# Long-Range Indoor Navigation with PRM-RL

## TL;DR

PRM-RL combines reinforcement learning and probabilistic roadmaps to enable long-range, noise-robust indoor robot navigation across complex environments, demonstrated both in simulation and real-world settings.

## Contribution

This work introduces PRM-RL, a hierarchical navigation approach that integrates RL-based local control with sampling-based planning for reliable long-range indoor navigation.

## Key findings

- PRM-RL outperforms baseline methods in success rate.
- The approach is robust to sensor and control noise.
- Robots successfully navigated over 5.8 km in real environments.

## Abstract

Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this with PRM-RL, a hierarchical robot navigation method in which reinforcement learning agents that map noisy sensors to robot controls learn to solve short-range obstacle avoidance tasks, and then sampling-based planners map where these agents can reliably navigate in simulation; these roadmaps and agents are then deployed on robots, guiding them along the shortest path where the agents are likely to succeed. Here we use Probabilistic Roadmaps (PRMs) as the sampling-based planner, and AutoRL as the reinforcement learning method in the indoor navigation context. We evaluate the method in simulation for kinematic differential drive and kinodynamic car-like robots in several environments, and on differential-drive robots at three physical sites. Our results show PRM-RL with AutoRL is more successful than several baselines, is robust to noise, and can guide robots over hundreds of meters in the face of noise and obstacles in both simulation and on robots, including over 5.8 kilometers of physical robot navigation. Video: https://youtu.be/xN-OWX5gKvQ