Real-Time Shape Estimation of Hyper-Redundant Flexible Manipulator Using Coiled Fiber Sensors

Abstract

The shape of flexible endoscopic surgical robot should be obtained to increase control accuracy and prevent unwanted tissue damage. To estimate the shape of flexible manipulator, space efficiency, cost-effectiveness, system complexity, and ease of calibration should be considered to integrate sensors into the manipulator. In this article, we propose a real-time method to estimate the shape of a hyper-redundant manipulator having embedded coiled fiber sensors. The main advantage of this method is guaranteeing shape recognition even when the manipulator is subjected to an external load. The fiber sensors are highly flexible, compact, and inexpensive, as well as they can functionally measure both compressive and tensile strain of hyper-redundant manipulator. The sensor design was optimized to achieve durability and sensitivity. The numbers of sensor and the placements were determined by the analysis of the kinematics and moment distribution of the manipulator. The accuracy of shape estimation was validated experimentally under both free-loading and loading conditions. The proposed method achieved real-time estimating capability with a mean maximum error of each joint position smaller than 3.54% in free-loading condition and 5.47% in loading condition.