ArticleSoft robotics2017
Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation.
Article in Soft robotics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Stretchable, Multiplexed, and Bimodal Sensing Electronic Armor for Colonoscopic Continuum Robot Enhanced by Triboelectric Artificial Synapse.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Continuum Robots and Magnetic Soft Robots: From Models to Interdisciplinary Challenges for Medical Applications.Micromachines · 2024Review
- Deep-Learning-Assisted Underwater 3D Tactile Tensegrity.Research (Washington, D.C.) · 2023Article
- Collaborative Continuum Robots for Remote Engineering Operations.Biomimetics (Basel, Switzerland) · 2022Article
- Characterization of continuum robot arms under reinforcement learning and derived improvements.Frontiers in robotics and AI · 2022Article
- Millimeter-Scale Soft Continuum Robots for Large-Angle and High-Precision Manipulation by Hybrid Actuation.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2021Article
- A Survey for Machine Learning-Based Control of Continuum Robots.Frontiers in robotics and AI · 2021Review
- Review of machine learning methods in soft robotics.PloS one · 2021Review
- Conditioned haptic perception for 3D localization of nodules in soft tissue palpation with a variable stiffness probe.PloS one · 2020Article
- Soft Robotics in Minimally Invasive Surgery.Soft robotics · 2019Article
- An Opposite-Bending-and-Extension Soft Robotic Manipulator for Delicate Grasping in Shallow Water.Frontiers in robotics and AI · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments.
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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.