Meta Reinforcement Learning Based Sensor Scanning in 3D Uncertain Environments for Heterogeneous Multi-Robot Systems

TL;DR

This paper introduces a meta-learning approach for sensor scanning in complex 3D uncertain environments using heterogeneous multi-robot systems, significantly enhancing exploration and adaptation speed.

Contribution

It presents a novel meta-learning method tailored for multi-robot sensor scanning in 3D uncertain environments, improving efficiency and adaptability.

Findings

Outperforms other methods by 15%-27% on success rate

Achieves 70%-75% faster adaptation speed

Effective in complex rescue scenarios

Abstract

We study a novel problem that tackles learning based sensor scanning in 3D and uncertain environments with heterogeneous multi-robot systems. Our motivation is two-fold: first, 3D environments are complex, the use of heterogeneous multi-robot systems intuitively can facilitate sensor scanning by fully taking advantage of sensors with different capabilities. Second, in uncertain environments (e.g. rescue), time is of great significance. Since the learning process normally takes time to train and adapt to a new environment, we need to find an effective way to explore and adapt quickly. To this end, in this paper, we present a meta-learning approach to improve the exploration and adaptation capabilities. The experimental results demonstrate our method can outperform other methods by approximately 15%-27% on success rate and 70%-75% on adaptation speed.