4.7 Article

Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation

Journal

SOFT ROBOTICS
Volume 4, Issue 4, Pages 324-337

Publisher

MARY ANN LIEBERT, INC
DOI: 10.1089/soro.2016.0065

Keywords

endoscopic navigation; finite element analysis; inverse transition model; soft robot control

Categories

Funding

  1. Croucher Foundation
  2. Research Grants Council (RGC) of Hong Kong [27209151, 17227616]
  3. Innovation and Technology Fund (ITF) of Hong Kong [ITS/361/15FX]
  4. NISI (HK) Limited

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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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