# Dynamic-Aware Autonomous Exploration in Populated Environments

arXiv: https://arxiv.org/abs/2104.02696 · HTML: https://mlsanity.com/paper/2104.02696/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles](https://mlsanity.com/paper/2310.17977/) (2023)

## Abstract (arXiv)

Autonomous exploration allows mobile robots to navigate in initially unknown territories in order to build complete representations of the environments. In many real-life applications, environments often contain dynamic obstacles which can compromise the exploration process by temporarily blocking passages, narrow paths, exits or entrances to other areas yet to be explored. In this work, we formulate a novel exploration strategy capable of explicitly handling dynamic obstacles, thus leading to complete and reliable exploration outcomes in populated environments. We introduce the concept of dynamic frontiers to represent unknown regions at the boundaries with dynamic obstacles together with a cost function which allows the robot to make informed decisions about when to revisit such frontiers. We evaluate the proposed strategy in challenging simulated environments and show that it outperforms a state-of-the-art baseline in these populated scenarios.