# FAR Planner: Fast, Attemptable Route Planner using Dynamic Visibility Update

arXiv: https://arxiv.org/abs/2110.09460 · HTML: https://mlsanity.com/paper/2110.09460/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2021.

## Built on by (most cited first, 11 of 11)

- [Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2506.05997/) (2025)
- [HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks](https://mlsanity.com/paper/2408.03768/) (2024)
- [VL-Nav: Neuro-Symbolic Reasoning-based Vision-Language Navigation](https://mlsanity.com/paper/2502.00931/) (2025)
- [Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments](https://mlsanity.com/paper/2412.00291/) (2024)
- [CTSAC: Curriculum-Based Transformer Soft Actor-Critic for Goal-Oriented Robot Exploration](https://mlsanity.com/paper/2503.14254/) (2025)
- [Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection](https://mlsanity.com/paper/2509.17877/) (2025)
- [DreamFlow: Local Navigation Beyond Observation via Conditional Flow Matching in the Latent Space](https://mlsanity.com/paper/2603.02976/) (2026)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [DEUX: Active Exploration for Learning Unsupervised Depth Perception](https://mlsanity.com/paper/2310.06164/) (2023)
- [An Earth Rover dataset recorded at the ICRA@40 party](https://mlsanity.com/paper/2407.05735/) (2024)
- [Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired](https://mlsanity.com/paper/2506.14857/) (2025)

## Abstract (arXiv)

The problem of path planning in unknown environments remains a challenging problem - as the environment is gradually observed during the navigation, the underlying planner has to update the environment representation and replan, promptly and constantly, to account for the new observations. In this paper, we present a visibility graph-based planning framework capable of dealing with navigation tasks in both known and unknown environments. The planner employs a polygonal representation of the environment and constructs the representation by extracting edge points around obstacles to form enclosed polygons. With that, the method dynamically updates a global visibility graph using a two-layered data structure, expanding the visibility edges along with the navigation and removing edges that become occluded by newly observed obstacles. When navigating in unknown environments, the method is attemptable in discovering a way to the goal by picking up the environment layout on the fly, updating the visibility graph, and fast re-planning corresponding to the newly observed environment. We evaluate the method in simulated and real-world settings. The method shows the capability to attempt and navigate through unknown environments, reducing the travel time by up to 12-47% from search-based methods: A*, D* Lite, and more than 24-35% than sampling-based methods: RRT*, BIT*, and SPARS.