Extreme environments are unstructured and change rapidly,making human exploration in unfamiliar areas difficult.Construction robotics can help reduce risks to human safety and property in these environments by integra...Extreme environments are unstructured and change rapidly,making human exploration in unfamiliar areas difficult.Construction robotics can help reduce risks to human safety and property in these environments by integrating digital technology and artificial intelligence.This technology has the potential to significantly improve the quality and efficiency of construction,making it a key area for future research.Extreme environments include hazardous work sites,polluted areas,and harsh natural conditions.Our review of construction robotics in these settings highlights several knowledge gaps.We focused on four main areas:mechanism design,perception,planning,and control.Our analysis reveals challenges in practical applications,such as creating adaptable mechanisms,accurately perceiving changing environments,planning for unstructured sites,and optimizing control models.Future research should explore:biomimetic designs inspired by nature,multimodal data fusion for perception,adaptive planning strategies,and hybrid control models that combine data-driven and mechanism-based approaches.展开更多
Large-scale machinery operated in a coordinat-ed manner in earthworks for mining constitutes high safety risks.Efficient scheduling of such machinery,factoring in safety constraints,could save time and significantly i...Large-scale machinery operated in a coordinat-ed manner in earthworks for mining constitutes high safety risks.Efficient scheduling of such machinery,factoring in safety constraints,could save time and significantly improve the overall safety.This paper develops a model of automated equipment scheduling in mining earthworks and presents a scheduling algorithm based on deep rein-forcement learning with spatio-temporal safety constraints.The algorithm not only performed well on safety parame-ters,but also outperformed randomized instances of various sizes set against real mining applications.Further,the study reveals that responsiveness to spatio-temporal safety constraints noticeably increases as the scheduling size increases.This method provides important noticeable improvements to safe automated scheduling in mining.展开更多
基金supported in the Strategic Research and Consulting Project of the Chinese Academy of Engineering(2023-XZ-90 and 2023-JB-09-10)the National Key Research and Development Program of China(2021YFF0500301 and 2023YFB3711300)+1 种基金the National Natural Science Foundation of China(72171092 and 71821001)the Natural Science Fund for Distinguished Young Scholars of Hubei Province(2021CFA091).
文摘Extreme environments are unstructured and change rapidly,making human exploration in unfamiliar areas difficult.Construction robotics can help reduce risks to human safety and property in these environments by integrating digital technology and artificial intelligence.This technology has the potential to significantly improve the quality and efficiency of construction,making it a key area for future research.Extreme environments include hazardous work sites,polluted areas,and harsh natural conditions.Our review of construction robotics in these settings highlights several knowledge gaps.We focused on four main areas:mechanism design,perception,planning,and control.Our analysis reveals challenges in practical applications,such as creating adaptable mechanisms,accurately perceiving changing environments,planning for unstructured sites,and optimizing control models.Future research should explore:biomimetic designs inspired by nature,multimodal data fusion for perception,adaptive planning strategies,and hybrid control models that combine data-driven and mechanism-based approaches.
基金National Natural Science Foundation of China(Grant Nos.72171092,52192664 and 71821001)Natural Science Fund for Distinguished Young Scholars of Hubei Province,China(Grant No.2021CFA091).
文摘Large-scale machinery operated in a coordinat-ed manner in earthworks for mining constitutes high safety risks.Efficient scheduling of such machinery,factoring in safety constraints,could save time and significantly improve the overall safety.This paper develops a model of automated equipment scheduling in mining earthworks and presents a scheduling algorithm based on deep rein-forcement learning with spatio-temporal safety constraints.The algorithm not only performed well on safety parame-ters,but also outperformed randomized instances of various sizes set against real mining applications.Further,the study reveals that responsiveness to spatio-temporal safety constraints noticeably increases as the scheduling size increases.This method provides important noticeable improvements to safe automated scheduling in mining.
