Towards Shared Autonomy Framework for Human-Aware Motion Planning in Industrial Human-Robot Collaboration

Abstract

Industrial human-robot collaboration (HRC) is a promising production mode that enables humans and robots complete a joint set of tasks in a shared workplace. In this context, to facilitate efficient and safe collaboration, an industrial robot needs to understand its human teammate's behavior and develop human-aware motion planning. However, the systematic theoretical explanation on this subject is limited. Shared autonomy allows the human intervention in the control loop of the autonomous robot to achieve human-robot common goals. In this paper, the framework of shared autonomy into industrial HRC context is presented. In the sight of shared autonomy, considering the intention of human behavior is partially observable, we formalize the human-aware motion planning as a Partially Observable Markov Decision Process (POMDP), where the robot addresses the sequential decision making problems under the uncertainty of human's intention. Moreover, the shared autonomy framework and its detailed systematic enabling approaches for human-aware motion planning is presented. The feasibility of the presented framework and approaches is also validated by the case study of a HRC assembly scenario, which could accomplish more fluent and safe collaboration.