[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125138-en":3,"doc-seo-125138-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125138,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Efficient autonomous navigation for mobile robots using machine learning","Efficient autonomous navigation for mobile robots is addressed by replacing heavy hand-coded pipelines with a learning-based policy acquisition process. The method trains a robot to imitate an expert navigation strategy using learning from demonstration (LFD), where the expert provides optimal trajectories generated by A*. By learning state-action links and evaluating across simulated environments with varying complexity and obstacle distributions, the approach is tested for flexibility and efficiency, demonstrating reliability and effectiveness.","IAES International Journal of Artificial Intelligence (IJ-AI)  \nVol. 13, No. 3, September 2024, pp. 3061∼3071  \nISSN: 2252-8938, DOI: 10.11591/ijai.v13.i3.pp3061-3071 ❒ 3061  \n\n| Efficient autonomous navigation for mobile robots using\u003Cbr>machine learning\u003Cbr>Abderrahim Waga1 , Ayoub Ba-ichou1 , Said Benhlima1 , Ali Bekri1 , Jawad Abdouni2\u003Cbr>1Department of Computer Science, Faculty of science, Moulay Ismail University, Meknes, Morocco\u003Cbr>2Department of Computer Science, National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Jul 16, 2023 Revised Dec 11, 2023 Accepted Jan 15, 2024\u003Cbr>Keywords:\u003Cbr>Autonomous navigation Ensemble methods Knowledge learning Learning from demonstration Machine learning |  | ABSTRACT\u003Cbr>The ability to navigate autonomously from the start to its final goal is the crucial key to mobile robots. To ensure complete navigation, it is mandatory todo heavy programming since this task is composed of several subtasks such as path planning, localization, and obstacle avoidance. This paper simplifies this heavy process by making the robot more intelligent. The robot will acquire the navigation policy from an expert in navigation using machine learning. We used the expert A*, which is characterized by generating an optimal trajectory. In the context of robotics, learning from demonstration (LFD) will allow robots, in general, to acquire new skills by imitating the behavior of an expert. The expert will navigate in different environments, and our robot will try to learn its navigation strategy by linking states and suitable actions taken. We find that our robot acquires the navigation policy given by A* very well. Several tests were simulated with environments of different complexity and obstacle distributions to evaluate the flexibility and efficiency of the proposed strategies. The experimental results demonstrate the reliability and effectiveness of the proposed method.\u003Cbr>This is an open access article under the CC BY-SA license. |\n| Corresponding Author: |  |  |\n| Abderrahim Waga\u003Cbr>Department of Computer Science, Faculty of science, Moulay Ismail University Avenue Zitoune, Meknes 11201, Morocco\u003Cbr>Email: [a.waga@edu.umi.ac.ma](a.waga@edu.umi.ac.ma) |  |  |\n\n1. INTRODUCTION  \nNowadays, mobile robots have become an obligatory part of human daily life. since the latter needs help in certain tasks, for example, the navigation of autonomous cars [1]-[3] or autonomous underwater vehicles [4] . In general, autonomous navigation has to be realized by combining two main tasks, global path planning [5]-[9] and local motion control [10] . The generated trajectories are often optimal by optimizing some criteria such as path length or collision risk with obstacles [11] . Trying to adapt this task to unknown environments or unexpected actions is still a challenge since it requires an expert who will re-program the sequence of actions or movements that the robot must perform to generate an optimal trajectory. In robotics, in order to overcome this challenge, some researchers try to exploit the strength of artificial intelligence with autonomous navigation. In this sense, learning from demonstration has attracted more interest in the last ten years since it tries to imitate the behavior of an expert [12] and also involves interactions between the mobile robot and the unknown environment and many other constraints, which poses some difficult requirements for artificial intelligence. This aspect is inspired by how humans learn by being guided by experts from infancy to adulthood, so the principle is to teach new tasks to mobile robots without doing heavy programming [13] .  \nThe fundamental idea of learning from demonstration is that the robot learns through several demonstrations by the expert. The demonstrations collected by the mobile robot are sequences of state-action pairs that are recorded during expert navigation. Unfortunat","cbCaivva3kzzurua","https://ap.wps.com/l/cbCaivva3kzzurua","pdf",6104686,1,11,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"Why is learning from demonstration used for mobile robot navigation?\",\"answer\":\"Learning from demonstration lets the robot acquire navigation skills by imitating expert behavior, reducing the need for heavy programming across subtasks like planning, localization, and obstacle avoidance.\"},{\"question\":\"What role does the expert A* play in the proposed approach?\",\"answer\":\"The expert A* generates optimal trajectories, and the robot learns a navigation policy by using demonstrations derived from this expert guidance.\"},{\"question\":\"How is the method evaluated?\",\"answer\":\"The approach is tested with simulated environments of different complexity and obstacle distributions to measure flexibility and efficiency, and results indicate reliability and effectiveness.\"}]","Efficient autonomous navigation for mobile 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is learning from demonstration used for mobile robot navigation?","Question",{"text":75,"@type":76},"Learning from demonstration lets the robot acquire navigation skills by imitating expert behavior, reducing the need for heavy programming across subtasks like planning, localization, and obstacle avoidance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does the expert A* play in the proposed approach?",{"text":80,"@type":76},"The expert A* generates optimal trajectories, and the robot learns a navigation policy by using demonstrations derived from this expert guidance.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the method evaluated?",{"text":84,"@type":76},"The approach is tested with simulated environments of different complexity and obstacle distributions to measure flexibility and efficiency, and results indicate reliability and 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