ArticleAutonomous robots2019
Asymptotically Optimal Kinematic Design of Robots using Motion Planning.
Article in Autonomous robots, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- On Surgical Planning of Percutaneous Nephrolithotomy with Patient-Specific CTRs.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- A simulation study to investigate the use of concentric tube robots for epilepsy surgery.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2022Article
- Optimizing Motion-Planning Problem Setup via Bounded Evaluation with Application to Following Surgical Trajectories.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2019Article
- Design Considerations for a Steerable Needle Robot to Maximize Reachable Lung Volume.IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and AutomationArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In highly constrained settings, e.g., a tentaclelike medical robot maneuvering through narrow cavities in the body for minimally invasive surgery, it may be difficult or impossible for a robot with a generic kinematic design to reach all desirable targets while avoiding obstacles. We introduce a design optimization method to compute kinematic design parameters that enable a single robot to reach as many desirable goal regions as possible while avoiding obstacles in an environment. Our method appropriately integrates sampling based motion planning in configuration space into stochastic optimization in design space so that, over time, our evaluation of a design's ability to reach goals increases in accuracy and our selected designs approach global optimality. We prove the asymptotic optimality of our method and demonstrate performance in simulation for (i) a serial manipulator and (ii) a concentric tube robot, a tentacle-like medical robot that can bend around anatomical obstacles to safely reach clinically- relevant goal regions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.