NIMS-PL: a cable-driven robot with self-calibration capabilities

  • Authors:

  • Per Henrik Borgstrom;Brett L. Jordan;Bengt J. Borgstrom;Michael J. Stealey;Gaurav S. Sukhatme;Maxim A. Batalin;William J. Kaiser

  • Affiliations:

  • Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA;Department of Mechanical Engineering, University of California at Los Angeles, Los Angeles, CA;Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA;Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA and Renaissance Computing Institute, Chapel Hill, NC;Department of Computer Science, University of Southern California, Los Angeles, CA;Center for Embedded Networked Sensing, University of California at Los Angeles, Los Angeles, CA;Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA

  • Venue:
  • IEEE Transactions on Robotics
  • Year:
  • 2009

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Abstract

We present the Networked InfoMechanical System for Planar Translation, which is a novel two-degree-of-freedom (2-DOF) cable-driven robot with self-calibration and online drift-correction capabilities. This system is intended for actuated sensing applications in aquatic environments. The actuation redundancy resulting from in-plane translation driven by four cables results in an infinite set of tension distributions, thus requiring real-time computation of optimal tension distributions. To this end, we have implemented a highly efficient, iterative linear programming solver, which requires a very small number of iterations to converge to the optimal value. In addition, two novel self-calibration methods have been developed that leverage the robot's actuation redundancy. The first uses an incremental displacement, or jitter method, whereas the second uses variations in cable tensions to determine end-effector location. We also propose a novel least-squares drift-detection algorithm, which enables the robot to detect long-term drift. Combined with self-calibration capabilities, this drift-monitoring algorithm enables long-term autonomous operation. To verify the performance of our algorithms, we have performed extensive experiments in simulation and on a real system.