Towards Better Human Robot Collaboration with Robust Plan Recognition and Trajectory Prediction

TL;DR

This paper presents an integrated framework for human-robot collaboration that combines plan recognition and trajectory prediction to enhance safety, robustness, and efficiency in dynamic manufacturing environments.

Contribution

It introduces a hierarchical approach to improve plan recognition robustness and integrates trajectory prediction for safer, more efficient human-robot collaboration.

Findings

Framework ensures safe HRC with collision avoidance.

Improves time efficiency of HRC teams.

Plan recognition is robust to noise.

Abstract

Human robot collaboration (HRC) is becoming increasingly important as the paradigm of manufacturing is shifting from mass production to mass customization. The introduction of HRC can significantly improve the flexibility and intelligence of automation. However, due to the stochastic and time-varying nature of human collaborators, it is challenging for the robot to efficiently and accurately identify the plan of human and respond in a safe manner. To address this challenge, we propose an integrated human robot collaboration framework in this paper which includes both plan recognition and trajectory prediction. Such a framework enables the robots to perceive, predict and adapt their actions to the human's plan and intelligently avoid collisions with the human based on the predicted human trajectory. Moreover, by explicitly leveraging the hierarchical relationship between the plan and trajectories, more robust plan recognition performance can be achieved. Experiments are conducted on an industrial robot to verify the proposed framework, which shows that our proposed framework can not only assure safe HRC, but also improve the time efficiency of the HRC team, and the plan recognition module is not sensitive to noises.