Decentralized Exploration of a Structured Environment Based on Multi-agent Deep Reinforcement Learning

摘要

Multi-robot environment exploration is one of the widely discussed topics in the field of robotics. It is the foundation for many real-world robotic applications. Many decentralized methods (that is, without a centralized controller) have been proposed in the past decades. Most of them focus on improving collaboration efficiency by utilizing low-level heuristic information, such as distances to obstacles and robot positions. In contrast, although a human being can make decisions on a similar task, he/she exploits high-level knowledge, such as the building's common structure pattern. This paper proposes a novel distributed multi-robot exploration algorithm based on deep reinforcement learning (DME-DRL) for structured environments that enables robots to make decisions on the basis of this high-level knowledge. DME-DRL is a distributed algorithm that uses deep neural networks to extract the structural pattern of the environment, and it can work in scenarios with or without communication. The experimental results showed that this approach can decrease the travel distance by approximately 10.84% on average, compared with those of traditional heuristic methods and can significantly reduce the communication cost in the exploration process.