Informed circular fields: a global reactive obstacle avoidance framework for robotic manipulators
In this paper, we present a global reactive motion planning framework designed for robotic manipulators navigating in complex dynamic environments. Utilizing local minima-free circular fields, our methodology generates reactive control commands while also leveraging global environmental information...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Robotics and AI |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2024.1447351/full |
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| author | Marvin Becker Philipp Caspers Torsten Lilge Sami Haddadin Matthias A. Müller |
| author_facet | Marvin Becker Philipp Caspers Torsten Lilge Sami Haddadin Matthias A. Müller |
| author_sort | Marvin Becker |
| collection | DOAJ |
| description | In this paper, we present a global reactive motion planning framework designed for robotic manipulators navigating in complex dynamic environments. Utilizing local minima-free circular fields, our methodology generates reactive control commands while also leveraging global environmental information from arbitrary configuration space motion planners to identify promising trajectories around obstacles. Furthermore, we extend the virtual agents framework introduced in Becker et al. (2021) to incorporate this global information, simulating multiple robot trajectories with varying parameter sets to enhance avoidance strategies. Consequently, the proposed unified robotic motion planning framework seamlessly combines global trajectory planning with local reactive control and ensures comprehensive obstacle avoidance for the entire body of a robotic manipulator. The efficacy of the proposed approach is demonstrated through rigorous testing in over 4,000 simulation scenarios, where it consistently outperforms existing motion planners. Additionally, we validate our framework’s performance in real-world experiments using a collaborative Franka Emika robot with vision feedback. Our experiments illustrate the robot’s ability to promptly adapt its motion plan and effectively avoid unpredictable movements by humans within its workspace. Overall, our contributions offer a robust and versatile solution for global reactive motion planning in dynamic environments. |
| format | Article |
| id | doaj-art-483b8581641c4cf083bfdd671db5c4f3 |
| institution | Kabale University |
| issn | 2296-9144 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj-art-483b8581641c4cf083bfdd671db5c4f32025-01-03T05:10:20ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442025-01-011110.3389/frobt.2024.14473511447351Informed circular fields: a global reactive obstacle avoidance framework for robotic manipulatorsMarvin Becker0Philipp Caspers1Torsten Lilge2Sami Haddadin3Matthias A. Müller4Institute of Automatic Control, Leibniz University Hannover, Hannover, GermanyInstitute of Automatic Control, Leibniz University Hannover, Hannover, GermanyInstitute of Automatic Control, Leibniz University Hannover, Hannover, GermanyMunich Institute of Robotics and Machine Intelligence, Technische Universität München (TUM), Munich, GermanyInstitute of Automatic Control, Leibniz University Hannover, Hannover, GermanyIn this paper, we present a global reactive motion planning framework designed for robotic manipulators navigating in complex dynamic environments. Utilizing local minima-free circular fields, our methodology generates reactive control commands while also leveraging global environmental information from arbitrary configuration space motion planners to identify promising trajectories around obstacles. Furthermore, we extend the virtual agents framework introduced in Becker et al. (2021) to incorporate this global information, simulating multiple robot trajectories with varying parameter sets to enhance avoidance strategies. Consequently, the proposed unified robotic motion planning framework seamlessly combines global trajectory planning with local reactive control and ensures comprehensive obstacle avoidance for the entire body of a robotic manipulator. The efficacy of the proposed approach is demonstrated through rigorous testing in over 4,000 simulation scenarios, where it consistently outperforms existing motion planners. Additionally, we validate our framework’s performance in real-world experiments using a collaborative Franka Emika robot with vision feedback. Our experiments illustrate the robot’s ability to promptly adapt its motion plan and effectively avoid unpredictable movements by humans within its workspace. Overall, our contributions offer a robust and versatile solution for global reactive motion planning in dynamic environments.https://www.frontiersin.org/articles/10.3389/frobt.2024.1447351/fullautonomous robotic systemsguidance navigation and controlreal-time collision avoidancerobotic manipulation armmotion planning |
| spellingShingle | Marvin Becker Philipp Caspers Torsten Lilge Sami Haddadin Matthias A. Müller Informed circular fields: a global reactive obstacle avoidance framework for robotic manipulators Frontiers in Robotics and AI autonomous robotic systems guidance navigation and control real-time collision avoidance robotic manipulation arm motion planning |
| title | Informed circular fields: a global reactive obstacle avoidance framework for robotic manipulators |
| title_full | Informed circular fields: a global reactive obstacle avoidance framework for robotic manipulators |
| title_fullStr | Informed circular fields: a global reactive obstacle avoidance framework for robotic manipulators |
| title_full_unstemmed | Informed circular fields: a global reactive obstacle avoidance framework for robotic manipulators |
| title_short | Informed circular fields: a global reactive obstacle avoidance framework for robotic manipulators |
| title_sort | informed circular fields a global reactive obstacle avoidance framework for robotic manipulators |
| topic | autonomous robotic systems guidance navigation and control real-time collision avoidance robotic manipulation arm motion planning |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2024.1447351/full |
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