Toward Intelligent Workplace: Prediction-Enabled Proactive Planning for Human-Robot Coexistence on Unstructured Construction Sites

Abstract

Construction robot path planning is critical for safe and effective human-robot collaboration in future intelligent workplaces. While many studies developed methods to generate paths for construction robots, very few, if any, have integrated the worker trajectory prediction on the jobsite. The objective of this research is to find a safe and efficient robot path, meanwhile, taking into account the predicted movement of construction workers. To this end, we propose a context-aware Long Short-Term Memory (LSTM)-based method to predict worker's trajectory. Based on the predicted trajectory, the A* and Dynamic Window Approach (DWA) are used to find an optimal path for the robot. The efficiency and effectiveness of the proposed method are manifested by simulated and field experiments. The proposed method will contribute to the body of knowledge for prediction-based construction robots path planning and provide the potential to be integrated into existing robot platforms to enhance their performance.