Adaptive Task Scheduling for an Assembly Task Coworker Robot Based on Incremental Learning of Human's Motion Patterns

Abstract

Future robots are expected to share the same workspace with humans and work in cooperation with them to improve productivity and maintain the quality of products. Considering this situation, we have developed a novel assembly task co-worker robot to support workers in their task by delivering the parts and tools to workers. Although such systems have improved work efficiency by predicting human's motion patterns, it is necessary to collect worker's data in advance and regenerate its model whenever the worker is changed. In this letter, we extend the previous system by installing an online learning algorithm and create a worker-dependent model without collecting data in advance. Trajectory prediction with high precision can be realized because of the worker-dependent model and effective utilization of the regularity of the worker's behavior. An adaptive task scheduling system based on the predicted result of the worker's behavior is proposed for improving work efficiency. Implementing the proposed algorithm, we evaluate the effectiveness of the task scheduling system by experiment.