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

Kit-Hang Lee, Denny K.C. Fu, Martin C.W. Leong, Marco Chow, Hing-Choi Fu, Kaspar Althoefer, Kam Yim Sze, Chung-Kwong Yeung, Ka-Wai Kwok
2017 Soft Robotics  
11 Bio-inspired robotic structures composed of soft actuation units have attracted increasing research 12 interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external 13 environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical 14 application. However, previous model-based control approaches often require simplified geometric 15 assumptions on the soft manipulator, but which could be very inaccurate
more » ... the presence of unmodeled 16 external interaction forces. In this study, we propose a generic control framework based on nonparametric, 17 online, as well as local training, in order to learn the inverse model directly, without prior knowledge of the 18 robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with 19 control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced 20 element formulation of finite element analysis (FEA) is employed to initialize the control policy, hence 21 eliminating the need for random exploration in the robot's workspace. The proposed control framework 22 enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external 23 disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic 24 navigation in complex and changing environments. 25 26
doi:10.1089/soro.2016.0065 pmid:29251567 pmcid:PMC5734182 fatcat:7oqh6houvzdaxmmncu57bhsg4i