# A Comparative Study of Bug Algorithms for Robot Navigation

arXiv: https://arxiv.org/abs/1808.05050 · HTML: https://mlsanity.com/paper/1808.05050/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 8 of 8)

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- [Perturbation-mitigated USV Navigation with Distributionally Robust Reinforcement Learning](https://mlsanity.com/paper/2512.00030/) (2025)
- [Collision Replay: What Does Bumping Into Things Tell You About Scene Geometry?](https://mlsanity.com/paper/2105.01061/) (2021)
- [Tactile Aware Dynamic Obstacle Avoidance in Crowded Environment with Deep Reinforcement Learning](https://mlsanity.com/paper/2406.13434/) (2024)
- [Insect-inspired Visual Point-goal Navigation](https://mlsanity.com/paper/2601.16806/) (2026)

## Abstract (arXiv)

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.