The probabilistic seismic hazard analysis (PSHA) method used in existing seismic ground motion parameters zonation map of China (the traditional PSHA-CN method) is based on a two-dimensional area seismic source framew...The probabilistic seismic hazard analysis (PSHA) method used in existing seismic ground motion parameters zonation map of China (the traditional PSHA-CN method) is based on a two-dimensional area seismic source framework and does not account for the rupture dimension of large earthquakes,which may lead to underestimation of seismic hazard at near-fault sites.By employing stochastic sampling to integrate three-dimensional fault sources and two-dimensional area seismic sources,a new PSHA-CN method was developed in recent years,but it faces limitations in accuracy and computational ef ciency due to sampling constraints,particularly for low probability of exceedance scenarios or large earthquakes with long return periods.To enhance the computational ef ciency of the new PSHA-CN method,this study developed a novel spatial integration algorithm for PSHA.The algorithm considers rupture dimension,enables ef cient fault geometry modeling using the Frankel Fault Surface (FFS) and Stirling Fault Surface (SFS) models,and maintains compatibility with the traditional PSHA-CN framework.Validation against test cases from the Pacific Earthquake Engineering Research Center (PEER) demonstrated the algorithm’s reliability.Furthermore,the algorithm was applied to assess seismic hazard in the Changsha-Zhuzhou-Xiangtan metropolitan region in Hunan Province to validate its performance in regions with moderate seismic activity in China.A comparative analysis of the new algorithm results with those of the traditional PSHA-CN method revealed that the PSHA-CN method underestimates near-fault seismic hazards.The proposed algorithm will be implemented in next-generation seismic ground motion parameters zonation map in China.展开更多
Obstacle avoidance is quite an important issue in the field of legged robotic applications, such as rescuing and detecting in complicated environment. Most related researchers focused on the legged robot’s gait gener...Obstacle avoidance is quite an important issue in the field of legged robotic applications, such as rescuing and detecting in complicated environment. Most related researchers focused on the legged robot’s gait generation after ssuming that obstacles have been detected and the walking path has been given. In this paper we propose and validate a novel obstacle avoidance framework for a six-legged walking robot Hexapod-III in unknown environment. Throughout the paper we highlight three themes: (1) The terrain map modeling and the obstacle detection; (2) the obstacle avoidance path planning method; (3) motion planning for the legged robot. Concretely, a novel geometric feature grid map (GFGM) is proposed to describe the terrain. Based on the GFGM, the obstacle detection algorithm is presented. Then the concepts of virtual obstacles and safe conversion pose are introduced. Virtual obstacles restrict the robot to walk on the detection terrain. A safe path based on Bezier curves, passing through safe conversion poses, is obtained by minimizing a penalty function taking into account the path length subjected to obstacle avoidance. Thirdly, motion planning for the legged robot to walk along the generated path is discussed in detail. At last, we apply the proposed framework to the Hexapod-III robot. The experimental result shows that our methodology allows the robot to walk safely without encountering with any obstacles in unknown environment.展开更多
基金Funding for this research was provided by the National Key R&D Program of China(Grant No.2022YFC3003505)This research was also funded by the National Natural Science Foundation of China(Grant No.41974065)the Special Fund of the Institute of Geophysics,China Earthquake Administration(Grant No.DQJB23Y32).
文摘The probabilistic seismic hazard analysis (PSHA) method used in existing seismic ground motion parameters zonation map of China (the traditional PSHA-CN method) is based on a two-dimensional area seismic source framework and does not account for the rupture dimension of large earthquakes,which may lead to underestimation of seismic hazard at near-fault sites.By employing stochastic sampling to integrate three-dimensional fault sources and two-dimensional area seismic sources,a new PSHA-CN method was developed in recent years,but it faces limitations in accuracy and computational ef ciency due to sampling constraints,particularly for low probability of exceedance scenarios or large earthquakes with long return periods.To enhance the computational ef ciency of the new PSHA-CN method,this study developed a novel spatial integration algorithm for PSHA.The algorithm considers rupture dimension,enables ef cient fault geometry modeling using the Frankel Fault Surface (FFS) and Stirling Fault Surface (SFS) models,and maintains compatibility with the traditional PSHA-CN framework.Validation against test cases from the Pacific Earthquake Engineering Research Center (PEER) demonstrated the algorithm’s reliability.Furthermore,the algorithm was applied to assess seismic hazard in the Changsha-Zhuzhou-Xiangtan metropolitan region in Hunan Province to validate its performance in regions with moderate seismic activity in China.A comparative analysis of the new algorithm results with those of the traditional PSHA-CN method revealed that the PSHA-CN method underestimates near-fault seismic hazards.The proposed algorithm will be implemented in next-generation seismic ground motion parameters zonation map in China.
基金supported by the National Basic Research Program of China (Grant No. 2013CB035501)
文摘Obstacle avoidance is quite an important issue in the field of legged robotic applications, such as rescuing and detecting in complicated environment. Most related researchers focused on the legged robot’s gait generation after ssuming that obstacles have been detected and the walking path has been given. In this paper we propose and validate a novel obstacle avoidance framework for a six-legged walking robot Hexapod-III in unknown environment. Throughout the paper we highlight three themes: (1) The terrain map modeling and the obstacle detection; (2) the obstacle avoidance path planning method; (3) motion planning for the legged robot. Concretely, a novel geometric feature grid map (GFGM) is proposed to describe the terrain. Based on the GFGM, the obstacle detection algorithm is presented. Then the concepts of virtual obstacles and safe conversion pose are introduced. Virtual obstacles restrict the robot to walk on the detection terrain. A safe path based on Bezier curves, passing through safe conversion poses, is obtained by minimizing a penalty function taking into account the path length subjected to obstacle avoidance. Thirdly, motion planning for the legged robot to walk along the generated path is discussed in detail. At last, we apply the proposed framework to the Hexapod-III robot. The experimental result shows that our methodology allows the robot to walk safely without encountering with any obstacles in unknown environment.
