Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion pl...Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is challenged.With the development of machine learning,the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature.The DRL-based motion planner is model-free and does not rely on the prior structured map.Most importantly,the DRL-based motion planner achieves the unification of the global planner and the local planner.In this paper,we provide a systematic review of various motion planning methods.Firstly,we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features.Then,we concentrate on summarizing reinforcement learning(RL)-based motion planning approaches,including motion planners combined with RL improvements,map-free RL-based motion planners,and multi-robot cooperative planning methods.Finally,we analyze the urgent challenges faced by these mainstream RLbased motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research.展开更多
遗址形态测量对分析遗址与环境关系、保护古遗址具有重要意义。本文利用RTK GPS对叶赫古城进行形态测量,将碎部测量的离散点进行空间插值,提取古城遗址形态示量参数,并分析遗址空间分布特征。结果表明,叶赫古城略呈椭圆形,面积64782.82 ...遗址形态测量对分析遗址与环境关系、保护古遗址具有重要意义。本文利用RTK GPS对叶赫古城进行形态测量,将碎部测量的离散点进行空间插值,提取古城遗址形态示量参数,并分析遗址空间分布特征。结果表明,叶赫古城略呈椭圆形,面积64782.82 m 2,城垣周长1022.38 m,地势东部略高于西部,最大坡度为55.21°,遗址向阳坡面积约为54825.71 m 2,占古城遗址总面积的84.63%。叶赫古城遗址地理区位优越,三维形态科学,研究以期为叶赫古城遗迹保护和开发提供科学依据。展开更多
基金supported by the National Natural Science Foundation of China (62173251)the“Zhishan”Scholars Programs of Southeast University+1 种基金the Fundamental Research Funds for the Central UniversitiesShanghai Gaofeng&Gaoyuan Project for University Academic Program Development (22120210022)
文摘Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is challenged.With the development of machine learning,the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature.The DRL-based motion planner is model-free and does not rely on the prior structured map.Most importantly,the DRL-based motion planner achieves the unification of the global planner and the local planner.In this paper,we provide a systematic review of various motion planning methods.Firstly,we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features.Then,we concentrate on summarizing reinforcement learning(RL)-based motion planning approaches,including motion planners combined with RL improvements,map-free RL-based motion planners,and multi-robot cooperative planning methods.Finally,we analyze the urgent challenges faced by these mainstream RLbased motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research.
文摘遗址形态测量对分析遗址与环境关系、保护古遗址具有重要意义。本文利用RTK GPS对叶赫古城进行形态测量,将碎部测量的离散点进行空间插值,提取古城遗址形态示量参数,并分析遗址空间分布特征。结果表明,叶赫古城略呈椭圆形,面积64782.82 m 2,城垣周长1022.38 m,地势东部略高于西部,最大坡度为55.21°,遗址向阳坡面积约为54825.71 m 2,占古城遗址总面积的84.63%。叶赫古城遗址地理区位优越,三维形态科学,研究以期为叶赫古城遗迹保护和开发提供科学依据。
