# Regulated Pure Pursuit for Robot Path Tracking

arXiv: https://arxiv.org/abs/2305.20026 · HTML: https://mlsanity.com/paper/2305.20026/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2023.

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

- [Image2Sim: Scaling Embodied Navigation via Generative Neural Simulator](https://mlsanity.com/paper/2607.05765/) (2026)
- [Racing With ROS 2 A Navigation System for an Autonomous Formula Student Race Car](https://mlsanity.com/paper/2311.14276/) (2023)
- [ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments](https://mlsanity.com/paper/2502.19892/) (2025)
- [Towards Safe Path Tracking Using the Simplex Architecture](https://mlsanity.com/paper/2503.10559/) (2025)
- [Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction](https://mlsanity.com/paper/2505.00237/) (2025)
- [Experimental investigation of pose informed reinforcement learning for skid-steered visual navigation](https://mlsanity.com/paper/2506.21732/) (2025)
- [Fauna Sprout: A lightweight, approachable, developer-ready humanoid robot](https://mlsanity.com/paper/2601.18963/) (2026)
- [DarkDriving: A Real-World Day and Night Aligned Dataset for Autonomous Driving in the Dark Environment](https://mlsanity.com/paper/2603.18067/) (2026)

## Abstract (arXiv)

The accelerated deployment of service robots have spawned a number of algorithm variations to better handle real-world conditions. Many local trajectory planning techniques have been deployed on practical robot systems successfully. While most formulations of Dynamic Window Approach and Model Predictive Control can progress along paths and optimize for additional criteria, the use of pure path tracking algorithms is still commonplace. Decades later, Pure Pursuit and its variants continues to be one of the most commonly utilized classes of local trajectory planners. However, few Pure Pursuit variants have been proposed with schema for variable linear velocities - they either assume a constant velocity or fails to address the point at all. This paper presents a variant of Pure Pursuit designed with additional heuristics to regulate linear velocities, built atop the existing Adaptive variant. The Regulated Pure Pursuit algorithm makes incremental improvements on state of the art by adjusting linear velocities with particular focus on safety in constrained and partially observable spaces commonly negotiated by deployed robots. We present experiments with the Regulated Pure Pursuit algorithm on industrial-grade service robots. We also provide a high-quality reference implementation that is freely included ROS 2 Nav2 framework at https://github.com/ros-planning/navigation2 for fast evaluation.