Dynamic-Aware Autonomous Exploration in Populated Environments

TL;DR

This paper presents a novel exploration strategy for autonomous robots that explicitly handles dynamic obstacles, enabling reliable exploration in populated environments by using dynamic frontiers and an informed decision-making process.

Contribution

The work introduces dynamic frontiers and a cost function to improve exploration in environments with moving obstacles, outperforming existing methods in simulated scenarios.

Findings

Outperforms baseline methods in dynamic environments

Effectively handles dynamic obstacles during exploration

Enables more complete environment mapping in populated areas

Abstract

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.