Self-calibration of a biologically-inspired cable-driven robotic arm

Identification of errors in the geometric model parameters of a robotic arm is critical for path planning and motion control. This paper presents the self-calibration of a novel biologically-inspired cable-driven robotic arm. A self-calibration model is formulated based on the differential change in the cable end-point distances. A computationally efficient algorithm using iterative least-squares is employed to identify the errors in the geometric model parameters. It does not require any external measurement devices because it utilizes the cable length data obtained from the redundant actuation scheme of the cable-driven arm. Both computer simulations and experimental studies were carried out to verify the robustness and effectiveness of the proposed self-calibration algorithm. From the experimental studies, errors in the geometric model parameters were precisely identified after a minimum of 35 pose measurements.