典型文献
Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles
文献摘要：
With the continuous development of robotics and artificial intelligence,robots are being increasingly used in various applications.For traditional navigation algorithms,such as Dijkstra and A*,many dynamic scenarios in life are difficult to cope with.To solve the navigation problem of complex dynamic scenes,we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present.The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output.The algorithm enhances the robots'ability to actively avoid obstacles while retaining the adaptability of traditional methods.
文献关键词：
中图分类号：
作者姓名：
Kaidong Zhao;Li Ning
作者机构：
Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China,and also with University of Chinese Academy of Sciences,Beijing 100049,China
文献出处：
引用格式：
[1]Kaidong Zhao;Li Ning-.Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles)[J].清华大学学报自然科学版（英文版）,2022(06):894-901
A类：
Obstacles
B类：
Hybrid,Navigation,Method,Multiple,Robots,Facing,Dynamic,With,continuous,development,robotics,artificial,intelligence,robots,are,being,increasingly,used,various,applications,For,traditional,navigation,algorithms,such,Dijkstra,many,dynamic,scenarios,life,difficult,cope,To,solve,problem,complex,scenes,present,improved,reinforcement,learning,local,path,planning,that,allows,perform,well,even,when,more,obstacles,applies,gmapping,upper,layer,input,uses,methods,output,enhances,actively,avoid,while,retaining,adaptability
AB值：
0.665514
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
