Cleanup bibtex - #61
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Pull request overview
Updates the site’s publication metadata by cleaning up BibTeX entries/keys and aligning project pages to the new citation keys, including adding the missing preview asset for a renamed entry.
Changes:
- Reformats and renames multiple entries in
_bibliography/papers.bib(keys, fields, ordering) and adds/updates metadata (e.g., month, address, publisher). - Updates project pages to cite the updated BibTeX keys.
- Adds the corresponding AVIF preview image and updates VS Code BibTeX formatting/spellcheck settings.
Reviewed changes
Copilot reviewed 4 out of 10 changed files in this pull request and generated 1 comment.
Show a summary per file
| File | Description |
|---|---|
assets/img/publication_preview/shu23twostage.avif |
Adds preview image referenced by the updated shu2023twostage BibTeX entry. |
_projects/5_project.md |
Updates citation keys for exoskeleton-related publications to match the cleaned BibTeX keys. |
_projects/1_project.md |
Updates citation keys for DLO manipulation publications to match the cleaned BibTeX keys. |
_bibliography/papers.bib |
Primary BibTeX cleanup: key renames, field normalization, and metadata refresh. |
.vscode/settings.json |
Updates editor spellcheck dictionary and LaTeX Workshop BibTeX formatting/sorting preferences. |
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| publisher = {IEEE}, | ||
| pages = {4534--4540}, | ||
| doi = {10.1109/ICRA57147.2024.10610098}, | ||
| abstract = {Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object,or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme.}, |
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The abstract has a typo: missing space after the comma in “object,or”. Please change it to “object, or” (and consider scanning the abstract for similar punctuation spacing issues).
Suggested change
| abstract = {Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object,or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme.}, | |
| abstract = {Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object, or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme.}, |
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bypass rules as there's no content change |
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