Meta Reinforcement Learning Based Sensor Scanning in 3D Uncertain Environments for Heterogeneous Multi-Robot Systems
This addresses the problem of efficient and rapid sensor scanning in complex, uncertain environments like rescue operations, though it is incremental as it builds on existing meta-learning and multi-robot techniques.
The paper tackles sensor scanning in 3D uncertain environments using heterogeneous multi-robot systems, presenting a meta-learning approach that improves success rates by 15%-27% and adaptation speeds by 70%-75% compared to other methods.
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.