simulation results, we conclude that the implemented Bug Algorithms' performances are sensitive to many types of sensor-noise, which was most noticeable for odometry-drift. This raises the question if Bug Algorithms are suitable for real-world, on-board, robotic navigation as is. Variations that use multiple sensors to keep track of their progress towards the goal, were more adept in completing their task in the presence of sensor-failures. This shows that Bug Algorithms must spread their risk, by relying on the readings of multiple sensors, to be suitable for real-world deployment.