Prediction of Human Activity Patterns for Human-Robot Collaborative Assembly Tasks

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 7; pp. 3934 - 3942
• Main Authors: Zanchettin, Andrea Maria, Casalino, Andrea, Piroddi, Luigi, Rocco, Paolo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Assembly; Cognitive human–robot interaction; Collaboration; Hidden Markov models; Informatics; intelligent and flexible manufacturing; Markov chains; Markov processes; planning; Predictive models; Robot control; Robots; scheduling and coordination; Task analysis
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2018.2882741

It is widely agreed that future manufacturing environments will be populated by humans and robots sharing the same workspace. However, the real collaboration can be sporadic, especially in the case of assembly tasks, which might involve autonomous operations to be executed by either the robot or the human worker. In this scenario, it might be beneficial to predict the actions of the human in order to control the robot both safely and efficiently. In this paper, we propose a method to predict human activity patterns in order to early infer when a specific collaborative operation will be requested by the human and to allow the robot to perform alternative autonomous tasks in the meanwhile. The prediction algorithm is based on higher-order Markov chains and is experimentally verified in a realistic scenario involving a dual-arm robot employed in a small part collaborative assembly task.