Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance
Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, amo...
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/11/7/722 |
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| author | Fatemeh Noroozi Morteza Daneshmand Paolo Fiorini |
| author_facet | Fatemeh Noroozi Morteza Daneshmand Paolo Fiorini |
| author_sort | Fatemeh Noroozi |
| collection | DOAJ |
| description | Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies. |
| format | Article |
| id | doaj-art-c5dda16b407942719f8aff1ce151be8e |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-c5dda16b407942719f8aff1ce151be8e2024-12-02T13:08:59ZengMDPI AGMachines2075-17022023-07-0111772210.3390/machines11070722Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical SignificanceFatemeh Noroozi0Morteza Daneshmand1Paolo Fiorini2Norwegian Institute of Bioeconomy Research (NIBIO), 1431 Ås, NorwayNorwegian Institute of Bioeconomy Research (NIBIO), 1431 Ås, NorwayDepartment of Engineering for Innovation Medicine, University of Verona, 37134 Verona, ItalyMotion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.https://www.mdpi.com/2075-1702/11/7/722motion planningmobile robotssocial robotsself-driving carshumanoid robots |
| spellingShingle | Fatemeh Noroozi Morteza Daneshmand Paolo Fiorini Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance Machines motion planning mobile robots social robots self-driving cars humanoid robots |
| title | Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance |
| title_full | Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance |
| title_fullStr | Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance |
| title_full_unstemmed | Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance |
| title_short | Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance |
| title_sort | conventional heuristic and learning based robot motion planning reviewing frameworks of current practical significance |
| topic | motion planning mobile robots social robots self-driving cars humanoid robots |
| url | https://www.mdpi.com/2075-1702/11/7/722 |
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