路径规划是移动机器人的重要研究内容。快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法因在机器人路径规划中的成功应用,自提出以来就得到了极大的研究与发展。快速扩展随机树作为一种新颖的随机节点采样算法,相对传统路径规...路径规划是移动机器人的重要研究内容。快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法因在机器人路径规划中的成功应用,自提出以来就得到了极大的研究与发展。快速扩展随机树作为一种新颖的随机节点采样算法,相对传统路径规划算法,具有建模时间短、搜索能力强、方便添加非完整约束等优点。介绍了快速扩展随机树算法的基本原理与性质,并从单向随机树扩展、多向随机树扩展、其他改进等方面概括了算法的研究现状。最后,展望了算法未来的研究方向与挑战。展开更多
Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is graduall...Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.展开更多
文摘路径规划是移动机器人的重要研究内容。快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法因在机器人路径规划中的成功应用,自提出以来就得到了极大的研究与发展。快速扩展随机树作为一种新颖的随机节点采样算法,相对传统路径规划算法,具有建模时间短、搜索能力强、方便添加非完整约束等优点。介绍了快速扩展随机树算法的基本原理与性质,并从单向随机树扩展、多向随机树扩展、其他改进等方面概括了算法的研究现状。最后,展望了算法未来的研究方向与挑战。
基金Project(52161135301)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(202306370296)supported by China Scholarship Council。
文摘Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.
