Dynamic-Aware Autonomous Exploration in Populated Environments
This addresses the challenge of reliable exploration for mobile robots in real-life populated settings where dynamic obstacles can hinder navigation.
The paper tackles the problem of autonomous robot exploration in environments with dynamic obstacles by introducing a novel strategy that uses dynamic frontiers and a cost function to decide when to revisit blocked areas, showing it outperforms a state-of-the-art baseline in simulated populated scenarios.
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.