Exploration and Mapping with Groups of Robots: Recent Trends

Abstract

Multi-robot exploration—i.e., the problem of mapping unknown features of an environment—is fundamental in many tasks, including search and rescue, planetary exploration, and environmental monitoring. This article surveys recent literature with the aim at identifying current research trends, open challenges, and future directions. Since the first formalization of the exploration problem, current research has extended systems and algorithms to map 3D environments and more complex phenomena and considered real-world constraints. Deep learning-based approaches have seen some preliminary applications in decision-making. While current research has been progressing towards systems that can work in the real world, long-term exploration in large unstructured environments with complex phenomena to map is still an open problem. This introduces opportunities for exciting research, including but not limited to generalized frameworks that bridge the gap between theory and practice, learning-based approaches, and robustness to failure and attacks, so that robots can be deployed safely in real-world environments.