ArticleProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems2019
Optimizing Motion-Planning Problem Setup via Bounded Evaluation with Application to Following Surgical Trajectories.
Article in Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A Metric for Finding Robust Start Positions for Medical Steerable Needle Automation.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2022Article
- Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm.Frontiers in robotics and AI · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
A motion-planning problem's setup can drastically affect the quality of solutions returned by the planner. In this work we consider optimizing these setups, with a focus on doing so in a computationally-efficient fashion. Our approach interleaves optimization with motion planning, which allows us to consider the actual motions required of the robot. Similar prior work has treated the planner as a black box: our key insight is that opening this box in a simple-yet-effective manner enables a more efficient approach, by allowing us to bound the work done by the planner to optimizer-relevant computations. Finally, we apply our approach to a surgically-relevant motion-planning task, where our experiments validate our approach by more-efficiently optimizing the fixed insertion pose of a surgical robot.
Identifiers
What OpenQuestion holds
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.