Generating Function Reallocation to Handle Contingencies in Human–Robot Teaming Missions: The Cases in Lunar Surface Transportation

Abstract

On lunar missions, efficient and safe transportation of human–robot systems is essential for the success of human exploration and scientific endeavors. Given the fact that transportation constructs bottlenecks for numerous typical lunar missions, it is appealing to investigate what function allocation strategies can generate optimal task implementation paths for robots with low-human workloads when the situation changes. Thus, this paper presents a novel approach to dynamic human–robot function allocation explicitly designed for team transportation in lunar missions. The proposed dynamic allocation framework aims to optimize human–robot collaboration by responding to existing and potential contingencies. First, a fitness concept model is designed to quantify the factors that motivate the functional adaptation of each agent in dynamic lunar mission scenarios. A hierarchical reinforcement learning (HRL) algorithm with two layers is then employed for decision-making and optimization of human–robot function allocation. Finally, the validity of the framework and algorithm proposed is validated by a series of human–robot function allocation experiments on a simulated environment that mimics lunar transportation scenarios, and is compared with the performance of other algorithms. In the future, path-planning algorithms can be incorporated into the proposed framework to improve the adaptability and efficiency of the human–robot function allocation in lunar missions.

Keywords

• fitness
• human–robot function allocation
• hierarchical reinforcement learning
• unstructured environment
• lunar mission