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
How multi-unmanned aerial vehicles(UAVs)carrying a payload pass an obstacle-dense environment is practically important.Up to now,there have been few results on safe motion planning for the multi-UAVs cooperative trans...How multi-unmanned aerial vehicles(UAVs)carrying a payload pass an obstacle-dense environment is practically important.Up to now,there have been few results on safe motion planning for the multi-UAVs cooperative transportation system(CTS)to pass through such an environment.The prob-lem is challenging because it is difficult to analyze and explicitly take into account the swing motion of the payload in planning.In this paper,a modeling method of virtual tube is proposed by fus-ing the advantages of the existing modeling algorithm for regu-lar virtual tube and the expansion environment method.The pro-posed method can not only generate a safe and smooth tube for UAVs,but also ensure the payload stays away from the dense obstacles.Simulation results show the effectiveness of the method and the safety of the planned tube.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.
基金supported by the National Natural Science Foundation of China(6237338661973327).
文摘How multi-unmanned aerial vehicles(UAVs)carrying a payload pass an obstacle-dense environment is practically important.Up to now,there have been few results on safe motion planning for the multi-UAVs cooperative transportation system(CTS)to pass through such an environment.The prob-lem is challenging because it is difficult to analyze and explicitly take into account the swing motion of the payload in planning.In this paper,a modeling method of virtual tube is proposed by fus-ing the advantages of the existing modeling algorithm for regu-lar virtual tube and the expansion environment method.The pro-posed method can not only generate a safe and smooth tube for UAVs,but also ensure the payload stays away from the dense obstacles.Simulation results show the effectiveness of the method and the safety of the planned tube.
基金supported by the International S&T Cooperation Projects of China(2015DFR10510)the National Natural Science Foundation of China(61440048+1 种基金61562018)the Scientific Research Program of the Higher Education Institutions of Hainan Province(HNKY2014-04)
