ArticleIEEE robotics and automation letters2022
RRT*-based Path Planning for Continuum Arms.
Article in IEEE robotics and automation letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Hybrid Offline-Online Configuration Planning Approach for Continuum Robots Based on Real-Time Shape Estimation.Sensors (Basel, Switzerland) · 2026Article
- A Novel Method of Path Planning for an Intelligent Agent Based on an Improved RRT* Called KDB-RRT.Sensors (Basel, Switzerland) · 2025Article
- Topology-Informed Quasi-Static Motion Planning for Continuum Robots with Contacts.IEEE robotics and automation letters · 2025Article
- CP-QRRT*: A Path Planning Algorithm for Hyper-Redundant Manipulators Considering Joint Angle Constraints.Sensors (Basel, Switzerland) · 2025Article
- Quasi-static Path Planning for Continuum Robots By Sampling on Implicit Manifold.IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation · 2024Article
- Cooperative Dynamic Motion Planning for Dual Manipulator Arms Based on RRT*Smart-AD Algorithm.Sensors (Basel, Switzerland) · 2023Article
- The Path Planning and Location Method of Inspection Robot in a Large Storage Tank Bottom.Computational intelligence and neuroscience · 2023Article
- RRT*-based Path Planning for Continuum Arms.IEEE robotics and automation letters · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Continuum arms are bio-inspired devices that exhibit continuous, smooth bending and generate motion through structural deformation. Rapidly-exploring random trees (RRT) is a traditional approach for performing efficient path planning. RRT approaches are usually based on exploring the configuration space (C-Space) of the robot to find a desirable work space (W-Space) path. Due to the complex kinematics and the highly non-linear mapping between the C-Space and W-Space of continuum arms, a high-quality path in the C-Space (e.g., a linear path) may not correspond to a desirable path/movement in the W-Space. Consequently, the C-Space RRT approaches that are based on C-Space cost functions do not lead to reliable and effective path planning when applied to continuum arms. In this paper, we propose a RRT* path planning approach for continuum arms that is based on exploring the W-Space of the robot as opposed to its C-Space. We show the successful applications of the proposed W-Space RRT* path planner in performing path planning with obstacle avoidance and in performing trajectory tracking. In all the aforementioned tasks, the quality of the paths generated by the proposed planner is superior to that of previous approaches and to its counterpart C-Space based RRT* approach, while the paths are generated in substantially less time.
Indexed as
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.