[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123202-en":3,"doc-seo-123202-105":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},123202,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Improving Path Planning Methods Using Machine Learning - May 2024","Path planning in robotics enables autonomous systems to navigate efficiently within complex environments. This bachelor project enhances sampling-based methods, focusing on Rapidly-exploring Random Trees (RRT) and RRT*, by incorporating machine learning to exploit learned environmental information. The work assesses performance and adaptability through comparative experiments against existing approaches. Results and analysis evaluate whether the proposed learning-augmented algorithms can surpass traditional techniques and support further advances in motion planning.","Bachelor Project  \nF3  \nCzech Technical University in Prague  \nFaculty of Electrical Engineering Department of Cybernetics  \nImproving Path Planning Methods Using Machine Learning  \nArtyom Tsoy  \nSupervisor: Ing. Vojtěch Vonásek, Ph.D.  \nField of study: Cybernetics and Robotics  \nMay 2024  \nii  \nAcknowledgements  \nI am sincerely thankful to my supervisor Ing. Vojtěch Vonásek, Ph.D., for his assistance and invaluable feedback. His guidance has been instrumental in navigating the challenges and refining the outcomes of this research endeavor. Additionally, I am truly grateful for the opportunity to study at CTU, where I have gained knowledge and skills that have greatly contributed to the successful completion of this thesis. I would also like to express my gratitude to my family and friends for their support.  \nDeclaration  \nI declare that the presented work was developed independently and that I have listed all sources of information used within it in accordance with the methodical instructions for observing the ethical principles in the preparation of university theses. Furthermore, I utilized artificial intelligence for grammar correction.  \nPrague, May , 2024  \nSignature  \nAbstract  \nPath planning in robotics plays a critical role in enabling autonomous systems to navigate in complex environments efficiently. This project focuses on enhancing traditional sampling-based path planning methods, such as Rapidly-exploring Random Trees (RRT) and RRT*, by integrating machine learning techniques. The objective is to improve the efficiency and adaptability of path planning algorithms through the utilization of learned information about the environment.  \nTo validate the efficacy of the proposed approach, comparisons were conducted against existing methods. Through experimentation and analysis, the performance and adaptability of the developed algorithms were assessed, highlighting their potential to outperform traditional techniques and contribute to the field of path planning.  \nKeywords: Path planning optimization, Sampling-based methods enhancement, RRT and RRT* algorithms, Environmental adaptation,  \nLearning-based planning, Machine learning  \nSupervisor: Ing. Vojtěch Vonásek, Ph.D.  \nAbstrakt  \nPlánování cest v robotice hraje kritickou roli při umožňování autonomním systémům efektivně navigovat složitými prostředími. Tento projekt se zaměřujena zlepšení tradičních metod plánování cest založených na vzorkování, jako jsou Rapidly-exploring Random Trees (RRT) a RRT*, prostřednictvím integrování technik strojového učení . Cílem je zlepšit efektivitu a přizpůsobivost algoritmů plánování cest využitím informací o prostředí získaných ze strojového učení .  \nPro ověření účinnosti navrženého přístupu byly provedeny srovnání s existujícími metodami. Skrz experimentování a analýzu byly zhodnoceny výkonnost a přizpůsobivost vyvinutých algoritmů, které zdůraznily jejich potenciál překonat tradiční techniky a přispět k oblasti plánování cest.  \nKlíčová slova: Optimalizace plánování cest, Zlepšení metod založených navzorkování, Algoritmy RRT a RRT*, Adaptace na prostředí, Plánování založené na učení, Strojové učení  \nPřeklad názvu: Využití strojovéhoučení v úloze plánování pohybu  \nContents  \n1 Introduction 3  \n1.1 Machine Learning .............. 4  \n1.2 Thesis structure ............... 6  \n2 Task formulation 7  \n2.1 Path planning problem ......... 7  \n2.2 Sampling-based path planning ... 8  \n2.3 Research Objectives ............ 8  \n2.4 Components of the Path Planning Problem ........................ 9  \n2.5 The Challenge ................ 10  \n3 Related works 11  \n3.1 Rapidly-exploring Random Tree (RRT) ......................... 11  \n3.2 Rapidly-exploring Random Tree star (RRT*) .................... 13  \n3.3 Probabilistic RoadMaps (PRM) 17  \n3.4 Informed RRT* .............. 18  \n3.5 RRT sharp (RRT\\# ) ........... 19  \n3.6 RRTX static ................. 20  \n3.7 Motion Planning Networks (MPNet) ....................... 21  \n3.8 Summarize .......","cbCaisjqq6dGVQ1l","https://ap.wps.com/l/cbCaisjqq6dGVQ1l","pdf",22861660,1,57,"English","en",105,"# Introduction\n## Machine Learning\n## Thesis structure\n# Task formulation\n## Path planning problem\n## Sampling-based path planning\n## Research Objectives\n## Components of the Path Planning Problem\n## The Challenge\n# Related works\n## Rapidly-exploring Random Tree (RRT)\n## Rapidly-exploring Random Tree star (RRT*)\n## Probabilistic RoadMaps (PRM)\n## Informed RRT*\n## RRT sharp (RRT#)\n## RRTX static\n## Motion Planning Networks (MPNet)\n## Summarize\n# Improving RRT* algorithm using machine learning method\n## Approach\n## Learning the Configuration Space\n## Proposed Solution\n## Integration to the RRT* algorithm\n## Extension of the machine learning method to 3D and 6D configuration spaces\n# Results and discussion\n## 2D Configuration Space\n## 3D and 6D Configuration Spaces\n# Conclusion","[{\"question\":\"What is the main goal of this project?\",\"answer\":\"The project aims to improve sampling-based path planning methods by integrating machine learning, with emphasis on RRT and RRT* performance and adaptability.\"},{\"question\":\"How does machine learning contribute to the path planning approach?\",\"answer\":\"Machine learning is used to learn information about the environment and the configuration space, then integrate it into the RRT* planning process to make the algorithm more efficient and adaptive.\"},{\"question\":\"How is the proposed method evaluated?\",\"answer\":\"The method is validated through comparisons with existing approaches using experiments and analysis that assess performance, cost convergence, runtime, and results across different configuration spaces.\"}]","Improving Path Planning Methods Using Machine Learning - 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