Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape

TL;DR

This paper introduces PLBA, a framework that combines active learning of human preferences with behavior adaptation for multi-robot systems navigating outdoor environments, improving efficiency and safety in uncertain conditions.

Contribution

The paper presents a novel joint preference landscape learning and behavior adjustment framework (PLBA) that integrates real-time human guidance with Gaussian process models for outdoor multi-robot navigation.

Findings

PLBA accurately predicts human preferences with high spatial correlation.

The framework enables rapid and safe behavior adaptation in complex environments.

Experimental validation shows improved task performance and safety in disaster search scenarios.

Abstract

Compared with single robots, Multi-Robot Systems (MRS) can perform missions more efficiently due to the presence of multiple members with diverse capabilities. However, deploying an MRS in wide real-world environments is still challenging due to uncertain and various obstacles (e.g., building clusters and trees). With a limited understanding of environmental uncertainty on performance, an MRS cannot flexibly adjust its behaviors (e.g., teaming, load sharing, trajectory planning) to ensure both environment adaptation and task accomplishments. In this work, a novel joint preference landscape learning and behavior adjusting framework (PLBA) is designed. PLBA efficiently integrates real-time human guidance to MRS coordination and utilizes Sparse Variational Gaussian Processes with Varying Output Noise to quickly assess human preferences by leveraging spatial correlations between environment characteristics. An optimization-based behavior-adjusting method then safely adapts MRS behaviors to environments. To validate PLBA's effectiveness in MRS behavior adaption, a flood disaster search and rescue task was designed. 20 human users provided 1764 feedback based on human preferences obtained from MRS behaviors related to "task quality", "task progress", "robot safety". The prediction accuracy and adaptation speed results show the effectiveness of PLBA in preference learning and MRS behavior adaption.