SCARA Self Posture Recognition Using a Monocular Camera

Abstract

Robotic manipulators rely on feedback obtained from rotary encoders for control purposes. This article introduces a vision-based feedback system that can be used in an agricultural context, where the shapes and sizes of fruits are uncertain. We aim to mimic a human, using vision and touch as manipulator control feedback. This work explores the use of a fish-eye lens camera to track a SCARA manipulator with coloured markers on its joints for the position estimation with the goal to reduce costs and increase reliability. The Kalman Filter and the Particle Filter are compared and evaluated in terms of accuracy and tracking abilities of the marker's positions. The estimated image coordinates of the markers are converted to world coordinates using planar homography, as the SCARA manipulator has co-planar joints and the coloured markers share the same plane. Three laboratory experiments were conducted to evaluate the system's performance in joint angle estimation of a manipulator. The obtained results are promising, for future cost effective agricultural robotic arms developments. Besides, this work presents solutions and future directions to increase the joint position estimation accuracy.