Toward Intelligent Workplace: Prediction-Enabled Proactive Planning for Human-Robot Coexistence on Unstructured Construction Sites
Toward Intelligent Workplace: Prediction-Enabled Proactive Planning for Human-Robot Coexistence on Unstructured Construction Sites
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DOI：
10.1109/wsc48552.2020.9384077
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Da Hu;Shuai Li;Jiannan Cai;Yuqing Hu
中科院分区：
文献类型：
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作者：
Da Hu;Shuai Li;Jiannan Cai;Yuqing Hu
建筑机器人路径规划是未来智能工作场所实现人-机器人安全有效协作的关键。虽然许多研究开发出了为建筑机器人生成路径的方法，但很少有研究将工人在工地上的轨迹预测整合在一起。本研究的目的是寻找一条安全、高效的机器人路径，同时考虑到建筑工人的预测运动。为此，我们提出了一种基于上下文感知的长短期记忆(LSTM)的工人轨迹预测方法。基于预测轨迹，采用A*算法和动态窗口算法(DWA)为机器人寻找最优路径。仿真和现场实验验证了该方法的有效性和有效性。所提出的方法将为基于预测的建筑机器人路径规划提供知识库，并为集成到现有的机器人平台以提高其性能提供了可能性。
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.